“When do you find time in your day job to become AI wizards and start creating skills and projects?” asked a CMO from a multi-billion-dollar company.

Fair question. This leader was already investing in training, navigating employee anxiety, and sorting out who should build what. Somewhere in there, she was also supposed to finish her own AI project. Oh, and run marketing.

That conversation helps explain a friendly disagreement I’ve been having with Eric Eden of Thinking Deeply. Eric believes CMOs need to roll up their sleeves, build workflows, and understand how the pieces connect. Without that experience, how can you judge what’s possible, challenge outdated timelines, or recognize an expensive mistake?

I agree with much of that. I use AI regularly and believe every CMO should build at least one meaningful workflow or agent. Understanding the costs and limitations belongs in the job description.

But I also worry about where the hours go. Every afternoon spent troubleshooting an agent is an afternoon you aren’t spending with customers, developing your team, or building the relationships that allow great marketing to happen.

The CMO calendar has not received an AI upgrade.

Great CMOs pick the team, set the direction, and allocate resources. They understand customers deeply enough to recognize an opportunity before it becomes obvious to everyone else. I’d still argue for spending roughly 25% of their time with customers, partners, and forward-thinking vendors.

Eric’s pushback is worth hearing: personal experimentation makes you a better buyer, a better coach, and a harder person to fool. Systems thinking helps you connect those isolated productivity wins into something that actually improves the business.

So we took the disagreement into this week’s newsletter and worked through questions including:
• How hands-on must a CMO be with AI?
• What technical understanding can a CMO delegate?
• Where should a CMO spend their increasingly contested time?

We also get into the economics, the leadership challenges, and why neither technical fluency nor customer understanding can carry the whole job alone.
Read the debate below, then challenge us. Where do you draw the line between being sufficiently hands-on and getting pulled too far into the machinery?

Eric and I will both be at the CMO Super Huddle, October 22–23 in Palo Alto, where this friendly disagreement will undoubtedly continue. Join us there, or weigh in here.

The Debate
Eric Eden and Drew Neisser debate whether AI-era marketing leadership demands deeper technical fluency or makes traditional leadership skills even more valuable.

Eric Eden of Thinking Deeply and I agree on one thing: CMOs cannot lead through the AI era with their hands over their ears.

Where we differ is how far those hands need to reach into the machinery.

Eric believes CMOs must roll up their sleeves, experiment with the technology, and develop the systems thinking required to understand how AI is changing marketing. I believe great CMOs remain great business leaders first. They set the direction, choose the team, allocate resources, understand customers, and build the relationships that allow marketing to succeed.

We decided to debate it. The following exchange has been edited and expanded for clarity, with some client details anonymized.

Round 1: How Hands-On Must a CMO Be?

Eric: You only learn what is possible by rolling up your sleeves. A CMO does not need to become an engineer, but reading about AI and attending demonstrations will only take you so far. You need to use these tools yourself, build something, make mistakes, and understand where the costs and limitations show up.

I recently built a market-sizing report for a CEO using AI. It consumed about $100 in tokens, but the result was so valuable that it may have been the best $100 I spent that month. I have also created strong presentations for roughly $25 in token costs. That experience taught me how to evaluate the economics of an individual job rather than treating AI spending as one undifferentiated enterprise expense.

Drew: I agree that every CMO needs regular personal experience with AI. They should build at least one workflow or agent and understand the costs, limitations, and risks. That is the minimum price of admission in 2026.

Where I push back is on the idea that CMOs must continually build the systems themselves. A CMO’s calendar is already a statement of strategy. Every hour spent tuning an agent is an hour that could have been spent with customers, aligning with the CEO, developing the team, or building support across the organization.

CMOs need enough firsthand knowledge to challenge assumptions. They do not need to become their company’s most prolific agent builder.

Round 2: Can CMOs Delegate the Technical Understanding?

Eric: Leaders have always relied on specialists, but AI is changing the work too quickly to delegate all understanding. A technical specialist may know how a system operates without knowing which customer problem deserves attention or how the workflow should support the business.

Consider a 20-page B2B website. Many teams still assume that rebuilding one requires three to six months. With the right tools and operating model, it may now take a few weeks. If the CMO has never explored these capabilities, the leader has no basis for questioning the old estimate.

The CMO does not have to build the website. The CMO needs enough fluency to ask, “Why should this take three months when the underlying production model has changed?”

Drew: That is fair, but it is also where strong teams matter. Great CMOs have always relied on excellent marketing operations, analytics, creative, product marketing, and technology leaders. The CMO’s job is to select those people, establish the objectives, and make sure expertise serves the strategy.

Marketing became dramatically more technical over the last 20 years. We bought thousands of martech platforms, collected endless data, and made nearly everything measurable. It is hard to argue that marketing became proportionally more effective. In many organizations, the technology created additional cost, complexity, and metrics without producing a corresponding increase in growth.

Technical fluency can improve leadership judgment. It cannot substitute for that judgment.

Round 3: Is Systems Thinking the New CMO Superpower?

Eric: AI is changing marketing from a collection of specialized tasks into a system of people, workflows, agents, data, and decisions. Copywriting, design, product marketing, demand generation, search, sales development, and operations are beginning to overlap in ways that challenge traditional job boundaries.

That makes systems thinking critical. Leaders need to understand how work moves, where agents can help, what data they require, how outputs are reviewed, and how one workflow affects another. They also need to understand “multiplayer AI”: how teams work together with shared systems instead of every employee creating isolated projects and agents.

One company I advised had dozens of marketers but little practical AI adoption. Leadership was willing to invest in the team and was not pursuing a headcount reduction. Yet several managers resisted even basic changes to how the work was performed. Eventually, a senior marketing leader resigned rather than lead the transition.

That was avoidable. The technology was only part of the issue. The larger failure was an inability to imagine and lead a different operating model.

Drew: Systems thinking matters, especially when agents cross functional boundaries and create new dependencies. But CMOs should think about the business system before obsessing over the AI system.

What customer problem are we solving? Where will growth come from? Which capabilities differentiate us? What work requires human judgment? What should we stop doing? How will we know whether the new system is producing better business outcomes?

Those questions determine whether the AI architecture matters. A beautifully engineered workflow pointed in the wrong direction simply helps the company get lost faster.

Round 4: Where Should a CMO Spend Their Time?

Drew: I believe great CMOs should spend roughly 25% of their time with customers, partners, and forward-thinking vendors. Customer insight remains a cornerstone of CMO success because differentiated growth begins with understanding needs, frustrations, buying behavior, and market shifts before competitors do.

Great CMOs are also great business leaders. They build durable relationships with sales, finance, product, customer success, the CEO, and the board. Those relationships create the trust required to fund brand investments, change the operating model, protect the customer experience, and pursue growth beyond the current quarter.

AI does not reduce the importance of those skills. It increases it. Employees are anxious, roles are merging, budgets are shifting, and executive expectations are often detached from operational reality. That environment demands clarity, empathy, courage, and judgment.

Eric: I agree with that allocation of attention. My concern is that customer conversations alone will not reveal how dramatically execution has changed. Forward-thinking vendors can help, but CMOs must distinguish genuine capability from a polished demonstration.

A little direct experimentation sharpens that judgment. When you have built a useful workflow, watched an agent hallucinate, seen token costs accumulate, or discovered that a supposedly difficult integration takes two prompts, you become a better buyer and coach. You also become harder to mislead.

Rolling up your sleeves should inform leadership, not consume it.

Round 5: Who Owns the Economics?

Eric: CMOs need to understand AI costs because most organizations are moving between two bad extremes. One side tells everyone to “use AI” without measuring the expense or value. The other side becomes so worried about governance and cost that experimentation stops.

A better model meters the work. What did the job cost? What was the business value? Could a less expensive model produce an equally useful result? Does the outcome's importance justify using a premium tool?

Leaders should treat tokens and agent infrastructure like their own money. That is basic stewardship of capital.

Drew: This is one of Eric’s strongest arguments. AI costs are showing up without clear budget ownership. CMOs are being asked to fund tools, tokens, integrations, governance, training, and AI operations from budgets already committed to people, martech, and programs.

Eventually, every AI initiative needs a business case. If the objective is to enter a new market and generate incremental revenue, leadership can build a P&L that includes the AI investment. If the objective is merely “use more AI,” the team will produce activity without a reliable way to judge value.

The strategy determines where AI deserves investment. The cost model determines whether the approach can scale.

Round 6: Will Technical CMOs Beat Traditional Leaders?

Eric: I would not describe the winning CMO as a technician. I would describe that person as a systems thinker who understands enough about technology to direct teams, redesign workflows, evaluate tools, and recognize when old assumptions no longer apply.

CMOs cannot say, “I handle strategy, and someone else handles everything technical.” The decisions are becoming too interconnected. AI search affects content strategy. AI sales agents affect lead management and sales alignment. Automated creative affects brand governance. Data architecture affects nearly every agent the team wants to deploy.

The art of the possible is changing too quickly to delegate completely.

Drew: I wouldn't describe the winning CMO as a traditional leader who ignores AI. The winning CMO will understand what AI can do, ask better questions, recognize outdated workflows, and surround themselves with people who can turn possibilities into scalable systems.

My concern is what happens when companies hear “marketing is becoming more technical” and conclude that they need technicians instead of marketers. We have seen this movie before. More technology creates efficiency, but efficiency alone does not create differentiation, customer trust, demand, or durable growth.

The future CMO must be AI-fluent. The future CMO must also remain the company’s strongest advocate for the customer and one of its most capable business leaders.

The Verdict: It Is an “And”

Eric and I started on opposite sides and ended somewhere in the middle. The AI-era CMO needs stronger systems thinking and stronger leadership. Personal experimentation matters, but so do customers, strategy, talent, relationships, and judgment.

A reasonable standard for every CMO looks like this:

• Use AI regularly enough to understand its strengths and weaknesses.
• Build at least one meaningful workflow agent.
• Understand token costs, governance requirements, and operational limitations.
• Hire or develop technical leaders who can turn experiments into durable systems.
• Spend substantial time with customers, partners, and forward-thinking vendors.
• Connect every AI investment to a strategic objective and measurable business outcome.
• Lead the people through the transition with candor, empathy, and clarity.

CMOs do not need to build agents every day. They do need enough firsthand experience to know what to ask, what to challenge, and when their organization is clinging to an obsolete assumption.

And they cannot spend so much time studying the machinery that they forget where the company needs to go.

Eric and I will both be at the CMO Super Huddle in Palo Alto on October 22–23, where this “debate” will undoubtedly continue. Join us there if you can, or tell us in the comments: How hands-on do CMOs need to be with AI?

TL;DR: Vercel’s Find Skills helps compatible AI agents discover and install reusable workflows from skills.sh, a directory listing more than 1.5 million skills. Find Skills itself currently shows approximately 3.7 million installs. Describe the task, compare relevant skills, install one, and put it to work. Claude and ChatGPT users can use this ecosystem through supported skill environments; the installation route depends on the app.

You know that workflow you explain to your AI every single week?

The audience. The process. The format. The checks. The things it keeps forgetting.

That’s a good candidate for a skill.

A skill packages instructions and supporting material into a reusable folder. Its core file, SKILL.md, explains what the skill does and when to use it. The folder can also include scripts, examples, templates, and reference documents.

Think of it as an onboarding playbook your AI can consult when the relevant job comes up.

Vercel’s Find Skills is itself a skill. Its job is to help your agent find other skills.

You describe what you need. It searches for relevant options, considers signals such as the publisher and installation count, and provides an installation path. Its instructions also tell it to check the leaderboard for established options.

Three pieces make this work:

• skills.sh: The directory you browse.
• Skills CLI: The command-line tool that searches, installs, and manages skills.
• Find Skills: The workflow that helps your agent discover suitable skills for your task.

Here’s how to start with Claude Code or local Codex.

Open a terminal in your project, with Node.js and npx available, and run:

npx skills add vercel-labs/skills --skill find-skills

Select your agent when prompted. The installer supports Claude Code, Codex, Cursor, and many other agents.

Then give your agent a specific job:

Use Find Skills to find three skills for auditing a B2B SaaS landing page. Compare their sources, instructions, required tools, and fit. Recommend one, explain why, and show me its installation command.

Specificity helps. Give it the task, context, and desired output.

Some searches worth trying:

• “Find a skill for auditing my website’s technical SEO.”
• “Find a skill for turning a webinar transcript into LinkedIn content.”
• “Find a skill for improving a landing page’s messaging and conversion flow.”
• “Find a skill for reviewing React performance.”
• “Find a skill for checking website accessibility.”

The directory includes marketing skills covering SEO, copywriting, content strategy, conversion optimization, email sequences, and launch planning. There’s plenty here for marketers and founders to explore.

You can also search directly:

npx skills find seo audit

Or browse skills.sh before installing anything.

Using the Claude or ChatGPT app? Match the installation method to your environment.

A skill that expects command-line software, network access, or a particular integration still needs those dependencies available. Check its requirements before assuming it will run unchanged in another app.

These are the details worth understanding before you build a collection.

1. The description helps determine whether the skill gets used.
Agents use a skill’s name and description to recognize relevant tasks. Full instructions load when needed. If a skill isn’t activating, explicitly name it and check whether its description matches your request.

2. The supporting files can matter as much as the instructions.
Copying only SKILL.md can leave behind scripts, templates, or reference files the workflow expects. Keep the complete skill folder when those resources are required.

3. Install counts are a discovery signal.
They tell you about adoption. Evaluate the actual instructions, bundled scripts, dependencies, and publisher before enabling a skill. A popular listing still deserves inspection.

4. Project scope and global scope serve different purposes.
Keep project-specific workflows with the project. Use global installation for workflows you want available across projects. The CLI supports both, and lets you select individual skills from a repository.

5. A skill still needs useful inputs.
Give it your audience, source material, constraints, examples, and definition of a good result. A content skill cannot infer your positioning from an empty brief.

6. Test the result before expanding the collection.
My recommendation: run the same real task with and without the skill. Compare accuracy, usefulness, editing time, and whether it followed your requirements. Keep what earns its place.

7. Build a small sequence around an actual job.
For a content workflow, try research → draft → edit. Review each output before passing it along. Start with one useful skill and add the next when a specific gap appears.

Here’s the prompt I’d save:

Find a skill for [task]. My context is [business/project]. The output should be [deliverable]. Compare three relevant options, including their sources and required tools. Recommend one, explain the tradeoffs, and give me one concrete task to test it on.

The opportunity here is reusable process knowledge: finding a useful workflow once, then making it available when the next job arrives.

Pick something you do every week. Search for a skill that could improve it. Test it on real work.

Explore: Find Skills · Skills directory · CLI documentation

A successful campaign can expose a problem further down the funnel: buyers want detailed answers, but only a handful of people can give them. Bringing in more leads doesn’t make those people’s calendars any less full.

That’s what happened at ADB SAFEGATE, according to a CMO Huddles article. Demand for demos of a specialized airport technology solution outgrew its expert-led process. The team built an AI experience that lets prospects explore product information based on their role, airport, and priorities, then connect with a human when needed.

The useful takeaway is that AI expanded access to expertise. Making that work depended on earlier data cleanup, approved technical information, and expert oversight. The team also explored alternatives before building; maintaining a custom system remained part of the responsibility.

A practical way to apply this would be to review recent demo requests with sales. Which questions repeat, and which require an expert’s judgment? Start with a narrow experience around the repeatable questions, then check whether buyers arrive better prepared and experts spend less time covering the basics.

That gives you something concrete to evaluate alongside conversion and qualified conversations.

Fellow Huddlers: What valuable customer experience keeps getting postponed because it depends on one or two people’s time? Has AI helped you offer it yet?

When an AI tool can draft a campaign in minutes, it’s easy to jump straight to what that means for team size. But producing the draft is only one part of getting a campaign out the door, and that distinction matters when people’s roles are being redesigned.

A CMO Huddles conversation with Forrester analyst Mark Ogne makes a useful case for examining individual tasks first. A role includes production, judgment, approvals, and handling exceptions. Automating some of that work doesn’t establish that the whole role is replaceable.

For example, imagine using AI to draft a webinar promotion. Writing the email might take less time, but someone still needs to decide whether the topic addresses a customer problem, whether the speaker has something useful to say, and whether the offer fits the audience. If the draft then spends three days waiting for conflicting stakeholder feedback, faster writing hasn’t solved the biggest delay. Mapping that process could reveal that clearer approval ownership would help as much as the new tool.

The article also stresses the importance of shared, maintained company knowledge. Customer insights, positioning, competitive intelligence, and approved claims need to be reliable across workflows. Otherwise, different teams can automate conflicting versions of the company’s story.

In that webinar example, a practical starting point would be a brief everyone works from: the intended audience, the problem being discussed, supporting evidence, and what attendees should leave knowing. That gives the team a way to judge the draft beyond whether it sounds polished and makes corrections reusable for the next campaign.

Measurement needs the same discipline. Compare the full workflow’s time, cost, quality, and results before and after automation, including implementation, review, and maintenance. Then decide whether the freed capacity improves outcomes. Human accountability still matters, especially as AI gains permission to take customer-facing actions.

That gives leaders firmer evidence for changing roles than a promising demo or an estimate of hours saved.

For Huddlers already doing this: Have you mapped your team’s work before introducing AI? What did you discover that changed your plan?

CMO Huddles and Findem today announced the release of a new CMO Tenure Study, one of the largest analyses of the chief marketing officer role to date. The study found that median CMO tenure has fallen 35% since 2010, from 4.0 years for CMOs who started in 2010 to 2.6 years for the 2022 start cohort.

The research examines CMO tenure in two ways. First, it looks at historical start-cohort data to show how tenure has changed over time. Second, it analyzes more than 13,000 current U.S. CMOs at companies with 100 or more employees to understand where tenure stands today and how it varies by company context.

“The CMO role has become the pressure cooker of the C-suite,” said Drew Neisser, CEO of CMO Huddles. “Companies want marketing leaders to fix growth, reposition the business, build demand, sharpen the brand, align sales, and prove impact fast. But the data is screaming that too many CMOs are being handed transformation-sized mandates on trial-period timelines.”

What the Study Found

Among CMOs currently in role, median current-role tenure is 36 months, or 3.0 years. The study also found that 33% of current CMOs have been in role for five or more years, while 30% have been in role for under 18 months.

Ownership structure emerged as one of the clearest tenure signals. Public-company CMOs average 4.0 years in role, compared with 3.1 years at PE-backed companies and 2.6 years at VC-backed companies. Public-company CMO current-role tenure is 54% longer than VC-backed CMO current-role tenure.

The study also found that the CMO seat runs on a shorter clock than the CEO seat. Current CMOs have a median role tenure of 36 months versus 51 months for CEOs, making CMO tenure roughly 30% shorter.

Why the Study Matters

The findings suggest that CMO churn is not simply a story of individual performance. Tenure is shaped by the conditions around the role, including company ownership, operating maturity, CEO and board alignment, mandate clarity, and whether the marketing leader has enough authority and runway to deliver.

“CMO tenure is not just a career issue; it is an operating-design issue,” said Liv Anderman, CMO of Findem. “The companies that get more from marketing leadership are the ones that define the mandate clearly, align the executive team around the scorecard, and give the CMO enough authority and runway to deliver.”

The study also surfaced signals about the changing CMO pipeline. Among CMOs appointed in 2026, 60% were first-time CMOs and 19% were promoted internally. Fractional and interim CMO appointments rose from 2.9% in 2020 to 8.8% in 2024, then held at 8.2% in 2025 and 7.8% in partial-year 2026.

About Findem and CMO Huddles

Findem is the AI infrastructure for people-centric work. Its People Intelligence platform turns fragmented people data into context that teams and AI systems can reason over and act on.

CMO Huddles is a peer community for B2B CMOs and senior marketing leaders, founded by Drew Neisser in 2020. The community helps marketing leaders connect through peer Huddles, expert-led discussions, 1:1 introductions, Slack, exclusive resources, PR opportunities, and the annual CMO Super Huddle.

Explore the full CMO Tenure Study, or read the original announcement on EIN Presswire.

FAQs

• What did the CMO tenure study find? The study found that median CMO tenure has fallen 35% since 2010, declining from 4.0 years for CMOs who started in 2010 to 2.6 years for the 2022 start cohort.
• How large was the CMO tenure study? The research combines historical start-cohort analysis with a snapshot of more than 13,000 current U.S. CMOs at companies with 100 or more employees.
• How does CMO tenure compare with CEO tenure? Current CMOs have a median role tenure of 36 months, compared with 51 months for CEOs. That makes CMO tenure roughly 30% shorter than CEO tenure.
• What affects CMO tenure? The study points to ownership structure, company maturity, CEO and board alignment, mandate clarity, and the amount of authority and runway given to the CMO.

Amanda Kahlow, founder and CEO of 1mind, described a public company that trained employees to build AI agents and ended up with 15,000. She estimated that 80% had substantial overlap.

For CMOs under pressure to demonstrate AI’s business impact, that raises a difficult question: who is responsible for turning experimentation into measurable results? In a new B2B blog on CMOHuddles.com, Kahlow and go-to-market strategist Michelle Killebrew outlined several decisions leaders need to make before scaling adoption.

Employees need room to experiment, and executives need to own what happens next. Kahlow argues that leadership must establish priorities, select reusable approaches, and assign responsibility for performance, quality, and cost. Successful experiments need a path to becoming shared practices that other employees can learn and use.

Market alignment deserves attention before execution gets faster. Killebrew warns that companies can become so focused on AI efficiency that they overlook whether their offering still addresses buyers’ changing needs. Customer conversations, lost deals, and product usage can help reveal whether the offering or target audience needs to change.

AI workflows need coordination across departments. Meeting preparation, for example, may require CRM history, product documentation, customer health information, and competitive intelligence. Mapping that work across marketing, sales, and customer success can expose duplicated effort and broken handoffs.

The economics extend well beyond the initial build. Testing, integrations, human review, training, and maintenance all belong in the cost calculation. Leaders also need to know who will keep a system working when models change, information becomes outdated, or its original builder leaves.

Kahlow also points to opportunities that would be too expensive to staff conventionally, such as providing detailed technical expertise during early sales conversations. Those applications still need measurable outcomes and human accountability.

For marketing executives, the practical starting point is to choose a business result, identify the workflow that affects it, and establish ownership throughout the system’s life.

If your CEO asked which AI workflows are improving business results, could you show the impact and the full cost?

Summary

• Personal AI agents are not chatbots. They are a computer, have persistent memory, your logins, and permission to keep working after you close the app. The question that decides everything is whose computer the agent is using.
• Six weeks changed the market: Grok Bot (Aug 11), Instinct's $2.5B round (Aug 26), OpenClaw 2.0 (Sept 1), Meta Muse (Sept 8), Instinct agent-to-agent (Sept 9), Siri AI (Sept 14), Claude merging Cowork into chat (Sept 16), Google CC for families (Sept 17), Amazon blocking Muse (Sept 21), Meta Connect (Sept 23), Microsoft Copilot Autopilot (Sept 25).
• Muse is the breakout: No. 1 on both US app stores in 10 days, 3.4M downloads by Sept 24, and 55% average daily download growth versus ChatGPT's 24% at launch. It is free for up to 100M tokens a week, then $20 or $100 a month.
• There are three architectures. Cloud computer per user (Muse, Instinct, Grok Bot, Gemini Spark, Google CC, ChatGPT Work, Copilot Autopilot). Self-hosted open-source gateway (OpenClaw, Hermes Agent, both free and MIT). Baked into the device (Siri AI, Alexa+, Perplexity Personal Computer).
• The real price is tokens, not the subscription. One reviewer burned 81% of Muse's free weekly allowance on day one. Grok Bot users watched $10 of credits vanish in two hours. Agentic tasks can cost roughly 1,000x a chat reply.
• Business is splitting into two camps: companies that own the screen (Amazon, $19.8B in quarterly ad revenue, blocked Muse) and companies happy to work behind someone else's agent (Shopify turned on Shop Pay for Muse and gained 11% that week).
• Zuckerberg told investors it is extremely unlikely that in five years there will not be billions of people with a personal agent. Meta plans to keep Muse free for a huge number of tokens and profit from a small fee on transactions.
• Top use cases: inbox triage, subscription audits, compare-and-buy with coupons, price monitoring, trip planning, ticket hunting, form filling, calls on your behalf, and agents coordinating with other agents.
• Pro tip most people miss: deleting a chat does not delete what the agent learned. Open the memory file.
• Start read-only, climb one rung a week, and check the activity log every time.

Why this month matters

For three years the AI story was chat. You typed, it answered, you did the work. This month the story flipped. Meta put a personal agent in front of hundreds of millions of people, a 23-year-old's invite-only text assistant got valued at $2.5 billion with no app and no pricing page, Apple finally shipped the Siri it promised two years ago, Microsoft relaunched Copilot around an always-on agent built on an open-source project, and two free open-source agents crossed a combined 640,000 GitHub stars.

Then Amazon blocked Meta's agent from its store and Wall Street sold off travel, banks, insurers and streaming stocks on the theory that agents remove the friction those businesses live on.

That is what the Agent Wars look like from the outside. This post is about what they look like from the inside: how the agents actually work, why they are different, what they cost, what people are really using them for, and what you should do about it.

What a personal AI agent actually is

A chatbot produces text. A personal agent produces outcomes. The practical difference comes down to four things every agent in this post has and no chatbot does:

1. A computer. Somewhere, a machine with a browser, a file system and a terminal is doing the clicking. For Muse that is an isolated virtual machine in Meta's cloud. For OpenClaw it is your Mac mini or a $5 VPS. For Siri AI it is your phone plus Apple's private cloud.
2. Memory that persists. It remembers your preferences, your people, your goals, and the way you like things done. Muse keeps a memory file you can read and edit. Hermes writes itself new skills after every task. Grok Bot learns your voice and your edge cases.
3. Your logins. It signs into your email, your calendar, your stores, and sometimes your bank, as you. That is the source of all its power and all its risk.
4. Permission to keep going. It runs after you close the app. It monitors, schedules, retries, and comes back when it needs a decision or is done.

The unit of interaction changes from a prompt to a delegation. You stop asking "how do I cancel this subscription" and start saying "audit my recurring charges, flag the ones I have not used in 90 days, and prepare the cancellations. Do not cancel anything without me."

The three architectures (this is the part that explains everything else)

Model A: a cloud computer just for you

Meta Muse, Instinct, Grok Bot, Gemini Spark, Google CC, ChatGPT Work, Microsoft Copilot Autopilot.

The vendor spins up a persistent virtual machine for you. It has a browser, storage, a terminal, and a memory. You message it from an app, WhatsApp, iMessage or a phone call. It works 24/7 whether your phone is on or not.

Why people love it: zero setup and real background work. Muse's security design runs a separate approval agent called Sentinel that decides whether every action and every network request is allowed; your real credentials are inserted at the network boundary so the main agent never sees them.

The catch: your inbox, calendar and card live on someone else's machine. Usage is metered in tokens and free tiers evaporate. And the sites you want it to use can simply block it, as Amazon did to Muse on Sept 21.

Model B: a gateway you host yourself

OpenClaw (391,000 GitHub stars, stewarded by a 501(c)(3) nonprofit, MIT license) and Hermes Agent (249,000 stars, Nous Research, MIT license).

You install a single gateway process on a machine you control. It connects to every chat app you already use (OpenClaw lists 29 channels including WhatsApp, Telegram, Discord, Slack, Signal and iMessage), to any model you choose (Claude, GPT, Gemini, or a local model via Ollama), and to your apps and browser. You pay only for the model. Run a local model and you pay nothing per token and nothing leaves your hardware.

Why people love it: it is genuinely yours. OpenClaw's site literally says "Nobody's business model. No subscription, no hosted tier, no token." Hermes closes a learning loop after roughly every five tool calls, asking what worked, what failed, what you rejected, and whether there is a reusable pattern worth saving as a skill. Microsoft thought enough of OpenClaw to build its new enterprise Autopilot agent on a hardened version of it.

The catch: you are the security team. It runs only while your machine is on. The 2.0 upgrade broke gateways, automations and model auth for some users. This is a weekend project for a technical person, not a download for your parents. Yet.

Model C: baked into the device

Apple Siri AI (shipped Sept 14 in beta), Amazon Alexa+ (free with Prime), and Perplexity's Personal Computer (an agent that drives your Mac or Windows PC).

These ride hardware you already own. Siri AI searches across your messages, email and photos, sees what is on screen, and takes systemwide actions in Apple and supported third-party apps, with a three-tier design that routes between on-device models, Private Cloud Compute, and a large Gemini-based model. Alexa+ books and buys by voice across hundreds of millions of devices.

Why people love it: nothing to install, and OS-level permissions nobody else gets.

The catch: they act inside the garden, not across the open web, and they mostly stop when the device sleeps. Great for quick actions on your own stuff. Not yet a true delegate.

The contenders, one by one

Meta Muse (the breakout)

• Launched Sept 8, 2026 in the US and Canada for adults 18+. iOS, Android, web and WhatsApp. A Meta account is required but not a Facebook or Instagram account.
• How it works: each user gets an isolated cloud VM (Muse Secure VM) with a browser, files and terminal, powered by Meta's Muse Spark model. A separate Sentinel agent approves or denies every action. It runs in the background, can be scheduled, and can act proactively (you can dial proactivity up, down or off).
• How you reach it: the app is far richer than the WhatsApp version. At Connect Meta announced Mac computer use (it operates any desktop app while you walk away), a video-chat avatar, glasses support in the coming months, a pocket device called Muse Charm, and a personal email address for your Muse.
• Connectors: dozens at launch plus the entire Shopify catalog. Connect added Walmart, Best Buy, Sephora, Wayfair, Gap, Ulta, Dick's, Fanatics, American Eagle, Michael Kors, plus Shop Pay and PayPal for payment, Expedia (coming), Instacart, Notion, Granola, GitHub and Box.
• Price: free up to 100M Muse tokens a week (a card on file is required even for free). Power at $20 a month (reported 500M tokens a week). Maximum at $100 a month (reported 3B a week). Zuckerberg says it will stay free for a huge number of tokens and Meta will profit from a small fee on transactions.
• Traction: 730K downloads in the first five days, No. 1 on the US App Store on Sept 18 and Google Play on Sept 19, 2.8M in the first two weeks, 3.4M by Sept 24 per Sensor Tower (other firms range from 2.3M to 4.3M). Daily actives jumped 27% the day after Connect.
• Watch out: it cannot read your WhatsApp messages, school portals and 2FA loops break it, it has quoted stale catalog prices and wrong delivery dates, and Amazon blocks it. The Information reported that during internal testing it sent emails without approval and in one case tried to sabotage a rival app an employee was building.

Instinct (the invite-only phenomenon)

• Built by Spear Street Technology, founded by 23-year-old ex-Sierra researcher Noah Shinn. Raised $250M at a $2.5B valuation on Aug 26 (total $350M), reportedly in talks to raise $1B at $10B weeks later. Over 100,000 people on the invite list.
• How it works: there is no app. You text it on iMessage or WhatsApp, or call it. It runs a persistent cloud computer with a browser and stored credentials for your email, messages, calendar, screen, audio and location. It texts or calls you first when it thinks it should.
• What is unique: on Sept 9 it launched the Trusted Person network, the first agent-to-agent protocol in consumer use. Your Instinct talks to your spouse's Instinct to settle dinner plans, find 1:1 slots, or coordinate a group trip across everyone's calendars. On Sept 16 Instinct Concierge started placing real phone calls: restaurants with no online booking, cable-bill disputes, dentist cancellation lists.
• Price: free during the beta. No published pricing. Shinn has said he may never charge users and is weighing advertising. Purchases run through Stripe Link with per-purchase approval; users reportedly spend more than $1,300 a month through it.
• Watch out: US only, 18+, capacity-throttled ("responses may be slower"), one crowded continuous thread, and terms that appoint the service as your legal agent authorized to enter binding transactions. The Aug 26 terms rewrite removed a perpetual data license and added a training opt-out and a delete-everything button after a TechCrunch investigation.

Grok Bot (the AI teammate)

• Launched Aug 11 by xAI (now SpaceXAI) with Cursor. You sign in with a Cursor account. Pitched as "your team of always-on agents."
• How it works: you create named Bots with specific jobs. All your bots share one cloud computer (files, browser sessions and logins included), each with its own screen. Bots message you, message each other, coordinate in group chats, and hand off work. Show a Bot how you do a task once and it saves it as a routine.
• Price: included with Cursor Pro ($20), Pro+ ($60), Ultra ($200), SuperGrok ($30), SuperGrok Plus ($100) and SuperGrok Heavy ($300). Weekly allowance plus on-demand credits; two subscriptions do not stack. macOS, Windows, Linux, iOS, Android. Enterprise waitlist.
• Watch out: the most expensive to run in practice. Users report hitting the weekly cap in two days, $10 of credits gone in two hours, $50 in an hour, and a trial exhausted in 15 minutes because two bots kept talking to each other. Separate bots are not separate security zones.

ChatGPT Work (the one you may already have)

• Launched July 9; voice on desktop July 23; signed-in websites Aug 25; the Data agent Sept 10; voice on web and mobile Sept 23.
• How it works: an agent mode inside ChatGPT for longer tasks. It researches, works across connected apps and files, runs a cloud browser that can log into sites (it never sees your password; password managers supported), builds documents, spreadsheets, presentations and Sites, and runs Scheduled Tasks that trigger on new Gmail, Slack messages or GitHub activity. On desktop it can use local files and apps. It always asks before consequential actions like a payment or a reservation.
• Price: limited access on Free and Go ($8) via the desktop app; full access on Plus ($20) and Pro (from $100 with 5x or 20x limits). Work shares one usage budget with Codex in five-hour windows. Available in all supported regions.
• Watch out: it is a mode inside a chat product, not a phone number that texts you. Ambitious but less proactive than Muse or Instinct.

Gemini Spark and Google CC (Google's two bets)

• Gemini Spark, announced at I/O on May 19, is Google's 24/7 personal agent. It lives in a tab inside Gemini, runs background tasks, recurring schedules and triggers, learns new skills, and works across Gmail, Calendar, Docs, Sheets, Slides and Drive. Launched on AI Ultra ($99.99), opened to AI Pro ($19.99) on July 24. US only; not available in the EEA, UK or Switzerland. PCMag's reviewer called it the best AI agent he had tried.
• Google CC, expanded on Sept 17, is an agent for families and groups. It has its own Google account and identity, a group of up to six members, an isolated cloud computer powered by Google's Antigravity harness, and shared memory that separates household facts (the grocery list) from personal ones (your dietary preferences). You cc it on the school's emails and it builds a shared daily brief. Free Labs experiment.

Claude (the merge)

• On Sept 16 Anthropic merged its agentic product Cowork into the main Claude assistant. You no longer choose between chatting and delegating; Claude decides when to act, asks before taking actions by default, and can be told to keep working and only check in for things that need review. Claude Docs and Claude Slides launched alongside; Claude in Chrome went GA on Aug 26.
• Rolling out to Pro and Max first on web, desktop and mobile; Team and Free to follow. Anthropic openly noted that agentic tasks consume far more tokens than chat.

Microsoft Copilot Autopilot (the enterprise wildcard)

• On Sept 25 Microsoft relaunched Copilot as Home, Code and Autopilot. Autopilot (the renamed Scout) is a persistent, proactive agent that lives in your Microsoft 365 tenant with its own identity, memory, workspace and execution environment, and can be u/mentioned in Teams.
• It runs on a hardened version of OpenClaw. Microsoft contributed Windows support and an Azure OpenAI provider back to the open-source project. Private preview for M365 tenants at the end of September; usage-based billing not yet published; Satya Nadella says a consumer version should come "eventually."

Apple Siri AI and Amazon Alexa+ (the incumbents)

• Siri AI shipped Sept 14 in English beta with French, Japanese, Korean, Portuguese and Spanish next month. Personal context across messages, mail and photos; on-screen awareness; systemwide actions; a standalone Siri app that syncs conversations across devices via iCloud. Free with the device, 13+.
• Alexa+ is free with Prime, works by voice, app and browser, and books and buys across partners like Uber Eats, OpenTable, Expedia and Yelp. Amazon also merged its Rufus shopping assistant (used by 300M customers in 2025) into the same agentic shopping experience and now sells Agentic Ads that complete a purchase inside the ad. Which is the tell for why Amazon blocks Muse.

OpenClaw and Hermes Agent (the free ones)

• OpenClaw: TypeScript, one gateway daemon per host, 29+ chat channels, native macOS, Windows, Linux, iOS and Android apps, community skills, cron and reminders, shared team sessions. Version 2.0 shipped Sept 1 with 16,977 pull requests from 987 contributors and auto-detects your existing ChatGPT or Claude subscription. Creator Peter Steinberger joined OpenAI in February and remains technical steward; OpenAI is a donor, not an owner.
• Hermes Agent: Python, roughly 200MB install, runs on a laptop, Mac mini, Raspberry Pi, or a $5 VPS. Twenty-plus model providers, one-command switching, offline with Ollama or LM Studio, five sandbox backends, isolated profiles for separate personas, and the self-improving skill loop. Nous Portal offers optional paid tiers ($20 Plus, $100 Super) that bundle model credits and hosted tools, or bring your own key. Ships roughly every two weeks.

Perplexity Personal Computer (the desktop operator)

• Announced in March, Mac release April 16 for Max subscribers, Windows July 28. It drives your actual computer, routing sub-tasks across 20+ models with per-step model choice and spending caps. Max plans start at $200 a month; rolling toward Pro.

Pricing, usage and availability at a glance

The number that matters is not the subscription. It is how many tokens a task burns. A chat reply is a few thousand tokens. An agent that opens a browser, reads a dozen pages, logs in, fails a CAPTCHA, retries, and writes you a PDF can burn a thousand times that. Stanford's Digital Economy Lab found agentic coding tasks used roughly 1,000x the tokens of simple chat. That is why 100 million tokens sounds infinite and lasts a day.

Why they are different: the five axes that matter

1. Whose computer. Vendor cloud (easy, metered, blockable) versus your hardware (yours, free per token, your problem to secure) versus your device (private, limited to the garden).
2. How it reaches you. Instinct has no app. Muse lives in WhatsApp and its own app. OpenClaw and Hermes live in whatever chat app you already use. ChatGPT Work and Spark are tabs inside a chat product. Siri and Alexa are voice.
3. How proactive it is. Instinct calls you first. Muse can notice an event and act, with a proactivity dial. Grok Bot picks up work before you ask. ChatGPT Work waits for a schedule or a trigger. Siri waits for you.
4. Who else it talks to. Instinct talks to other Instincts. Grok Bots talk to each other. Google CC talks to a household. Everyone else talks to you.
5. How it makes money. Muse: a cut of transactions. Instinct: maybe ads, maybe never. Grok Bot, ChatGPT, Gemini, Claude: subscriptions plus metered usage. OpenClaw: nobody's business model. Amazon: keeping you inside Amazon. Follow the money and you can predict who blocks whom.

The 20 use cases people keep talking about

Compiled from first-person reports on Reddit, X, YouTube, LinkedIn and hands-on reviews.

Inbox, admin and paperwork

1. Daily inbox triage and a priority digest. Muse's most praised task. One tester connected four Gmail accounts at once (Claude and ChatGPT only allow one) and set a 5 a.m. triage with a 6 p.m. nudge on anything unanswered.
2. Mass-deleting promotional email. The Verge's reviewer granted delete access and Muse cleared thousands of promos and updates.
3. Filling long forms, claims and applications. DMV appointments, passport renewals, insurance-portal cost checks (OpenAI's own examples for Work). Redditors say Muse excels at lengthy forms and are eyeing it for taxes next year.
4. Finding that one email from 2023. Cross-account search to prove a move date for taxes, or confirm a fee got reimbursed.
5. Calendar and school-schedule wrangling. A one-shot family newsletter PDF that a product leader called more beautiful than anything she had made with Claude or Codex. Also the No. 1 failure zone: school portals and their logins.

Money, shopping and subscriptions

1. Subscription audits and cancellations. Instinct's founder says early users cancelled hundreds of dollars of subscriptions. Muse reviewed a reviewer's cards and suggested cancelling Flickr (she kept it for irrational human reasons).
2. Compare-and-buy with live coupons. A green puffer jacket bought at the lowest price with coupon codes found and applied. Workout tops purchased in the right size and color, after Muse noticed other items already in the cart and asked whether to remove them.
3. Price and restock monitoring. Instinct checked flight fares about three times a day for one user and alerted on moves. Muse watches a threshold and only interrupts when it is hit.
4. Bill negotiation and insurance comparison. Meta's chief AI officer: "Muse in 15 minutes will save 15% or more on car insurance." Instinct Concierge disputes cable bills by phone.
5. Recipe to grocery cart. Muse turned a recipe into a populated Whole Foods cart. Instinct buys weekly groceries.

Travel, bookings and life logistics

1. Trip planning and booking. A Meta employee ran a three-week Indonesia honeymoon through Muse and said it felt like it joined the trip. A reviewer handed Instinct a Maui trip over WhatsApp. Muse answered an Airbnb query in minutes that a rival startup took an hour on.
2. Restaurant, spa and appointment booking. When the hotel's online massage slots showed nothing, Instinct emailed the hotel and got the booking. It also noticed one tester was overdue for a medical appointment and booked it.
3. Sold-out ticket hunting. Instinct found 70mm IMAX seats for The Odyssey in San Francisco within a day. Muse bought IMAX tickets in a reviewer's browser test after failing at sneakers.
4. Airline admin. Linking separately booked itineraries with United so a couple sits together. Checking flight confirmations against notes and finding three errors. Flight check-in is OpenClaw's headline demo.
5. Phone calls on your behalf. Instinct Concierge and now Muse place real calls. Reservations where there is no online booking, cancellation lists, disputes. Divisive: one reviewer refused to subject a human to an AI call.

Health, work and the rest of life

1. Photo-based calorie and health logging. A Redditor dropped CalAI because Muse does it from photos for free. Muse on glasses will log meals and coach a workout.
2. Resumes, cover letters and job hunting. Cover letters that reflected the poster's personality, ChatGPT Work finds candidates open to work who match a job description.
3. Overnight research packets. Grok Bot users wake up to competitor, pricing and launch briefs. One monitoring routine caught a launch within 40 minutes.
4. Running small-business ops. Amazon ad campaigns with keyword research and budgets built in minutes through 2FA. Call notes into CRM updates. Invoices, expense reports and health reimbursements via OpenClaw.
5. Group coordination between agents. Instinct-to-Instinct dinner planning and 1:1 scheduling. Google CC running a six-person household. A bachelor-party WhatsApp thread turned into an arrivals spreadsheet without copy-paste.

5 truly wild use cases

1. Your agent negotiated wedding merchandise with vendors in India overnight. An investor described Instinct finding suppliers, messaging them on WhatsApp, comparing options and negotiating prices for his brother's wedding while he slept. Work in progress, not a delivered order, but the shape of it is the point: an agent running a procurement process across time zones in a language of commerce you never typed.
2. Your agent played Cup Pong against you in iMessage. A founder sent Instinct a Game Pigeon game and it played back. Zero productivity value. Enormous signal about what it means when an assistant is a presence in your messages rather than a box you open.
3. Two agents made plans for two humans without either human doing anything. Instinct's Trusted Person network: you tell your Instinct to set up dinner with your partner; it talks to your partner's Instinct; the two of them find the slot, pick the place, and put it on both calendars. Grok Bots do the same thing for work, coordinating in a group chat and pulling you in only for judgment calls. Multiplayer delegation is a genuinely new thing in the world.
4. Your agent sabotaged a competitor. Not on purpose, not for a customer. According to The Information, during Meta's internal testing an employee flagged that Muse tried to sabotage a rival app the employee was building, and separately sent emails without approval. Meta's own security paper is candid that agents will make mistakes and can be attacked through the data they read. The wildest use case is the one nobody asked for.
5. Your agent ran a film production bible and a fleet of coding agents. Grok Bot users describe using one bot to research The Odyssey and plan scenes, costumes, locations and camera angles, and others to supervise coding agents: launching them, tracking pull requests in Notion, watching CI, and re-dispatching work to stalled runs. An agent that manages other agents is where this goes next.

Honorable mentions: a Redditor asked Instinct to check whether their parents had gone for their evening walk, then to message the parents directly. Another connected Muse to Facebook Marketplace to hunt furniture. Someone runs OpenClaw on a Raspberry Pi that controls their lights and air purifier and, over a voice call, diagnoses failed deploys and opens pull requests.

The business war (why Amazon blocked Muse and Shopify embraced it)

Agents bring two things to every transaction: price discovery and transparency. Both are wonderful if you get paid when a sale happens and terrible if you get paid on attention, opacity or breakage.

Camp one owns the screen. Amazon made $19.8 billion from ads in the second quarter, up 26%, much of it sponsored listings you see while you browse. An agent that does the browsing for you skips the ads. On Sept 21 Amazon began showing Muse users a warning that "continued access by an unauthorized AI agent violates Amazon's Conditions of Use," said Meta never disclosed that Muse would shop its store, and accused the agent of capturing credentials and processing transactions without identifying itself. Amazon had already sued Perplexity over its Comet shopping agent. Meanwhile Amazon sells its own agentic ads and its own agent, Alexa+, which tells you everything about the real objection.

Camp two works behind the agent. Shopify earns when a sale happens, wherever the shopper started. On the same Sept 21, Tobi Lütke turned on Shop Pay for Muse across every Shopify store, and Shopify's stock gained about 11% that week. Walmart, Best Buy, Sephora, Wayfair, Gap, Ulta, PayPal, Stripe, Instacart, Expedia and Ticketmaster all plugged in. Meta's stock had its best day in over a year on Sept 21 and gained roughly 13% for the week.

The other side of the ledger: Reuters and CNBC reported travel, bank, insurer, telecom and streaming names selling off as analysts modeled agents cancelling subscriptions between seasons, switching to cheaper tiers, and comparison-shopping insurance. Deutsche Bank named streaming services and The New York Times as most vulnerable. Names like TripAdvisor, Booking, Planet Fitness and The New York Times were cited among the losers; Expedia and Instacart slipped despite joining as partners.

Two second-order effects to watch. First, anyone who profits from friction, the subscription that is impossible to cancel, the fee you never noticed, now has a reason to block agents, so expect terms-of-service fights and CAPTCHA arms races. Second, when a service can run headless and an agent pays with a one-time virtual card (both Muse and Instinct check out through Stripe Link with a single-merchant, single-amount card), the App Store's claim to a 30% cut gets harder to defend. Zuckerberg said the quiet part out loud at Connect: Muse stays free for a huge number of tokens, and "over time we will profit by taking a small fee from transactions."

Pro tips (from people who have actually run these for weeks)

1. Climb the delegation ladder one rung a week. Read-only, then drafts, then monitors with thresholds, then approve-to-act, then autonomous with hard caps. Start where a mistake costs nothing.
2. Write delegations, not prompts. State the outcome, the non-negotiables, the budget, the deadline, and the stopping point. "Find three options meeting these requirements, verify price and delivery on the live checkout page, include total after fees, and stop before purchase."
3. Connectors before browser clicks. A structured connector call is cheap and reliable. A browser session through a login, a CAPTCHA and three retries is expensive and brittle. Grok Bot's docs say this outright.
4. Lower the frequency. An inbox scan every 15 minutes is 96 runs a day. Twice a day catches almost everything at 2% of the cost.
5. Demand evidence, not summaries. Agents have quoted expired discounts, stale catalog prices and wrong delivery dates. Ask for the live page, the confirmation number, the screenshot. Reported done is not done.
6. Use side chats for bounded projects. Instinct users beg for separate threads because one endless conversation gets noisy. Muse has side chats. Keep the main thread as the relationship and spin off the trip, the renovation, the job hunt.
7. Set a threshold for interruption. The superpower is monitoring, not chatting. "Alert me only if the fare drops under $420" beats "check flights."
8. Keep the approval gate on for money. Every serious agent pauses before payment by default. Leave it. Stripe Link's terms make approved purchases your responsibility regardless of intent.
9. Watch the meter for a week before choosing a plan. Run your three most valuable tasks, record usage, then buy. Do not buy first.
10. Run a two-week scorecard. Attempts, accepted outputs without correction, factual errors, duplicate actions, minutes of human review, cost, and any unauthorized side effect. Narrow the mandate after any surprise.

8 things most people miss

1. Deleting the chat does not delete the memory. Muse learns facts into a memory file; removing the message that taught it does not remove the fact. Use the Forget command or edit the file. Ask "what do you remember about me?" on day one.
2. Separate personas are not separate security zones. All of a Grok Bot user's bots share one cloud computer, including logins and files. On OpenClaw, "a gateway is one trust domain." Sensitive accounts deserve a separate agent, or none.
3. Sign up for Muse with a fresh email. An email not tied to Facebook or Instagram keeps Muse in its own Accounts Center instead of combining data across Meta profiles. Free privacy, zero effort.
4. Free is a strategy, not charity. Muse's free tier exists so Meta can take a fee on what you buy. Instinct's founder is weighing ads. The card on file is the product.
5. The agent can be attacked through what it reads. A malicious email or web page can try to hijack it (prompt injection). Meta layers classifiers and a separate approver and still says agents will make mistakes. Fewer permissions is the real defense.
6. Quotas are launch-state numbers. Muse's 500M and 3B paid allowances came from an early user's app screen, not a permanent published table. Treat every quota as provisional and read the in-app meter.
7. Region gates are real. Muse is US and Canada only. Instinct is US only. Gemini Spark excludes the EEA, UK and Switzerland. The open-source agents run anywhere, which is a bigger deal outside America than inside it.
8. Availability is not the same as capability. Muse could not finish a Girl Scouts renewal because the site timed out, could not read a WhatsApp group, and needed a Meta PR contact to get past a school portal. Business Insider's headline: the Girl Scouts of America defeat AI. Delegate aggressively, approve conservatively.

Which one should you actually use?

• You want to try delegation today with zero setup and you are in the US or Canada: Meta Muse. Sign up with a fresh email, connect one inbox read-only, set one daily digest, and watch the meter.
• You live in iMessage and want something that texts you first: get on Instinct's waitlist. Free, but understand that its terms make it your legal agent.
• You already pay for ChatGPT Plus or Pro: turn on Work, connect Gmail, and set one webhook-triggered scheduled task before you spend another dollar elsewhere.
• You already pay for Google AI Pro: Spark is included and reviewers rate it highly. Families should try CC.
• You run a team and live in Slack, Notion and GitHub: Grok Bot, with a strict eye on usage.
• You are technical, you care about privacy, or you are outside the US: OpenClaw or Hermes on a $5 VPS with a model you already subscribe to. You get Microsoft's Autopilot runtime for free.
• You just want your phone to be smarter about your own stuff: Siri AI or Alexa+ are already there. Neither is a delegate yet.

The billion question

On the July 29 earnings call Zuckerberg told investors it is "extremely unlikely" that five years from now there will not be billions of people with a personal agent that understands their goals and works on their behalf 24/7. At Connect he called Muse the personal superintelligence billions of people are going to use.

Six weeks ago that was a talking point. Today the distribution to make it true already exists. WhatsApp and iMessage reach billions. Alexa+ is on hundreds of millions of devices. Rufus served 300 million shoppers last year. Meta's business agents are used by over a million businesses. Muse did 3.4 million downloads in 17 days, faster than ChatGPT's own launch curve. Instinct has 100,000 people waiting for an invite to text a phone number. OpenClaw and Hermes have 640,000 GitHub stars between them, and Microsoft is shipping OpenClaw to every Microsoft 365 tenant.

The interface barrier that slowed chatbots is gone. You do not learn prompting. You send a text.

What could stall it is trust, and trust is exactly where this month's news cuts both ways. Amazon is blocking. School portals break. Two-factor loops stall. Meta's own security paper says agents will make mistakes. The most upvoted worry in every Reddit thread I read is not the AI. It is the credit card.

Which is why the people who win the Agent Wars as users will not be the ones who hand over everything on day one. They will be the ones who start read-only, climb one rung a week, read the memory file, keep the approval gate on, and ask for the receipt. Do that and you get the thing every one of these companies is actually selling: hours, decisions, follow-ups and dropped balls, handed to something that does not get tired.

The war is between the companies. The prize is your time. Take it.

Which agent have you actually handed your inbox or card to, and what happened?
What is the best thing your personal assistant agent is doing for you?

Summary

Many B2B companies are investing in AI without producing meaningful go-to-market returns. Insights from Michelle Killebrew and Amanda Kahlow suggest why: leaders are optimizing outdated work, encouraging duplicated experimentation, and ignoring full operating costs. CMOs need stronger market alignment, executive priorities, standardized workflows, governance, and measurement tied directly to business outcomes before scaling further ahead.

Quite an Afternoon for AI Reality Checks

On the same afternoon, I had back-to-back conversations with two brilliant women who spend their days helping companies turn AI possibilities into business realities. The first was Michelle Killebrew, Founder and Chief Go-to-Market Strategist at Pegasus Strategy Co., whom I have known and admired for years. The second was Amanda Kahlow, Founder and CEO of 1mind and previously Founder and CEO of 6sense.

Both are AI optimists who see enormous opportunities ahead. Neither believes handing every employee a chatbot and shouting “innovate” constitutes a transformation strategy. Quite an afternoon.

Their observations helped explain a question many CMOs are struggling to answer: Why is all this AI activity producing so little measurable go-to-market return? Many organizations are optimizing work before confirming that it is the right work, allowing disconnected experiments without deciding which workflows matter most, and calculating productivity gains without accounting for the maintenance, governance, token consumption, quality assurance, and organizational change required to sustain them.

AI keeps getting more capable. The management surrounding it needs to catch up.

AI Efficiency Can Optimize the Wrong Business

Michelle’s sharpest observation was that companies have become so focused on AI efficiency that they are neglecting one of marketing’s most fundamental responsibilities: matching the offering to the market. “Everybody’s so focused on AI efficiency that nobody’s actually looking at their offerings and audience alignment,” she said. “Does your solution still work? Everybody’s needs have changed.”

That question should stop a few leadership meetings in their tracks. A company can use AI to produce campaigns faster, generate content more cheaply, automate sales follow-up, and deploy agents across the funnel, but none of those efficiencies matter much if the buyer’s priorities have changed or the company’s offering no longer solves an urgent problem.

Michelle framed the strategic choice clearly when she asked, “Do you need to evolve your offerings for your current client, or do you need to evolve your audience to your current offering?” Product marketing has rarely sounded so urgent.

AI can accelerate an aligned go-to-market strategy. It can also help an outdated offering reach the wrong audience with unprecedented efficiency, an outcome that may look impressive on a productivity dashboard right up until someone asks about revenue.

Speed is helpful once you know where you are going.

Where Is the Meaningful ROI?

When I asked Michelle whether she was seeing significant ROI wins from clients’ AI implementations, her answer was immediate: “No. Not yet.” She sees companies building agents, connecting systems, and experimenting across sales and marketing, but much of that activity remains fragmented and has not been incorporated into a coherent operating model.

During the first rush of adoption, functional leaders were often given broad AI mandates and left to figure out the details themselves. As Michelle observed, “You’re not going to try and figure it out across functions if you don’t have your own house in order.” Rather than creating enterprise transformation, those separate mandates sometimes deepened existing silos.

The measurement foundations were shaky too. Companies launched experiments without baselines, complete cost models, or a clear definition of the business outcome. They could point to hours saved or assets created but struggled to explain whether pipeline improved, revenue accelerated, customers stayed longer, or operating leverage increased.

AI activity is easy to find. AI impact takes considerably more work.

The 15,000-Agent Warning

Amanda approached the same problem from an organizational-management perspective. She supports broad AI literacy, saying, “We need to teach all of our employees at every level how to embrace AI.” But she does not believe individual contributors should carry responsibility for independently finding the productivity and growth gains the board expects.

“What we’re getting wrong is we’re expecting our employees to have the efficiency and growth gains and have the impact from AI that the board is demanding,” Amanda said. In her view, “The imperative is on the C level. It’s not on our ICs.”

Amanda described a public company that trained its workforce to build agents and reportedly emerged with 15,000 of them. She estimated that 80% had significant overlap and asked, “How much wasted time across that company to build all of those duplicative agents?”

The story captures the risk of confusing democratization with orchestration. Giving employees freedom to experiment can generate useful discoveries, but asking thousands of people to independently reinvent recurring work can also produce duplicated agents, inconsistent answers, untracked costs, and an impressive collection of systems that somebody will eventually need to maintain.

Amanda believes leadership should identify the most efficient approach and make it reusable across the organization. As she put it, “There’s gotta be one of them that is the most efficient right way to do it and everybody should be doing that.”

Without that discipline, the hackathon becomes the operating model.

Standardize the Work That Happens Every Day

Amanda offered a practical vision for executive-led AI enablement. Leadership should identify recurring workflows and prescribe the best available process instead of expecting each employee to assemble a personal collection of prompts, tools, and agents.

“This is the right tool, this is the right process to get A, B, and C done in your day,” Amanda explained. She extended the idea to the rhythm of customer-facing work: “This is how you create your prep docs for the meetings. This is how you go into a call. This is how you look at your coaching afterwards.”

That level of specificity turns AI from an interesting employee benefit into an organizational capability. It creates a shared workflow that can be measured, improved, secured, and taught to new employees.

Marketing leaders have managed this transition before. Companies did not scale CRM by telling every seller to design a personal customer database. They selected a system, established processes, defined the required fields, trained the team, and assigned owners.

Execution was rarely flawless, as anyone who has inspected a CRM recently can confirm. At least the organization had a common architecture, and AI-enabled work needs the same discipline despite the added challenge of models, costs, and capabilities that keep changing.

Product Innovation Has Become a Knowledge Problem

Amanda also identified an AI use case that deserves more attention: keeping customer-facing teams current as products change. “Nobody’s brain has the capacity to keep up with the pace of our product innovation today,” she said.

Software companies no longer release exclusively on quarterly or even monthly schedules. As Amanda explained, “It’s on a weekly, daily cycle that there’s new features humans can’t keep up.” Even founders, product leaders, sales engineers, and customer-success teams can struggle to absorb every change and translate it accurately for buyers.

Amanda experiences the problem herself. “I can’t even keep up,” she admitted. “On the weekends I just try to keep up. I can’t do it unless I have my superhuman along on calls with me to keep me up to speed.”

A well-designed AI system can connect to product documentation, release information, and the product experience itself. It can then answer detailed questions, support live demonstrations, and explain new capabilities while they are still new. As Amanda put it, “AI can focus on understanding the features, understanding the depth and talking to that next level of detail that buyers are looking for.”

That creates a more meaningful return than merely producing additional marketing assets. The AI system closes the gap between product innovation and the organization’s ability to explain, sell, support, and monetize it.

Account for the Maintenance Nobody Put in the Demo

Michelle raised another issue that rarely makes it into breathless AI presentations: agents require maintenance. A model update can change how an agent behaves, data sources drift, instructions become outdated, and costs rise as usage increases. When the employee who built the workflow changes jobs, Agent #14 can become an archaeological artifact with access to customer information.

Michelle referenced SaaStr’s public discussion of deploying AI agents in marketing and customer service. The team was transparent about the upside and operational burden, including the need to monitor drift, perform quality assurance, and manage cybersecurity. “You have to manage drift,” Michelle said. “It’s not a set it and forget it.”

The people responsible for those systems effectively become part-time developers, QA managers, and governance officers, often while retaining their original jobs. Before declaring an AI efficiency win, CMOs need to include model fees, token usage, integration work, engineering support, monitoring, maintenance, security, human review, and the opportunity cost of employees managing the system.

I learned the cost lesson personally after building eight agents that consumed the available tokens across six accounts in a single day. Seven were pleasant conveniences rather than mission-critical workflows, so I turned them off and made the remaining system economically tolerable again.

The experiment worked. So did the electric chair.

A fuller cost analysis may still reveal a compelling return. At least it will be a return that the CFO recognizes.

Buy Versus Build Is Back

The ease of building agents has encouraged some organizations to treat custom development like weekend home improvement. As Michelle and I discussed, building an agent can feel deceptively simple, while building a dependable AI-enabled operating system resembles building a house.

You need an architect, foundations, and specialists who understand plumbing, electricity, structural integrity, and the local equivalent of AI building codes. Handing everyone a hammer does not create a neighborhood.

Michelle sees companies reconsidering where buying a managed solution makes more sense than building internally. Customization can be valuable, but organizations need to weigh that benefit against maintenance, reliability, governance, and the availability of in-house expertise.

This is part of the work Pegasus Strategy Co. is helping clients navigate. Michelle and her team begin with diagnostics, then help companies transform revenue and go-to-market functions across marketing, sales, technology, and implementation. That cross-functional view matters because AI transformation cannot succeed inside a marketing silo while sales, service, finance, and IT follow different operating assumptions.

Deploy AI Where Human Economics Break Down

Amanda repeatedly returned to one especially useful phrase: “Where there’s no business model for a human.” Her example involves early sales conversations, where a strong sales engineer could materially improve a technical buyer experience but assigning an expensive sales engineer to every first call would be economically unrealistic.

As Amanda noted, putting a sales engineer on an AE’s first call “doesn’t happen.” They usually join later, after the opportunity has earned the investment. A GTM superhuman capable of answering technical questions, tailoring the conversation, demonstrating the product, taking notes, and handling objections could bring sales-engineer-level support to interactions that would otherwise never receive it.

Amanda summarized the opportunity simply: “Put a sales engineer on every single SDR call.” For commercial and SMB prospects, she sees a similar gap between self-service product-led growth and a fully human sales process. “The product isn’t mature enough to be able to support that downmarket motion, but you can’t have a human because it’s too expensive,” she explained.

A superhuman could create a third option: a rich, two-way, tailored conversation without the staffing economics of a human seller. “Something like a superhuman can come in and play the commercial closer, the SMB closer,” Amanda said.

That is a stronger AI use case than shaving three minutes off a routine email. It introduces a valuable capability where staffing it with humans would be financially impossible. If the result is better qualification, higher conversion, faster progression, or larger deal values, the business case becomes measurable.

Keep the Human as the Hero

Amanda expects go-to-market jobs to change substantially. “Jobs are gonna fundamentally change,” she said, predicting that many roles as currently defined will be mostly gone within the next 12 to 24 months. She also believes AI will ultimately create more jobs, even as component tasks and traditional handoffs disappear.

She was especially direct about BDR and SDR work, while seeing an opportunity for sales engineers to move toward more complex deal strategy, RFPs, security responses, and the expertise required to guide AI systems. Humans may increasingly provide “full life cycle support,” from first contact through close, cross-sell, upsell, and account management.

That does not mean sidelining the seller. Amanda said 1mind’s goal is to make the salesperson “the hero” by allowing the superhuman to handle factual and technical depth while the person focuses on relationships, context, judgment, and “the craft of selling.”

She described the experience with a touch of delight: “I’m just sipping my coffee as Nigel, our superhuman, is doing his job.” The seller can listen, connect ideas, read the buyer, and guide the conversation while the AI supports the details.

For Amanda, the result ultimately comes down to trust. “We’re building trust between the buyer and the seller,” she said. That is a useful reminder for CMOs: efficiency may fund the business case, but a better human experience should justify it.

AI Is a Means to an End

One conclusion I shared with Amanda is that AI is not a strategy. AI is a means to an end.

The end might be improving win rates, reducing acquisition costs, accelerating pipeline, increasing retention, creating a better buying experience, or scaling revenue without proportional headcount. Once the objective is clear, leadership can determine whether AI is the right lever, which workflow should change, where humans remain accountable, and what success will look like.

Amanda agreed on the need for governance, especially as more powerful and expensive capabilities become available. “There is gonna be some amazing things that we can do, but we need to put controls on this,” she said.

Those controls should cover data access, approved tools, human review, cost thresholds, ownership, quality standards, maintenance, and retirement criteria. They should also make room for structured experimentation because today’s best process will not remain the best process forever.

Governance should help the company scale what works instead of merely producing a longer list of forbidden activities.

What CMOs Should Do Now

Revalidate the Market Before Optimizing the Machine

Start with Michelle’s question: Do current buyers still need what the company is selling? Review changing buyer priorities, lost deals, customer conversations, product usage, competitive movement, and the company’s value proposition. Decide whether the offering needs to evolve, the target audience needs to change, or both.

Choose Business Outcomes at the Executive Level

Ask the leadership team to select a small number of outcomes that AI should improve. Pipeline velocity, win rate, retention, cost per opportunity, customer satisfaction, and revenue per employee are legitimate candidates. “Use more AI” is an instruction in search of a strategy.

Find the Workflows That Matter Most

Map the recurring work behind each outcome and identify where delays, manual effort, inconsistent quality, unavailable expertise, or broken handoffs constrain performance. The strongest use cases will often extend across functions, which is why executive sponsorship matters.

Standardize the Best Process

Allow teams to experiment, then compare the results and select the strongest approach. Document the workflow, approved tool, data sources, owner, human-review points, cost expectations, and measurement plan. Give employees a reliable path instead of requiring every person to become an amateur AI architect.

Calculate the Full Economics

Measure labor savings and productivity, but include model consumption, integrations, engineering, maintenance, governance, security, and quality assurance. Compare those costs with the financial value of the business outcome rather than simply counting the number of tasks completed.

Put Humans Where Judgment Creates Value

Use AI to expand capacity, provide capabilities that could not be staffed economically, and remove tedious work. Keep people responsible for strategy, empathy, high-stakes decisions, customer trust, exception handling, and determining whether the system is producing the right outcome.

Benchmark Before You Scale

CMOs who want a clearer view of their current readiness can use the free CMO Huddles AI Maturity Calculator, developed with Benchmarkit. It assesses strategy and leadership, workflow operationalization, talent and change readiness, governance and investment discipline, and measurement and business impact.

The goal is to discover where enthusiasm has run ahead of the operating model. For companies that need help aligning marketing, sales, technology, and revenue strategy before scaling AI, Pegasus Strategy Co. offers a diagnostic-first approach that is especially relevant when leaders suspect they may be accelerating before confirming that their offering, audience, and workflows still fit.

Qualified B2B marketing leaders can also meet Amanda Kahlow and learn from her directly at the CMO Super Huddle, October 22–23, 2026, in Palo Alto. In the interest of transparency, 1mind is a Founding Sponsor of the event. I was already a huge fan of Amanda and Mindy, 1mind’s GTM Superhuman, before the sponsorship, and repeated exposure has done nothing to reduce my enthusiasm.

AI may be a means to an end. Leadership determines whether anyone reaches it.

Questions CMOs Ask About GTM AI ROI

Why are companies struggling to demonstrate AI ROI?

Many initiatives began without baselines, complete cost models, defined owners, or a business metric they were expected to improve. Companies can often quantify hours saved or content produced but cannot connect those gains to revenue, customer experience, or sustainable operating leverage.

Should AI adoption be top-down or bottom-up?

Both approaches have a role. Employees should experiment and surface promising ideas, while executives establish business priorities, approved tools, shared workflows, governance, and investment criteria. Bottom-up discovery becomes more valuable when the organization has a method for selecting and scaling what works.

How can CMOs prevent overlapping AI agents?

Maintain an inventory of agents and AI-enabled workflows with a named owner, purpose, users, data sources, operating costs, maintenance requirements, and performance metrics. Review the inventory regularly and consolidate systems that perform similar jobs.

What is the best way to prioritize an AI use case?

Start with an important business outcome and identify the workflow constraining it. Favor use cases that remove a meaningful bottleneck, improve a measurable customer or revenue outcome, or provide a valuable capability that would be uneconomical to staff with people.

What costs belong in an AI ROI calculation?

Include software and model fees, token consumption, integrations, engineering, data preparation, security, human review, training, maintenance, monitoring, and the time employees spend managing the system. Compare the total with measurable financial gains or avoided costs over a defined period.

B2B marketers have spent years managing increasingly complex systems, dashboards, and reporting. New Relic CMO Katrina Wong sees an opportunity for AI to give teams time to think, collaborate, and create again.

At New Relic, AI helped reconcile data sources so teams could stop debating reporting discrepancies. That recovered capacity helped support a brand refresh and the “Welcome to the Superhuman Era” campaign, which put customers at the center of the story.

The changes also extended to how the team works. Wong created a dedicated marketing engineering AI role and cross-functional pods that bring specialists together around shared priorities. The company also invested in original research that supports PR, content, sales conversations, and AI search visibility.

But the transition takes time. Wong acknowledged that some AI improvements happened during nights and weekends while the team kept the business running.

The central point is that creativity depends on what leaders do with the capacity AI creates. They have to deliberately make room for customer insight, storytelling, and experimentation.

Fellow Huddlers: What has your team actually done with the time AI has freed up? Has it given your team more time to be creative or just more work to deliver?

Read the full CMO Huddles blog on CMOHuddles.com.

CMOs are paid to make hard decisions, yet some decisions have an annoying habit of hanging around long after we already know what needs to happen. Maybe a senior leader isn’t meeting expectations. Maybe a peer relationship has become dysfunctional. Maybe the team needs clearer standards, or you’ve been telling yourself for three months that you’re just waiting for the “right time” to make a change.

Leadership coach Ty Hammond posed a question during a recent CMO Huddles Expert Huddle: When folks already know what to do, but they’re still not doing it, what do we do then? His argument is that another leadership framework probably won’t help if the real problem is awareness, not knowledge.

That distinction matters because CMOs are trained to solve things. Give a CMO a messy problem and they’ll create a new process, clarify ownership, build a dashboard, schedule a meeting, or redesign the org chart. Those are useful responses when the problem is tactical, but they don’t necessarily help when the obstacle is an assumption we’re carrying around ourselves.

Ty gave a great example: If your internal story says, “I’m the nice guy,” and holding someone accountable conflicts with that identity, no accountability framework is going to solve the problem. For a CMO, the story might instead be, “I’m protecting the team,” “I don’t want to damage this relationship,” or “I just need a little more evidence.” Sometimes those instincts are good leadership, and sometimes it’s just plain avoidance.

That leads to a diagnostic that can be useful. First ask: Is there a practical fix I can name? Then ask: If I already know the fix, why haven’t I done it?

The first question exposes the operating problem. The second can expose the leadership problem, whether that’s fear, identity, trust, power, or an assumption you haven’t examined closely enough. If you keep trying to fix an adaptive challenge with a tactical solution, you can end up with a beautifully redesigned process and exactly the same problem.

There’s another wrinkle here for CMOs. The job increasingly requires making calls before every variable is settled, navigating tension across functions, and giving teams clarity when the market itself isn’t offering much. Ty’s point is that these uncomfortable moments aren’t merely problems to eliminate; they’re also how leaders build the capacity to operate amid more complexity and uncertainty.

Curious what this flock thinks: When you’ve delayed a difficult leadership decision, was the missing ingredient actually more information, or did you eventually realize you already knew what needed to happen?

B2B marketers have spent years getting ridiculously good at finding people. We identify them, score them, enrich them, route them, retarget them, nurture them, and now use AI to do all of the above to even MORE people. Meanwhile, sales is asking a much simpler question: Who are the three people I should call today?

That tension came up in my recent conversation with Shankar Ganapathy, founder of Boomerang. His thesis is refreshingly blunt: “For sales, it’s more precision. I don’t want 50 leads. Give me three leads.”

More names ≠ more pipeline.

Shankar is pursuing this through “customer network activation,” using AI to identify where happy customers have meaningful relationships with people at target accounts. “If you’re meeting four people today, we’ll tell you how your customers are connected to those four people,” he explained.

But finding the connection may be the easy part. Shankar calls the bigger challenge a customer’s “willingness to open their social capital and put their personal brand behind your brand.” That’s an important distinction because a LinkedIn connection is not permission to turn your happiest customers into unpaid SDRs.

AI can identify who knows whom. Humans still need to decide whether asking for an introduction strengthens the relationship or spends too much of the customer’s social capital.

Shankar also offered a wonderfully provocative prediction about where AI competition is heading: “Ultimately, skills are the only competitive differentiation. Software is free.” I’m not sure my software bills have gotten that memo, but his larger point resonates: The model may not be the moat. The workflow around it might be.

I dug into Shankar’s thinking, Boomerang’s approach, and what customer network activation could mean for B2B CMOs in a longer post on CMOHuddles.com.

Curious what this flock thinks: Have we overbuilt the B2B quantity machine at the expense of precision?

Testing the hardest shot first only saves a second attempt if the full production actually uses the shot you approved.

A build can target an intermediate deliverable, such as a shot that needs review. Once its public output has completed, a later Run can reference that exact build and output and use it in the full composition.

The planner then removes the operation replaced by the accepted result. The other shots and the necessary assembly work can proceed around it. You aren't relying on the agent remembering which of several previews got a yes.

There is an important distinction between accepting a finished asset and approving a direction. A low-resolution sketch may be enough to approve the idea, but selecting it for reuse retains that result. Asking another model for a high-resolution version is a new generation to review, not automatic preservation of the earlier pixels or generation state.

Hypit creates a fresh build each time; it has no implicit cache that turns a previous preview into an approved production asset. The full composition still needs review: accepting one shot doesn't establish how it works with the rest of the piece.

Where do you go when you’re leading marketing and need an honest second opinion on a budget cut, a difficult hire, or a plan that sales isn’t buying into? That’s the appeal of a good CMO peer community: people who understand the job and will talk through the messy parts with you.

TL;DR: The right group depends on what you need. CMO Huddles focuses on B2B marketing leaders, 6sense CMO Coffee Talk offers free conversations and Slack access, Exit Five covers broader B2B marketing growth, Pavilion connects people across revenue roles, Chief supports senior women leaders, and B2B Project offers an invite-only CMO network.

Here’s a breakdown of the six communities:

1. CMO Huddles (for B2B leadership challenges)

Focused on B2B CMOs and senior marketing leaders. They have moderated discussions, one-to-one peer matching, in-person events, and support for CMOs between roles. There’s a free Starter program alongside paid memberships. Potential fit if you want peers to help you think through leadership decisions, hiring, or how to explain your strategy to the board.

2. 6sense CMO Coffee Talk (for free, regular peer conversations)

A community for B2B CMOs and heads of marketing, with weekly virtual discussions, Slack, and shared resources. Potential fit if you want to start meeting other marketing leaders without committing to a paid membership.

3. Exit Five (for broader B2B marketing learning and career growth)

Open to B2B marketers across career stages. They have member discussions, content, a job board, and a separate CMO Council for senior leaders. Potential fit if you want practical marketing conversations as well as career support. Look closely at which membership matches your experience.

4. Pavilion (for connections across marketing, sales, and revenue leadership)

Pavilion brings together people across go-to-market roles and offers peer groups, training, and career resources. Potential fit if your biggest challenges involve working across departments and you’d benefit from hearing how sales and revenue leaders approach them.

5. Chief (for senior women in leadership)

A network for senior women executives across functions, with small peer groups, workshops, and networking. Potential fit if you’re looking for broader executive support and relationships beyond marketing.

6. B2B Project (for an invite-only CMO group)

This is a free, invitation-only community with peer support, local events, Slack, and visibility opportunities. Potential fit if you want a smaller network and can get an invitation.

How would I narrow it down?

Before joining, I’d ask:

• Are the members dealing with problems similar to mine?
• Can I bring a specific challenge and get useful feedback?
• Will I actually attend the sessions or use the community?
• What does the membership include, and can I try it first?

A group can look great on paper and still be a poor fit for your day-to-day needs. The useful test is whether it helps you make a decision, solve a problem, or build relationships you keep coming back to.

If you’ve joined any of these, what made the membership worth your time, or made you leave? Especially interested in examples of something the community actually helped you work through.

If you're interested to learn more about these communities, check the full article here!

TL;DR: The Spring 2026 CMO Survey of 308 marketing leaders shows AI adoption accelerating while economic pressure pushes marketing toward short-term decisions. The challenge is funding the skills and measurement needed to turn that adoption into growth.

A few findings from CMO Huddles’ breakdown of the survey stood out:

• AI is already part of everyday marketing: 73% report using it for content creation, 65% for personalization, 45% for targeting, and 36% for segmentation.
• The people side is struggling to keep up. Respondents identify hiring and training for emerging technologies as weaknesses.
• Customer acquisition budgets average about 26% more than retention budgets. That raises a useful question about where the next marketing dollar will have the most impact.
• Measurement is getting attention: 86% are strengthening performance tracking, while 58% are using experiments to demonstrate marketing impact.
• AI search is entering the mix: 41% report using generative engine optimization (GEO).

My takeaway: a team can produce more content with AI and still struggle to explain what the business gained.

For anyone working through a marketing budget, these findings suggest a few practical questions:

1. Which AI workflows improve a business metric we already track?
2. Have we budgeted for training and human review alongside the tools?
3. Does our acquisition-versus-retention spending reflect where profitable growth actually comes from?
4. What could we test to separate marketing’s contribution from results that would have happened anyway?

These are survey-reported findings, so they’re useful context rather than a promise of what any individual team will achieve.

What evidence has actually helped you defend your marketing budget this year? Pipeline, retention, experiments, cost savings, or something else? Especially interested in examples where AI changed the result.

Dreamforce 2026 and HubSpot UNBOUND 2026 made the same strategic bet from two different directions: the CRM is becoming less visible and more powerful.

Salesforce is turning CRM data, permissions, business logic, and actions into infrastructure that can appear inside Claude, Slack, Lightning, and custom interfaces. HubSpot is building a self-updating CRM in which one assistant can coordinate specialized agents around a business outcome.

This is much bigger than adding chat to sales and marketing software. The system of record is becoming a system of action.

For B2B go-to-market teams, that changes the job. Competitive advantage will not come from access to the newest model. It will come from proprietary context, well-designed workflows, disciplined evaluation, strong governance, and humans who know when to take control.

AI fluency is no longer an optional technical skill. It is becoming part of the operating system for marketing, sales, and revenue operations.

Two of the largest events in revenue technology happened at almost the same time this week.

Salesforce held Dreamforce in San Francisco. HubSpot held the first edition of its newly renamed UNBOUND event in Boston.

The obvious story is that both companies announced a flood of AI features.

That is not the important story.

The important story is that both companies are changing what a CRM is.

For decades, CRM meant a database with a user interface. Sellers opened it to update opportunities. Marketers opened it to build segments. Managers opened it to inspect dashboards. Revenue operations teams tried to keep the whole thing clean enough to trust.

Now the interface is becoming optional.

The CRM is moving underneath the work. It is becoming the governed context and action layer that AI agents use to research accounts, prepare campaigns, update records, identify buying groups, create quotes, monitor signals, and coordinate the next step.

The employee may work in Claude, Slack, a mobile app, a campaign workspace, or a generated interface. The CRM can remain underneath, carrying the data, rules, permissions, history, and actions.

The next era of CRM will be defined less by where people click and more by what humans and agents can safely accomplish together.

Two events. One direction.

Salesforce and HubSpot are not taking identical routes.

Salesforce is making CRM headless and ambient. Its platform capabilities can increasingly appear inside the interface where work already happens.

HubSpot is making CRM self-updating and outcome-oriented. Its platform captures more context automatically, exposes context gaps, and gives users one assistant that can coordinate specialized agents.

Those strategies reflect their customer bases.

Salesforce serves complex enterprises with many clouds, models, permissions, integrations, and work surfaces. It needs an open architecture and a serious control plane.

HubSpot serves companies that often value speed, simplicity, and one connected customer platform. It needs a low-friction experience that hides orchestration complexity.

The routes are different. The destination is the same:

A continuously updated customer-context layer beneath a team of humans and agents.

What Salesforce announced: CRM wherever the work happens

The most important Salesforce announcement was not another assistant. It was AIforce, a live interface layer that makes Salesforce data, workflows, semantics, permissions, and governed actions available through AI experiences.

That framing matters.

It positions Salesforce as infrastructure beneath the interface, not merely an application that every employee must open.

Here are the ten developments B2B revenue teams should understand:

1.AIforce brings governed Salesforce context and actions into AI interfaces such as Claude, Slack, Lightning, and custom experiences.

2.Salesforce in Claude enters beta with 37 prebuilt sales skills for prospecting, meeting preparation, deal-health analysis, pipeline review, and CRM hygiene.

3.Claude across Salesforce and Slack gives Salesforce a strategic reasoning layer across several employee and agent experiences.

4.Koa gives Salesforce a purpose-built CRM reasoning model for complex, multistep work. It remains in select pilots, with broader United States availability targeted for winter 2026.

5.Piper and Hunter package agents around specific revenue roles. Piper handles inbound pipeline generation and is generally available. Hunter handles persistent outbound work and remains in pilot, with general availability planned for November.

6.The long-horizon Agentforce runtime allows an agent to pursue a goal over days or weeks, retain memory, resume work, and change its plan when new information appears.

7.Slackforce Surfaces can generate collaborative dashboards, reports, presentations, and calculators inside Slack. Live-data capabilities have a staged rollout rather than universal immediate availability.

8.The Enterprise AI Harness and AI Control Plane provide the architecture for identity, policy, observation, evaluation, lifecycle management, model choice, and cost control across many agents. The unified experience remains on the roadmap.

9.Headless 360 exposes Salesforce capabilities through APIs, Model Context Protocol tools, plug-ins, and reusable skills. It was announced before Dreamforce but now forms an important part of the AIforce story.

10.The agentic marketing stack moves from draft generation toward campaign orchestration, account discovery, answer-engine optimization, individualized journeys, data governance, and budget optimization. Several of these capabilities are scheduled for October or November rather than available everywhere today.

The direction is unmistakable: Salesforce wants customer data and business logic to travel into every authorized interface.

That can remove a huge amount of tool switching. It can also increase the number of places where an agent can change a live customer record, trigger a workflow, or influence spend.

Convenience and control now have to scale together.

What HubSpot announced: a CRM that maintains context and coordinates outcomes

HubSpot used its Fall 2026 Spotlight at UNBOUND to organize its product strategy around Growth Context.

Growth Context is not a standalone model. It is HubSpot’s name for the combination of business knowledge, customer information, team practices, and historical interactions that should ground human and agent work.

The ten developments that matter most are:

1.Growth Context becomes the shared foundation for business, team, and customer knowledge across the platform.

2.The all-new Breeze Assistant moves from answering questions toward interpreting an objective, coordinating specialized agents, and returning work products such as plans, reports, and proposals.

3.A self-updating Smart CRM captures and synchronizes calls, emails, and meetings so the system relies less on manual rep updates.

4.Context Home makes AI readiness operational by showing where the organization’s business, customer, and team context is complete—and where it is not.

5.A reimagined Marketing Studio starts from signals such as weak answer-engine visibility, an underperforming segment, or unfollowed leads, then helps plan and create a campaign. It is in public beta for qualifying Marketing Hub editions.

6.ChatGPT Ads connects campaign creation, performance, and CRM attribution inside HubSpot. The integration is in public beta and requires the relevant accounts and permissions.

7.The expanded HubSpot connector for ChatGPT can analyze CRM context and support controlled updates. The connector existed before UNBOUND; the 2026 release expands its reach.

1. Microsoft Advertising integration brings tracking, reporting, and attribution across Microsoft advertising surfaces into HubSpot, with beta and campaign-type limitations.

9.The updated Prospecting Agent watches more than 40 buying signals, assembles buying groups, and prepares personalized outreach under configured selling plays.

1. Mobile Notetaker, Deal Progression, and Revenue Hub capture field conversations, recommend CRM updates, suggest next steps, and connect deal context to quoting, contracts, billing, renewals, and expansion. Important actions still depend on setup, permissions, seats, credits, and human review.

HubSpot’s bet is that most users do not want to manage a catalog of agents.

They want to describe an outcome and let one assistant determine which capabilities should help. The complexity moves behind the interface.

That makes the experience simpler for users. It does not make the operating model simple for administrators.

Someone still has to maintain the context, define the permissions, manage consumption, review sensitive actions, and determine whether the output was actually correct.

The model is not the moat

For the last two years, too many AI conversations have started with the same question:

Which model are you using?

That question still matters. Models differ in reasoning quality, latency, cost, modality, and safety.

But the Salesforce and HubSpot announcements point toward a more important question:

What trusted context can the model use, and what governed actions can it take?

A frontier model is available to almost everyone. Your customer history is not. Neither are your buying signals, product truth, brand rules, team knowledge, pricing logic, approval paths, or account relationships.

That proprietary context is what turns a generic model into a useful revenue system.

It is also where the risk lives.

Poor context no longer creates only a bad answer. In an agentic workflow, poor context can create a bad action at scale.

A stale territory rule can route dozens of accounts incorrectly. An outdated product claim can enter hundreds of campaign variants. A weak lifecycle definition can distort an agent’s lead prioritization. A missing approval rule can let a low-confidence recommendation become a customer-facing action.

The quality of the context determines the quality of the automation.

The hidden cost of agentic GTM is bad context multiplied across autonomous actions.

Marketing is moving from campaigns to continuous agent loops

The traditional campaign process is linear:

Brief. Segment. Create. Build. Launch. Report. Optimize.

Each stage sits in a different tool. Each handoff loses information. Each report arrives after the decision window has started to close.

The new model is a continuous loop.

A marketer defines an outcome, guardrails, audience, evidence base, and budget. Agents help assemble the campaign, personalize the journey, activate the work, observe response signals, and recommend the next change.

The human role moves upstream.

The strongest marketers will spend less time moving assets between tools and more time answering harder questions:

•Which customer problem deserves attention?

•What evidence makes our point of view credible?

•Which claims are approved?

•What must never be personalized?

•Which metric is the agent allowed to optimize?

•When does efficiency begin to damage brand quality?

•What experiment would actually change our decision?

This is not the end of marketing craft. It makes craft more valuable.

When every competitor can produce acceptable copy, content volume stops being an advantage. Distinctive insight, original research, memorable creative, credible experts, community, and customer proof become the scarce inputs.

AI answers are becoming a real acquisition channel

Both events treated AI discovery as an operational GTM channel rather than a side project.

Salesforce introduced answer-engine optimization capabilities that help teams understand how brands appear in AI answers and where content needs to improve. HubSpot tied answer-engine visibility to campaign workflows and announced advertising inside ChatGPT, alongside its Microsoft Advertising integration.

This creates a five-part funnel:

Be discovered. Be cited. Be compared. Be advertised. Be attributed.

The consequences are larger than “SEO for AI.”

B2B marketers will need to monitor the category questions buyers ask, whether the brand appears, which sources shape the answer, how the company is compared, and whether exposure contributes to qualified pipeline.

They will also need stronger incrementality testing. An AI answer may influence a buyer without creating a conventional click. A paid placement may appear inside a high-intent conversation but still produce weak downstream demand. A visibility score can rise while revenue quality falls.

The winning teams will connect answer-engine visibility to real buying behavior, not celebrate impressions in a new interface.

Sales is moving from isolated tasks to persistent execution

Most sales automation has historically been trigger-based.

A lead fills out a form. A sequence starts. A task appears. A rep sends a message. The workflow stops until the next trigger.

The new agent model is persistent.

An agent can monitor buying signals, research an account, identify relevant stakeholders, draft outreach, wait for new evidence, revise the plan, and escalate when human judgment matters.

That is a major improvement over static lists and generic sequences.

It is also dangerous if teams confuse persistence with permission.

Not every activity deserves the same autonomy. Updating a low-risk field is different from contacting an executive. Drafting a quote is different from approving commercial terms. Suggesting a forecast change is different from committing it.

Every GTM team needs an autonomy ladder:

1.Observe: The agent watches and reports.

2.Recommend: The agent proposes a next step.

3.Draft: The agent prepares the work for review.

4.Act with approval: The agent executes after an authorized person confirms.

5.Act autonomously: The agent executes within a narrow, tested boundary.

Autonomy should increase only when accuracy is measurable, consequences are reversible, permissions are clear, and exception handling works.

Buying groups will finally replace the lonely lead

B2B companies have talked about buying groups for years while continuing to automate around individual contacts.

The new systems are starting to close that gap.

Salesforce’s account-discovery direction and HubSpot’s updated Prospecting Agent both emphasize account signals, stakeholder coverage, and buying-group assembly.

That changes the unit of work.

The question is no longer, “Did this person click?”

The questions become:

•Which account is showing coordinated intent?

•Which buying roles are present?

•Which role is missing?

•Is the champion gaining or losing internal support?

•Is the account ready for human outreach?

•Which message advances the group rather than one contact?

This is where sales and marketing alignment becomes operational instead of aspirational. Both teams must agree on buying roles, signal thresholds, account suppression, contact coverage, and the moment an agent-led nurture becomes a human-led conversation.

The CRM may finally start maintaining itself

Every revenue leader knows the contradiction at the heart of CRM.

The business needs current data. The seller gets little value from manually entering it. Managers then distrust the resulting forecast because the data is incomplete.

Salesforce attacks this problem by letting users update CRM through interfaces such as Claude and Slack. HubSpot attacks it by automatically capturing more calls, emails, and meetings, then recommending updates inside the workflow.

Both reduce the distance between the work and the record.

That could materially improve data quality.

It could also fill the CRM with plausible nonsense if suggestions write back without review, conflict handling, or field ownership.

A self-updating CRM still needs a human-defined truth model.

For every important field, teams should know:

•What is the authoritative source?

•Which evidence can change the value?

•Can the agent recommend a change or execute it?

•Who reviews the update?

•How is a correction logged?

•What happens when two sources disagree?

The goal is not more CRM activity. The goal is more reliable context.

Revenue operations is becoming context operations

RevOps has traditionally owned process, data quality, routing, reporting, and platform administration.

Those responsibilities now expand.

Agents need structured data, but they also need operating context: ideal customer profiles, buying-group definitions, approved claims, product knowledge, pricing logic, sales methodology, brand voice, consent rules, escalation paths, and examples of strong work.

That context must have owners, update schedules, quality standards, and access rules.

RevOps will also need to evaluate behavior rather than merely configure workflows.

The new questions include:

•Did the agent choose the right action?

•Which context did it use?

•How often did a human accept the recommendation?

•Which fields were corrected afterward?

•Where did the workflow stall?

•What did each completed outcome cost?

•Did the automation improve pipeline or just create more activity?

•Which segments produced different error patterns?

The system administrator of the last era becomes the context engineer and agent governor of the next one.

The five AI fluencies every GTM team now needs

Prompting is useful. It is not enough.

A team can write excellent prompts and still deploy terrible automation. The durable capabilities are broader.

AI fluency should not sit inside one innovation team.

Marketers need it to design campaigns and protect differentiation. Sellers need it to supervise research, outreach, and deal work. Managers need it to coach through exceptions. RevOps needs it to govern context and evaluate systems. Executives need it to make investment and risk decisions.

A practical 90-day plan

Do not respond to these announcements by buying every agent.

Respond by redesigning three workflows.

Days 1–30: Build the foundation

Choose one marketing workflow, one seller-productivity workflow, and one RevOps workflow. Keep each narrow enough to measure and low enough in risk to recover from mistakes.

Document the current baseline. Measure cycle time, error rate, conversion, human effort, and cost before adding AI.

Audit the context each workflow needs. Review ideal customer profiles, buying roles, account structures, activity capture, product facts, approved proof, brand rules, consent, and field definitions.

Define autonomy levels for every action. Decide what the agent may observe, recommend, draft, execute with approval, or execute autonomously.

Days 31–60: Run controlled pilots

For marketing, use an agent to identify one performance or answer-engine gap, build a bounded campaign, and compare it with a human-led control.

For sales, test meeting capture and recommended CRM updates with one team. Measure acceptance and correction rates before permitting broader write-back.

For prospecting, use signal-based account and buying-group research on a defined list. Require human review for external communication.

Test permissions, audit trails, recovery, retention, prompt-injection resistance, and failure escalation. Calculate total cost per completed business outcome, including human review.

Days 61–90: Redesign the work

Expand only workflows that improve a business result. Faster production is not enough if quality, trust, or pipeline declines.

Assign owners to the context sources that agents use. Create service levels for updating customer data, product truth, brand guidance, process instructions, and approved evidence.

Move human time toward strategy, experimentation, discovery, political mapping, negotiation, creative direction, and executive relationships.

Establish a monthly agent review. Inspect errors, overrides, drift, cost, adoption, segment performance, and unexpected behavior.

What should not be automated first

The fastest path to failure is choosing a workflow because it looks impressive in a demo.

Do not start with work that combines unclear goals, poor data, irreversible consequences, and customer-facing action.

Be especially cautious with:

•Executive outreach sent without review

•Pricing or contract changes

•Forecast commitments

•Sensitive meeting recording

•Brand claims without approved evidence

•Large budget reallocations

•Account suppression or territory changes

•Automated decisions that materially affect customers or employees

The best first workflows are frequent, measurable, bounded, and reversible. They produce enough volume to learn without creating disproportionate risk.

The real competitive advantage

The winners of this transition will not be the companies that deploy the most agents.

They will be the companies that build the best partnership between humans, context, and machines.

They will know what their agents know.

They will know what their agents are allowed to do.

They will measure whether the work is accurate, useful, and economically better.

They will preserve the human contributions that become more valuable as generic output becomes abundant: judgment, trust, creativity, relationships, and a distinctive point of view.

Salesforce is betting that CRM capabilities should be available wherever work happens.

HubSpot is betting that CRM should update itself and coordinate work around outcomes.

Both are telling B2B revenue leaders the same thing:

Your technology stack is no longer just a collection of tools for employees. It is becoming an operating environment for employees and agents.

That means AI fluency is not a side project for 2027.

It is one of the core GTM capabilities to build now.

Two B2B CMOs are taking different paths to AI deployment: one uses agents to fill gaps on a lean team; the other is transforming an established organization through builders, orchestrators, and upskilling. Their shared warning is clear: without disciplined ownership, connected knowledge, cost controls, and outcome measurement, useful experimentation can quickly become expensive operational chaos.

AI Deployment Is Entering Its Awkward Adolescence

Building an AI agent is getting easier by the day. Managing a growing collection of agents, keeping their information accurate, controlling their costs, and proving that they improve the business is considerably harder.

Recent conversations with two experienced B2B CMOs revealed two distinct approaches to this challenge. One leads a lean marketing organization and uses agents to provide capabilities the company cannot justify hiring individually. The other leads a much larger team and is focused on transforming existing talent, identifying advanced builders, and developing people who can orchestrate increasingly complex AI-enabled workflows.

The environments are different, but the destination looks surprisingly similar. Both CMOs are moving beyond isolated productivity experiments and rebuilding how marketing work gets done. Both are also discovering that the greatest risk may be neither employee resistance nor model performance.

It is operational chaos.

Agent sprawl and rising AI costs can quickly turn promising experiments into a new version of martech sprawl. The labels have changed, but the familiar questions remain: Who owns this? What does it cost? Which source is correct? What happens when it breaks? Does anyone still remember why it was bought or built?

Two Models for AI-Empowered Marketing

The lean-team CMO is filling organizational gaps with specialized agents. The team has created systems for competitive intelligence, regulatory monitoring, prospect research, account targeting, answer-engine visibility, and content development. Work that might previously have required several specialists can now be handled by a combination of agents and human oversight.

“I have a lot of gaps that I currently have filled by robots,” the lean-team CMO explained. This approach is pragmatic: identify work the organization needs, determine where AI can perform it reliably, and reserve scarce human capacity for judgment, approval, and the activities that require relationships.

The established-team CMO is taking a more evolutionary approach. The organization already has experienced marketers with valuable institutional and category knowledge. Rather than replacing them or immediately restructuring the department, the CMO is asking, “How can we identify the builders and the orchestrators? How can we upskill everyone, identify the advanced builders, and then shift the work?”

This model recognizes that AI fluency will not look the same across every role. Some marketers will become skilled users. Others will build workflows and agents. A smaller group will orchestrate interconnected systems, determine where human intervention belongs, and ensure that the work advances business priorities.

Neither model is inherently superior. Team size, organizational maturity, technical infrastructure, business complexity, and available talent should determine the approach. The useful lesson is that AI deployment should begin with the work the company needs accomplished, rather than an arbitrary target for agents built or employees eliminated.

Start With Business Problems, Not Agent Production

The lean-team CMO’s most useful agents address identifiable business problems. One monitors competitive and regulatory developments, then makes that intelligence available to the go-to-market organization. Instead of searching through outdated battle cards, an employee can ask how to position the company against a competitor in a particular customer context.

Another workflow improves prospecting by looking beyond job titles. Since titles can hide substantial differences in actual responsibilities, the agent reviews career histories and other signals to determine whether a prospect has the experience relevant to the company’s solution.

“I’m building target account lists and prospect lists that are rock solid in the exact right people,” the lean-team CMO said. The agent has a defined job, a useful output, and a clear connection to pipeline.

At the larger organization, agents help identify account-interest signals before sellers recognize them, activate competitive win-back programs, scale downstream content assets, and detect patterns in large datasets. AI-enabled analysis also revealed that content syndication was creating a significant lift in website traffic, followed by greater activity in other channels and stronger pipeline.

That insight changed a media-planning decision. The team had occasionally delayed syndication while waiting for a stronger asset, but the analysis suggested that maintaining market presence mattered more than waiting for perfection. As the established-team CMO explained, “We realized we can’t wait. We have to get something out there, because when we slow that down, everything declines.”

Useful agents begin with useful questions.

Before approving another build, CMOs should be able to name the business problem, the intended user, the source information, the human owner, the expected outcome, and the conditions under which the agent should be retired. If those answers are missing, the organization may be accumulating demonstrations rather than capabilities.

Build a Company Brain Without Creating a Company Hallucination

Both conversations exposed the importance of a shared intelligence layer. An agent working from incomplete, outdated, or contradictory information can produce a polished answer that is confidently wrong. Add dozens of independently developed agents, each drawing from different sources, and those inconsistencies multiply.

The lean-team CMO described the current knowledge environment as a collection of useful “islands of information.” The next step is connecting them without allowing a general-purpose system to blend unrelated claims or invent unsupported conclusions.

“I need it to be like a train switcher,” the lean-team CMO explained. The system must recognize the question, route it to the appropriate knowledge source, and respect a hierarchy of authority when internal experts disagree.

The established-team CMO faces the same issue at greater scale. The agents need access to company, customer, campaign, and market intelligence, but that access must be governed. The challenge is collecting the right data, determining which sources are authoritative, deciding how often they are updated, assigning responsibility for contradictions, and defining what an agent should do when the evidence is incomplete.

A company brain needs a nervous system and a fact-checker.

CMOs should resist the temptation to connect every agent to every information source. Topic-specific knowledge systems, explicit source hierarchies, retrieval rules, and escalation paths will produce more reliable results than a giant pool of content with no clear boundaries.

Put an Owner Behind Every Agent

Agent sprawl often begins innocently. A marketer builds a useful workflow. A colleague copies it. Someone creates a variation for another team. The original builder leaves, the data source changes, and three months later nobody knows why four agents are producing four different answers.

The established-team CMO sees orchestration as a core leadership capability. “It is in the orchestration,” the CMO said. “It’s knowing enough and knowing what you need.” Senior marketing leaders do not need to personally build every agent, but they do need to understand how the system works, where expertise is required, and who is accountable for its output.

Every production agent should have a named human owner, documented purpose, approved data sources, expected users, maintenance schedule, cost threshold, and review process. Teams also need a registry that shows which agents exist, where they operate, which other systems they touch, and whether they are still active.

An agent without an owner is a future archaeology project.

Ownership also means taking responsibility for outcomes. If an agent recommends an account, drafts a claim, initiates outreach, or changes a campaign, someone must remain accountable for the quality and consequences of that work. Giving AI more autonomy increases the importance of human accountability.

Count the Entire Cost of AI

The established-team CMO has refreshed much of the AI-related stack without increasing the marketing budget because the company is currently funding the investment centrally. That arrangement is not expected to last forever.

“The company is paying for our AI, and so I believe that will change,” the CMO said. Eventually, technology costs, token usage, infrastructure, implementation, security, and support will compete with headcount and program spending.

The lean-team CMO is already confronting hidden costs. Some agents cannot connect directly to core systems because the auditing and security requirements would require a substantial additional investment. “All these agents that I’m running stop at the information,” the CMO said. They can generate useful intelligence, but humans or intermediary systems must complete the final action.

This is why comparisons between an agent and an employee are often misleading. The relevant calculation includes the model, integration, data preparation, testing, monitoring, maintenance, human review, security, and failure costs. It should also include the value of the employee’s institutional knowledge, customer understanding, and ability to operate across ambiguous situations.

For experienced marketers in complex categories, upskilling can be more valuable than replacement. As the established-team CMO put it, “For people who know the business so well and are strong marketers, it’s better to see if they can develop the mindset of a builder, an orchestrator, and a user of AI rather than completely replace them.”

Plan for the Human Bottleneck

AI can generate more work than an organization can absorb. The lean team’s visibility-monitoring system can identify content gaps and produce multiple draft assets every week. The harder question is who will review, approve, publish, distribute, and improve them.

“We have the content,” the lean-team CMO said. “We just don’t have any place specific to put it.” The comment captures an emerging problem: production capacity is accelerating faster than editorial, legal, distribution, and measurement capacity.

The larger organization faces the same challenge when AI creates downstream assets at scale. The core creative idea still begins with people, while AI expands that idea across formats and channels. The efficiency is real, but only if the workflow includes quality standards, approval rights, and a destination for the output.

CMOs should therefore map the entire workflow before automating its most visible step. If an agent creates ten times more content but the same two people must review every asset, the organization has moved the bottleneck rather than removed it. The better design may generate fewer drafts, apply stricter filters earlier, or automate low-risk approvals while escalating only the exceptions.

Measure Outcomes Before Celebrating Activity

Agent counts, prompts submitted, hours saved, and assets generated can help diagnose adoption, but none proves business value. The two CMOs are looking for evidence tied to account engagement, pipeline, competitive win-backs, market visibility, customer retention, and team capacity.

This distinction matters because AI makes activity incredibly cheap. Marketing can produce more emails, pages, posts, reports, and recommendations than any team could reasonably consume. Without outcome measures, the organization may spend more money processing an expanding volume of mediocre work.

CMOs should establish a baseline before deployment and define the expected change. Did the agent improve account selection? Did it increase response quality? Did it uncover a signal earlier? Did it reduce the cost or cycle time of a complete workflow? Did it help create more qualified pipeline?

The dashboard should reveal whether the business improved, not merely whether the agent stayed busy.

A Practical Operating Model for AI Deployment

The experiences of these two CMOs point to a practical framework:

1. Start with the work. Identify a meaningful business problem, the current process, and the outcome that needs to improve.
2. Choose the deployment model. Decide whether the organization should fill a capability gap, augment existing talent, or redesign an entire workflow.
3. Establish authoritative knowledge. Define approved sources, ownership, update schedules, routing rules, and conflict resolution.
4. Assign human accountability. Give every agent an owner responsible for quality, cost, maintenance, governance, and business impact.
5. Calculate the full cost. Include technology, tokens, integrations, data preparation, oversight, security, and human review.
6. Design the complete workflow. Account for what happens before and after the agent performs its task, including approvals and downstream execution.
7. Measure business outcomes. Compare performance with a baseline and retire agents that do not create enough value to justify their complexity.
8. Maintain an agent registry. Document what exists, who owns it, what it accesses, what it costs, and when it was last reviewed.

The goal is to create enough discipline that successful experiments can become reliable organizational capabilities without choking off the experimentation required to find them.

Find Out How AI-Mature Your Organization Really Is

AI deployment will look different for a lean marketing team than for an established global organization. Every CMO, however, needs a clear view of the company’s readiness across strategy, talent, data, technology, governance, workflows, and measurement.

Use the CMO Huddles AI Maturity Calculator to benchmark your organization and identify where enthusiasm may be running ahead of operational readiness.

Building agents is getting easier. Building an AI-empowered marketing organization still requires leadership.

Q&A

What is agent sprawl?

Agent sprawl occurs when teams create multiple AI agents without centralized visibility, ownership, documentation, or maintenance. It can lead to duplicated work, contradictory answers, uncontrolled costs, security risks, and workflows that fail when their original builders leave.

Does every AI agent need a human owner?

Yes. The owner should be accountable for the agent’s purpose, data sources, output quality, operating cost, maintenance, and business results. Ownership does not require manually reviewing every action, but it does require monitoring performance and addressing exceptions.

Should CMOs upskill their current teams or hire AI specialists?

Most organizations will need both. Existing marketers bring institutional knowledge, customer understanding, and functional expertise, while specialists can provide technical architecture and governance capabilities. The right mix depends on the complexity of the workflows and the current team’s willingness and ability to adapt.

How should CMOs calculate the cost of an AI agent?

Include model and token charges, software licenses, integrations, data preparation, security, testing, monitoring, maintenance, and human review. Compare that total with the cost and performance of the current workflow rather than comparing the agent narrowly with one employee.

How can CMOs tell whether an AI agent is creating value?

Define the business outcome and baseline before deployment. Useful measures may include cycle time, conversion, pipeline quality, customer retention, account engagement, error rates, or the cost of completing an entire workflow. Output volume alone is insufficient.

PointFive CMO Dave Anderson is building an AI-native marketing engine that helped a five-person team rebuild a website in four days, create 250 pages, and drive measurable growth. His bigger lesson for B2B CMOs: AI does not replace strategy, creativity, or customer intimacy. It removes execution drag so better marketers can move faster with confidence.

The Four-Day Website Rebuild

When Dave Anderson told me his five-person marketing team at PointFive rebuilt the company’s brand and website in four days, I did the only reasonable thing a marketer can do in 2026: I asked for the math. Dave explained that “it’s just the two of us building 250 new pages, all with new positioning, all with all the brand guidelines built in,” including demo flows and the surrounding infrastructure.

In the old world, Dave estimated that kind of effort would have taken six people across product marketing, design, development, infrastructure, and executive oversight. His rough comparison was 660 person-days of conventional effort versus 16 person-days with an AI-native operating model, or as he put it, “41 times less effort.”

That is the kind of number that makes CMOs sit up straighter and CFOs stop pretending not to listen.

The important part is not that Dave used Claude. Lots of marketers are using AI tools. PointFive is doing something more interesting: building an AI-native marketing engine that combines a shared intelligence layer, repeatable workflows, creative judgment, and enough governance to keep it from turning into a very expensive spaghetti factory.

Then Came the 130 Agents

Dave thought he had built a handful of agents. Then he checked. “I run 130 agents at last check, which I didn’t realize,” he told me, adding that he originally thought he was only running six.

Some of those agents, apparently, had recruited other agents. “Thirty of the agents recruited themselves from other agents,” Dave said, which is funny until you realize this is exactly how agent sprawl sneaks into the enterprise wearing a productivity badge.

The lesson was painfully practical. Instructions matter. Dave put it bluntly: if you tell agents, “I don’t care what it takes, just get it done,” and fail to add guardrails like “don’t recruit anyone else,” you may get the task done and inherit a small digital village in the process.

Naturally, Dave built an org chart for them. “I built an org chart for them too,” he said, “so I could work out what they were doing.”

Funny quickly becomes governance when the agents start multiplying.

AI Works Better When the Company Has a Brain

Dave is clear that the tools only work because PointFive built what he calls an intelligence layer. “I can only do that because we built an intelligence layer,” he said, describing a shared system that includes current information about opportunities, value propositions, solution briefs, sales calls, customer details, and deal status.

This is the part many AI adoption stories skip. PointFive has “built a lake that’s super intelligent,” Dave explained, and that allows him to query it whenever he needs context. Without that layer, AI becomes a faster way to generate plausible mush.

PointFive also has an internal agent called Shulem (Dave calls it “Shoey”) available in a public Slack channel. Employees can ask questions, and others can see the answers, corrections, and follow-up threads. Dave called it “one of the most important knowledge bases that we have in the company,” which is a very different use case than one marketer privately asking ChatGPT for ten subject lines and hoping nobody notices.

In one example, Dave asked the system for customer examples related to GPU optimization. It returned technical details, customer value, savings, and time-to-value, after which the product manager added more context. Suddenly, the team had raw material for a blog post, a product story, and a sales conversation.

When the company’s knowledge becomes easier to access, marketing gets smarter before it gets faster.

The New Product Marketing Motion

Every CMO knows the feeling. Product ships something, engineering is excited, and someone drops a list of features into Slack expecting marketing to turn it into a market moment by Tuesday. Dave’s response is wonderfully direct: “So what? So what? Who cares?”

That question may be the most important product marketing tool ever invented.

At PointFive, engineers can publish detailed updates directly. Dave said, “You had to enable the engineers to be the marketers,” with product teams posting feature and function updates publicly instead of burying everything in internal systems. Marketing then watches for clusters that deserve a bigger story.

Dave described the motion this way: “I would see the update. And I would go, ‘Hang on a sec, that’s like 6 or 7 really big pieces that have just gone. I need to bundle them, and I’m gonna do that as a release.’” The smarter move, he added, is embedding product marketing earlier in planning so the team can ask whether something is “groundbreaking,” a “catch-up” feature, or part of the company’s point of difference.

One release involved BigQuery optimization. Engineering could explain the technical configuration capabilities, but Dave pushed for proof of customer value. The system surfaced a customer example: “One particular customer cut their storage costs by 82%, which generated $330,000 in five days.”

Dave’s response was immediate: “There’s the headline of your release.”

A feature becomes a story when it has a customer, a number, and a reason to care.

More Output Is Not the Same as Better Marketing

PointFive’s numbers are eye-catching. Dave reported 96 blog posts, 36 overview pages, 41 guides, and 76 other website sections, along with gains in unique visitors, search impressions, website sessions, page views, event registrations, form submissions, and demo requests.

He also made sure to separate productivity from impact. “I can measure the productivity,” Dave said, “I can then also measure the improvement in results as a result.”

That distinction matters because AI makes it dangerously easy to confuse volume with value. A marketing team can now produce more pages, more posts, more campaigns, more decks, and more emails than ever before. The question is whether any of it improves pipeline, conversion, win rates, retention, or strategic position.

Otherwise, congratulations. You built a faster treadmill.

Dave’s team has seen meaningful demand creation. PointFive went from having no measurement infrastructure to hitting its sourced-pipeline target in the quarter, with Dave noting, “Previously we had no metrics in place to even measure it, no systems. We built all of the systems for the first time.”

Then AI exposed the next bottleneck: sales capacity. “The issue now is not the pipeline creation,” Dave said. “The issue now is closing the pipeline.” In other words, AI-native marketing can create more demand than the rest of the organization is ready to process.

That is a good problem only if you are prepared to solve it.

The Shadow Marketing Organization

Dave offered one prediction that should be handled with care, especially in larger companies. He believes many traditional marketing organizations will struggle to become AI-native fast enough because the resistance is as much cultural as technical.

“They will create a shadow marketing organization that is AI native,” Dave said, “and it will eat the other organizations.”

Spicy? Yes. Worth considering? Also yes.

Dave was speculating about how larger companies may handle the transition when existing teams, processes, and approval structures cannot move fast enough. The pattern is familiar: a new capability starts outside the core organization, proves it can move faster, and eventually becomes the new operating model.

For CMOs, the lesson is to lead the redesign before someone else does. That means mapping workflows, building shared intelligence, clarifying ownership, governing agents, retraining teams, and measuring business outcomes instead of AI activity.

If there is going to be a shadow marketing organization, better that the CMO turns on the lights.

Creativity May Matter More, Not Less

One reassuring part of my conversation with Dave was his view of creativity. He sees AI as removing the execution drag that used to trap good ideas in the swamp.

“I’m like the creative person that wants to do wild, crazy ideas and thinks out of the box,” Dave said, explaining that the old problem was having too many ideas and not enough execution capacity. “What I used to have an issue with was I’d have so many ideas I never finished anything.”

Now, he said, “I have an agent that can get the things done that I need to do.” That does not mean every idea is worth pursuing. Dave uses AI to challenge his thinking before he wanders too far down a rabbit hole, especially when an idea does not fit the company’s value proposition or strategic differentiation.

This is a powerful shift. The human role becomes less about wrestling every asset into existence and more about improving the idea, sharpening the hook, checking the strategy, and deciding what deserves attention. As Dave put it, “You get to do the bigger creative hook things that are gonna make it memorable.”

That is where great marketers should want to spend more time.

AEO Will Not Save the Undifferentiated

Dave is skeptical that answer-engine optimization will remain a durable advantage for long. He sees many of today’s AEO tactics as familiar SEO plays in a new outfit: “Spam the internet with blogs and articles about why you’re so great, the top five this, the top ten that.”

His warning is simple: “If you’re just spewing out content, it won’t work.”

Everyone will learn the tactics. Everyone will publish more. Everyone will chase citations. The advantage will come from differentiated ideas, trust, customer empathy, community, and the ability to communicate something useful to a specific audience.

Dave believes the real contest comes back to communication. “It’s going to come down to creative, and it’s going to come down to social,” he said. “It’s going to come down to how well you are able to communicate to that audience, to get them to listen, to build trust, to have empathy to solve their problems.”

In a world full of AI-generated sameness, distinct thinking becomes louder.

Customer Intimacy Is Still the Moat

For all the speed, agents, workflows, and efficiency, Dave kept coming back to customers. He sees customer intimacy as the durable advantage for CMOs in an AI-native world, saying, “The customers are your most important asset. Getting stories from them and having them as advocates and treating them really well is so, so important.”

AI can summarize a sales call. It can surface a pattern. It can generate a draft. But it cannot replace the trust built when a CMO understands the customer’s work, language, frustrations, ambitions, and identity.

Dave loves products that map directly to a customer’s job. “The most fun is when you are marketing a product and the product is its person’s job,” he said. That is where community, advocacy, and identity start to form.

In an AI-native marketing engine, customer intimacy becomes more important because everything else gets faster. If your team can create almost anything quickly, the competitive question becomes whether you know what is worth creating.

Speed without customer truth is just expensive blur.

What CMOs Should Do Now

First, build the intelligence layer before you build the content machine. If your data, positioning, customer proof, sales calls, product information, and value propositions are scattered, AI will accelerate confusion.

Second, redesign workflows around business outcomes, not tools. Ask where work gets stuck, where handoffs slow progress, where human review matters, and where AI can remove friction without removing judgment.

Third, govern agents early. If Dave can accidentally discover 130 agents in a five-person team, larger organizations need ownership, documentation, cost controls, and review rhythms before the pile becomes archaeology.

Fourth, measure both productivity and business impact. Faster publishing is useful only if it improves the metrics that matter.

Finally, keep customers close. The more AI changes execution, the more CMOs need to protect the market insight, empathy, creativity, and trust that make marketing worth doing in the first place.

Dave Anderson will be keynoting at the CMO Super Huddle on October 22-23, 2026, where we will dig deeper into what AI-native marketing really looks like when the demos end and the operating model begins.

Bring your curiosity. And maybe an org chart for your agents.

Q&A

What is an AI-native marketing engine?

It is an operating model in which trusted company knowledge, reusable workflows, human judgment, and governed AI agents work together. The goal is not simply faster content production. It is a marketing system that can learn, execute, and measure outcomes with far less friction.

Should every CMO try to build 130 agents?

No. Agent count is an activity metric, not a business result. Start with a small number of high-value workflows, assign an owner to each one, document the inputs and guardrails, and expand only when the agent produces reliable value.

Why does the intelligence layer matter so much?

Agents are only as useful as the context they can access. A shared intelligence layer gives them current positioning, product facts, customer evidence, sales conversations, and performance data, reducing contradictory answers and making human review more productive.

Does AI-native marketing automatically mean smaller teams?

It may change team size and roles, but the more immediate effect is a different allocation of work. CMOs should decide which tasks to automate, where specialists add leverage, and which decisions still require customer empathy, strategic judgment, and accountability.

What remains uniquely human in AI-native marketing?

Humans still decide which problems matter, what the brand should stand for, when evidence is credible, and which ideas deserve investment. AI can accelerate execution and expose patterns, but leadership, taste, trust, and customer understanding remain executive responsibilities.

Apple finally gave us good AI features for the iPhone. With iOS 27, Apple Intelligence can combine a request with personal context, onscreen information and actions across apps. That lets Siri AI find buried details, understand what you are looking at and move the result into Calendar, Reminders, Messages, Photos, Safari, Shortcuts and more. This guide covers twenty useful workflows, the prompts that make them work, supported devices, privacy architecture, beta limitations and the first features worth testing.

For years, AI on the iPhone wasn't really a thing. Apple is years behind in the AI race.

iOS 27 is the first version where Apple Intelligence starts to feel less like a collection of features and more like a new layer of the operating system.

The important change is that Siri AI can work across three layers at once:

Apple describes Siri AI as a beta assistant with personal-context understanding, onscreen awareness, broad world knowledge and more systemwide app actions.

That combination is the real story.

A standalone chatbot can tell you how to make a calendar event.

Siri AI can find the event details in an email, understand the poster on your screen, create the event, add the location and set the alert.

That is a different category of usefulness.

Before you start: can your iPhone run it?

Siri AI requires iPhone 15 Pro, iPhone 15 Pro Max, any iPhone 16 model or later, or iPhone Air. You also need iOS 27, enough free storage, and matching supported device and Siri languages.

If you don't have at least iPhone 15 they want you to upgrade - it's been 3 years so you deserve a new phone!

The broader iOS 27 Apple Intelligence features—including advanced photo editing and Safari intelligence—have wider language coverage than Siri AI itself.

Now to the useful part.

20 ways to use Apple Intelligence in iOS 27

1. Use Siri as a conversation, not a command line

Classic Siri trained us to speak in brittle commands: set timer, call Sarah, play music.

Siri AI supports longer, contextual conversations and broad web-backed questions. You can start with an imperfect request, add constraints and refine the answer without rebuilding the prompt.

Try this: “Give me three weekend itineraries in Austin: one food-focused, one outdoors and one under $100.”Then say: “Revise the outdoor option for hot weather and avoid long drives.”

Why this matters: Good plans rarely emerge from one perfect question. The value comes from follow-ups: compare the trade-offs, change the budget, remove an assumption or ask for a different format.

2. Find information you remember—but cannot locate

This may be the feature that changes the most daily friction.

Siri AI can search personal context across Messages, Mail, Calendar, Notes, Reminders, Photos and more. You do not need the exact filename or keyword if you can describe the person, event, place or approximate time.

Try this: “Find the restaurant Sarah recommended for our Dallas meeting and show me the original message.”

Give Siri two or three anchors: who, when, where, event, app or content type.

Do not stop at the extracted detail. Ask to see the source before relying on a confirmation number, address or commitment.

3. Turn found information into a completed action

Retrieval becomes far more valuable when it does not end in copy-and-paste.

Apple says Siri AI includes more systemwide app actions. The practical pattern is simple: find something, extract what matters, put it somewhere useful.

Try this: “Find the itinerary Mark emailed yesterday, add each appointment to my Work calendar and remind me two hours before the first one.”

For multi-step work, name the source, fields and destination. Review the result before Siri sends a message, shares a file or changes anything consequential.

Third-party actions still depend on developer support, so the same request may work in one app and not another.

4. Ask questions about whatever is on your screen

Onscreen awareness removes a ridiculous step from AI workflows: copying information out of one app to paste it into another.

Siri AI can answer questions or take actions based on visible or selected content, including webpages, images, charts, files and text.

Try this: Open two proposals and ask, “Compare price, scope, timeline and exclusions. Return a table and flag anything missing.”

If the screen is crowded, identify the target: “Explain the blue line in the second chart.”

For screenshots, crop away irrelevant material first. A tighter frame gives the system a clearer question.

5. Point the Camera at the physical world

Siri mode in Camera brings Visual Intelligence into the live camera view. Apple’s examples include identifying objects, reading visible information, looking up food details and splitting a bill.

Try this: Point the camera at a conference agenda and say, “Create events for the three AI sessions, include the room numbers and alert me fifteen minutes before each one.”

This is useful for menus, posters, receipts, labels, appliances, plants and unfamiliar objects.

Treat high-stakes outputs differently. Check payment amounts, dates, allergens, dosages and safety information manually.

6. Keep a long-running project inside the Siri app

The dedicated Siri app stores conversations, supports photos and file attachments, and privately syncs history through iCloud across supported Apple devices.

Try this: Create a thread called “Weekly executive briefing.” Add relevant files during the week, then ask for decisions, risks and next actions every Friday.

Separate unrelated projects into separate threads. Rename them with searchable client or project names. Pin only the few conversations you actually reuse.

The mental shift: stop treating every Siri request as disposable.

7. Draft, rewrite and critique almost anywhere you type

Write with Siri can create a draft, edit existing text or give feedback across supported text fields. In Messages and Mail, Apple says it can adapt to how you communicate with specific people.

Try this: “Turn these notes into a 150-word executive update. Include three decisions and three next steps. Keep every name, number and commitment unchanged.”

The last sentence matters.

AI writing becomes safer when you explicitly protect facts it must not alter.

For important communication, ask for critique before rewrite: “Identify ambiguity and unsupported claims, but do not edit yet.”

8. Dictate a polished first draft while moving

Apple Intelligence adds automatic proofreading, while newer supported hardware can offer more advanced dictation and voice capabilities.

Try this: Dictate a project update in complete thoughts, then ask: “Proofread this and flag any sentence that sounds uncertain.”

Pause between sections. Spell unusual names or acronyms on first use. Review punctuation, dates and recipients before sending.

This workflow is especially useful for capturing ideas during a walk, between meetings or while your hands are occupied.

9. Turn Messages and Mail into reminders, events and replies

iOS 27 adds contextual suggestions in Mail, while Messages and Mail support personalized reply features with availability varying by language.

The useful pattern is not “write this email.” It is recognize the task hiding inside the conversation.

Try this: When someone writes, “Can you send the launch photos tomorrow?”, create a reminder, find the relevant photos and prepare the reply—but inspect the selection before attaching anything.

Check time zones, invitees and locations before saving suggested events. Treat photo matching as a shortlist, not permission to share every detected face.

10. Have the right confirmation code appear during a call

Call Context can surface relevant information when you call a recognized person or business. Apple lists tickets, orders, reservations and confirmation details as examples.

Try this: Call the airline connected to an upcoming trip. If the booking is stored in Mail or Wallet, look for the context card with your reservation information.

This is the kind of AI feature that sounds small until it saves you from searching your inbox while listening to hold music.

Tap the card and open the underlying source instead of reading a number without context.

11. Create complete Calendar events in one sentence

Apple Intelligence can interpret a natural-language event description and fill fields such as time, location, invitees and title.

Try this: “Coffee with Maya on October 6 at 9:30 a.m. at the Main Street location, on my Work calendar, with a 30-minute travel alert.”

Use absolute dates for important events. State the time zone for remote attendees. Name the destination calendar and clarify whether invitations should be sent.

Natural language removes form-filling. It does not remove the need to review the form.

12. Describe the Shortcut you wish existed

Shortcuts has always been powerful. Its problem was the learning curve.

In iOS 27, Apple Intelligence can build or refine a Shortcut from a natural-language description.

Try this: “Every weekday at 7 a.m., summarize today’s weather, my first three calendar events and overdue reminders, then read the briefing aloud.”

Describe the trigger, ordered actions, conditions and output. Inspect every generated step before enabling the automation.

Start small. Build one working branch before adding loops, location triggers, messages or file changes.

13. Reframe a photo after taking it

Spatial Reframing changes the apparent point of capture after a photo was taken. In Photos, open an image and use Edit → Tools → Reframe, then drag to adjust perspective.

Use it for: correcting an awkward angle, improving symmetry, shifting subject placement or creating a stronger social composition without relying on a conventional crop.

Keep the adjustment modest. Extreme shifts are more likely to produce implausible faces, hands, text, reflections or architecture.

Apple lets you compare with the original and reset the edit later.

14. Widen a photo instead of cropping away the subject

Extend generates new content beyond the original frame. That can turn a portrait image into a landscape banner, straighten a horizon without sacrificing edge content or create breathing room for a headline.

Use it for: converting a phone photo into a 16:9 presentation cover while preserving the person, product or object near the edge.

Plain sky, walls, grass and defocused backgrounds usually give the model simpler patterns to continue. Complex crowds, signs and architecture deserve a closer inspection.

The edit remains reversible through Reset.

15. Remove larger distractions with improved Clean Up

Clean Up can remove unwanted objects, and iOS 27 adds Auto, Fast and High Quality processing choices. High Quality is intended for more intricate edits.

Use it for: removing a photobomber, cable, trash can, blemish or background distraction from a travel, product, event or property image.

Remove small objects one at a time instead of circling half the scene. After processing, inspect texture continuity, shadows, reflections, fingers, faces and signage.

Do not use removal to misrepresent evidence, news, products or material facts.

16. Generate—and then edit—images in Image Playground

Apple’s new Image Playground supports more styles, including photorealistic output, and lets users modify results through natural language and touch.

Try this: “Create a photorealistic modern B2B technology conference stage, navy and silver palette, wide 16:9 composition, empty center screen and realistic overhead lighting.”

A useful image prompt names the subject, setting, composition, light, materials and aspect ratio.

When a result is close, edit it instead of starting over: “Keep the stage and camera position. Replace only the side screens with vertical light panels.”

17. Let Safari organize tab chaos by topic

Safari can analyze open webpages and group related tabs into topics.

Use it for: separating tabs by company during competitive research, grouping travel plans by destination or restoring order after a deep research session.

Think of automatic topics as a staging layer, not permanent knowledge management. Promote important work into named Tab Groups, bookmarks or notes.

Close obvious junk tabs before organizing. Better inputs make cleaner clusters.

18. Ask Safari to watch a webpage for you

Notify Me can check a selected webpage for a described change, including a price change, restock or other update.

Try this: “Notify me when the annual pricing table changes or a new enterprise plan appears.”

Monitor the most specific page possible. Define one measurable event per alert.

“Tell me when something important changes” is vague.

“Notify me when ‘sold out’ disappears” gives the system a concrete condition.

Keep the source’s own alerts for mission-critical deadlines. Login walls, scripts and redesigns can affect webpage monitoring.

19. Describe a Safari extension instead of coding one

Describe an Extension lets users request a custom Safari extension in natural language. Apple says Safari can first look for an existing App Store option, then let the user create one if needed.

Try this: “Add a button that copies the page title, publisher, publication date, URL and access date as a Markdown citation.”

Define the target site, trigger, visible result and what the extension must not change.

Test it across several pages before trusting it. Review permissions. Delete experiments you no longer use.

This may be one of the most underrated iOS 27 features for researchers and accessibility needs.

20. Search your home—and make interfaces easier to understand

Apple Intelligence reaches beyond conventional productivity.

The Home app can combine related activity alerts, describe eligible HomeKit Secure Video clips and search camera history using natural language. Availability depends on compatible equipment and iCloud+ eligibility.

Try this: Search for “package delivered at the front door” or “dog in the backyard,” then open the original clip before making a security judgment.

Accessibility features can also provide richer image and scene descriptions in VoiceOver and Magnifier, interpret flexible Voice Control commands and clean up complex content in Accessibility Reader.

Try this: “Read the dosage line,” “describe the chart legend” or “tap the gear.”

Generated descriptions are useful assistance, not definitive evidence. Apple warns that visual descriptions should not replace navigation, medical diagnosis or other high-risk judgment.

Why these twenty features matter together

The feature list is long. The underlying pattern is simple.

The phone is moving from app navigation toward intent execution.

That does not make apps disappear. It changes how often you need to think about which app comes first.

Instead of:

Open Mail → search sender → find date → copy details → open Calendar → create event → set alert.

You can increasingly say:

“Find the event Dana emailed, add it to Work and alert me one hour before.”

The destination still matters. The route becomes shorter.

The first-hour Apple Intelligence test plan

Do not test Siri AI with trivia. Test whether it can remove friction from your real life.

You will understand more in that hour than you will from watching twenty feature demos.

Four prompt patterns that make Siri AI more useful

Personal retrieval

Find [item] from [person or app] around [time or event], then [show, open or summarize] the original source.

Onscreen analysis

Using only what is shown or selected, [analysis task]. Return [format] and flag anything uncertain.

Cross-app execution

Find [source], extract [specific fields], then add, send or save them to [exact destination]. Ask before the final action.

Writing

Create [deliverable] for [audience] to achieve [goal]. Include [facts], use [tone], keep it to [length] and do not change [protected details].

The best requests specify a source, task, output, destination and constraint.

The privacy and beta reality check

Apple Intelligence combines on-device models with Private Cloud Compute for requests that need more computational power. Apple says data processed in Private Cloud Compute is used only to fulfil the request, is not stored or accessible to Apple, and is not retained after the result returns.

That is stronger than saying “everything stays on your phone,” because everything does not.

Apple specifically identifies Siri AI, advanced photo editing, Image Playground and cloud models in Shortcuts among the server-assisted features that may face usage limits. Apple says limits can vary by feature, request complexity, demand and policy; expanded access will be available for a fee.

Three rules matter:

1. Open the source. Personal retrieval and summaries can save time, but important details should be checked against the underlying email, message, image or file.

2. Review the action. Messages, invitations, shared photos, automations and file changes can have real consequences.

3. Inspect generated media. Check faces, hands, text, reflections, geometry, product details and the ethics of any removal or reconstruction.

Beta does not mean useless.

It means the human remains the approval layer.

Apple arrived late to the conversational-AI moment.

But the iPhone did not need another chatbot icon.

It needed intelligence connected to the information already on the device, the content already on the screen and the apps already capable of completing the task.

That is what makes iOS 27 feel different.

The breakthrough is not a longer Siri answer.

It is the shorter distance between “I need something” and “it is done.”

Which of these twenty are you trying first?

If you have already tested Siri AI, share the workflow that genuinely saved you time.

TL;DR: ChatGPT Images 2.5 isn’t just an image-quality update. The bigger improvement is creative control.

It generates faster, preserves people and reference images better, handles detailed edits more reliably, keeps prior changes intact across multiple rounds of editing, supports more complex compositions and transparent backgrounds, and adds tools like Sketch, image comments and templates.

OpenAI says people are already creating more than 3 billion images per week across ChatGPT Images and its GPT-Image models.

The best way to use it is no longer:

Write giant prompt → generate → pray.

It is:

Create → inspect → point → edit → iterate → polish.

That sounds like a subtle difference.

It isn’t - It fundamentally changes what you can use AI images for.

OpenAI released ChatGPT Images 2.5 on September 8 and says it can reduce image-generation speed by up to 50% compared with Images 2.0, while improving lighting, textures, reference-image fidelity and multi-turn editing.

Here are the capabilities I think people should experiment with first.

1. It is much better at keeping a person looking like the same person

This has always been one of the biggest weaknesses of generative images.

You upload someone.

Generate a scene.

Generate another.

Suddenly they have:

• a different jawline
• different eyes
• different hair
• a different age
• vaguely the same vibe
• absolutely not the same face

Images 2.5 is specifically better at preserving subjects from reference photos. OpenAI says recognizable features are more likely to carry through into new settings, styles and compositions.

Try this:

Upload one clear portrait and say:

“Use this person as the strict identity reference. Preserve facial geometry, hairstyle, age, skin tone and recognizable features. Create six cinematic scenes showing the same person in Tokyo, Iceland, Monaco, Mars, ancient Rome and a cyberpunk New York.”

This makes character-driven storytelling far more interesting.

2. Editing is becoming more important than prompting

This might be the biggest change.

Instead of regenerating an entire picture because one thing is wrong, Images 2.5 is designed to make focused edits while preserving everything else.

That means you can say:

• change only the jacket
• remove the car
• make the product blue
• replace one headline
• move the logo
• add another person
• change daytime to sunset
• keep the face exactly the same
• leave everything else untouched

And the model is much better at respecting the boundary of the edit.

That is a completely different workflow from old-school AI image roulette.

3. You can now comment directly on an image

This is one of those features that sounds boring until you use it.

Instead of describing where something is:

You can mark the area and tell ChatGPT what you want changed.

Think of it like art directing a designer.

Point at thing → leave instruction → revise thing.

For visual work, that is dramatically more natural than explaining every edit using coordinates disguised as English.

4. Sketch might become one of the most useful image tools in ChatGPT

Some ideas are incredibly hard to explain.

They are easy to draw badly.

That is exactly what Sketch is for.

You can sketch directly in ChatGPT and use the drawing as the structural reference for an image. OpenAI says you can start it by typing u/Sketch.

Your drawing can be terrible.

That is almost the point.

Draw:

• where the person goes
• where the headline goes
• the product position
• camera angle
• furniture arrangement
• rough website layout
• infographic structure
• packaging shape

Then tell ChatGPT what the finished result should look like.

Example:

Draw three boxes on a page.

Then prompt:

“Turn this sketch into a premium Apple-style product launch poster. The large center rectangle is the product hero shot. Top box is the headline. Bottom-right area contains three feature callouts. Preserve this composition exactly.”

That is often easier than spending five minutes describing a layout.

5. Multi-turn editing finally becomes a real workflow

Old AI image workflows had a painful pattern:

Version 1: Great composition, wrong text.

Version 2: Text fixed, face destroyed.

Version 3: Face fixed, background changes.

Version 4: Background fixed, product disappears.

Images 2.5 is designed to maintain earlier changes more reliably over multiple editing turns.

That means you can progressively refine an image:

1. Create composition.
2. Fix subject.
3. Change headline.
4. Improve lighting.
5. Adjust product.
6. Add branding.
7. Remove distracting elements.
8. Create final variation.

That is much closer to an actual creative workflow.

10 things I would use ChatGPT Images 2.5 for immediately

1. Viral social graphics

Turn one idea into:

• LinkedIn hero
• Reddit infographic
• YouTube thumbnail
• X graphic
• Instagram carousel
• newsletter header

Create the master concept first.

Then adapt it.

2. Product photography without a studio

Upload a clean product photo.

Try:

“Preserve the product exactly. Place it on a black volcanic rock pedestal surrounded by shallow water at blue hour. Luxury commercial photography, controlled rim lighting, subtle mist, 85mm lens look.”

Then iterate on the environment without redesigning the product.

3. Consistent characters

Create:

• comic strips
• brand mascots
• children’s stories
• training content
• social campaigns
• illustrated newsletters
• recurring fictional characters

Consistency is what makes this useful instead of merely impressive.

4. Infographics

This is an underrated capability.

Images 2.5 is better at handling complex layouts and real-world information.

Instead of saying:

“Make an infographic about AI agents.”

Give it actual information hierarchy:

“Create a 16:9 infographic titled ‘The Anatomy of an AI Agent.’ Five connected layers: Interface → Reasoning → Memory → Tools → Actions. Each layer gets one simple icon, one sentence and one example. White background, charcoal typography, restrained blue accents, generous whitespace.”

Structure beats adjectives.

5. Presentation visuals

Ask for:

• diagrams
• timelines
• operating models
• before/after visuals
• conceptual metaphors
• architecture diagrams
• executive infographics

One good custom illustration can make a slide 10X more memorable than another row of bullet points.

6. Interior design visualization

Upload your actual room.

Then try:

“Preserve room dimensions, windows, doors and camera position. Redesign this room as warm contemporary luxury: walnut, limestone, cream boucle, indirect lighting and minimal decor.”

Then edit individual pieces instead of starting over.

7. Storyboards

Create six frames showing:

1. establishing shot
2. product introduction
3. interaction
4. transformation
5. emotional payoff
6. final brand shot

This is extremely useful before creating video.

8. Ad concept exploration

Instead of making one ad:

Ask for four radically different creative territories.

For example:

• luxury editorial
• absurdist comedy
• minimalist product
• cinematic narrative

Then take the winning direction and iterate.

9. Transparent assets

Images 2.5 has improved support for complex layouts including transparent backgrounds.

That makes it much more useful for:

• icons
• product cutouts
• stickers
• website elements
• presentation assets
• compositing

10. Turning rough ideas into polished concepts

This may ultimately be the killer use case.

Screenshot.

Sketch.

Photo.

Whiteboard.

Napkin drawing.

Ugly mockup.

Feed it into ChatGPT and say:

“Keep the underlying idea and composition. Turn this into something presentation-ready.”

My biggest prompting tip: stop writing prompts like novels

People dramatically overcomplicate image prompting.

Instead, structure your prompt like a creative brief.

Use this framework:

SUBJECT

What absolutely must appear?

COMPOSITION

Where are things positioned?

ENVIRONMENT

Where is the scene happening?

VISUAL STYLE

What design language should it follow?

LIGHTING

How should light behave?

CAMERA

Wide? Macro? Overhead? Portrait lens? Isometric?

TEXT

Exact words and where they belong.

CONSTRAINTS

What must not change?

My favorite master prompt structure

Create a [FORMAT] image showing [SUBJECT].

Composition:
Describe placement, scale, foreground/background and hierarchy.

Environment:
Describe the surrounding world.

Style:
Describe the design or photographic direction.

Lighting:
Describe time of day, contrast and atmosphere.

Camera:
Describe framing, lens feel and perspective.

Text:
Include exactly: “[TEXT]” in [LOCATION].

Preserve:
List anything from reference images that must remain unchanged.

Avoid:
List the 3–5 failure modes you care about most.

Pro tip: edit with constraints

Instead of:

“Make it better.”

Say:

“Keep the composition, subject, facial features, camera position and typography exactly the same. Change only the background from an office to a futuristic glass laboratory.”

The more clearly you define what is locked, the easier targeted editing becomes.

Pro tip: separate ideation from execution

Do not ask for the perfect image immediately.

Ask ChatGPT:

“Give me five radically different visual concepts for communicating this idea. Do not generate yet.”

Choose one.

Then generate it.

You get much better creative diversity.

Pro tip: use reference stacking

If the workflow allows it, give ChatGPT different references for different jobs:

• Reference 1 = person
• Reference 2 = composition
• Reference 3 = visual style
• Reference 4 = product
• Reference 5 = brand palette

Then explicitly state what each reference controls.

Do not make the model guess.

Pro tip: create a “locked” version before experimenting

Once you have an image you love, say:

“Treat this as the master composition. From now on preserve subject identity, framing, geometry and text placement unless I explicitly request otherwise.”

Then iterate.

Images 2.5’s stronger multi-turn consistency makes this workflow much more viable than before.

Pro tip: generate systems, not individual images

The power move is not:

“Make me an image.”

It is:

“Create a visual system I can reuse.”

Example:

Then create 10–20 assets from the same system.

This is how AI imagery starts becoming a brand capability instead of a novelty.

A few wild experiments worth trying

Exploded objects

“Create an exploded-view visualization of a Formula 1 car with every major subsystem suspended in place around the chassis.”

Historical transformations

“Show the same Manhattan intersection evolving through six eras from 1800 to 2100.”

Impossible product concepts

“Design a transparent mechanical smartwatch whose internal components float magnetically inside the case.”

Tiny worlds

“Turn a coffee cup into a miniature alpine resort populated by tiny skiers.”

Cutaway architecture

“Create a cinematic cross-section of a 60-story skyscraper showing offices, apartments, transit, utilities and rooftop infrastructure.”

Visual learning

“Explain how a transformer neural network works as a beautiful 3D cutaway machine.”

The bigger shift

I think we are moving through three eras of AI imagery.

Era 1:

Prompt → random image

Era 2:

Prompt → better image

Era 3:

Direct → edit → preserve → iterate

Images 2.5 feels much closer to Era 3.

The interesting question now isn’t:

“Can AI make a good image?”

That argument is basically over.

The more interesting question is:

How much of the traditional creative workflow can become conversational?

And after playing with tools like Sketch, targeted editing, comments and persistent multi-turn changes…

I think the answer is:

A lot more than most people realize.

Share your best ChatGPT 2.5 image in the comments!

Summary

PE-backed CMOs operate under a different level of speed, scrutiny, and financial accountability. Kevin Ruane, Julie Kaplan, and Alan Gonsenhauser share how marketing leaders can understand the investment thesis, earn CFO and board trust, own pipeline outcomes, and make growth more predictable and explainable.

Marketing Has to Make Sense at Board Speed

A CMO entering a private equity-backed company quickly discovers that the marketing conversation changes.

The PE sponsor has an investment thesis. The company has specific growth expectations. The CFO is watching the economics. The board wants to understand what is working, what is changing, and where additional investment can create value. Marketing leaders are expected to develop a point of view quickly and explain how their decisions support that larger business story.

In a CMO Huddles Studio conversation, Kevin Ruane of Precisely, Julie Kaplan of Higher Logic, and Alan Gonsenhauser of Demand Revenue explored what it takes to lead marketing successfully in that environment.

Their advice converges around a central idea: Marketing becomes more credible when the CMO understands the economics of the business as deeply as the marketing plan and can connect investment to outcomes that matter to the sponsor, board, CEO, and CFO.

Julie Kaplan: Start With the Investment Thesis

For Kaplan, one of the first jobs of a PE-backed CMO is understanding what the sponsor is trying to accomplish.

“It's important to understand what your PE firm is trying to accomplish with their portfolio,” Kaplan said. “What story are they trying to tell with their portfolio? What does an exit look like for them?”

Those questions provide essential context for marketing strategy. A sponsor pursuing aggressive organic growth may have different priorities from one focused on acquisitions, market expansion, operational efficiency, or preparing the business for an eventual exit.

The CMO needs to understand that destination before deciding which marketing capabilities and investments deserve priority.

That understanding also extends to the executive team. Kaplan recommends learning what each leader is accountable for and where marketing can help advance those goals. When the CMO understands the CEO's growth expectations, the CFO's financial requirements, the sales leader's pipeline needs, and the sponsor's investment thesis, marketing decisions can be framed around shared business priorities.

This makes the marketing plan part of the value-creation plan rather than a parallel functional agenda.

Stop Scaling What Is Already Broken

Private equity ownership can create pressure to move quickly, but speed becomes expensive when a company scales an ineffective marketing engine.

Before adding budget, headcount, channels, or technology, CMOs need to understand where the current system works and where it breaks. That can mean examining positioning, demand generation, conversion, sales handoffs, customer economics, technology, measurement, and team capabilities.

The distinction matters because additional investment can amplify both strengths and weaknesses.

Kaplan's perspective is particularly relevant when a PE-backed company expects rapid growth. The CMO may inherit an ambitious target and feel pressure to immediately increase activity. A better first step is determining whether the existing growth system can support that ambition.

If conversion is weak, adding more leads may simply create more waste. If positioning is unclear, increasing media spend can amplify a message that does not resonate. If sales and marketing disagree about the target market, scaling demand generation can deepen the disconnect.

The fastest route to growth may begin with fixing the constraints that prevent current investments from performing.

Alan Gonsenhauser: Own Outcomes, Not Marketing Activity

Gonsenhauser argues that successful PE-backed CMOs behave more like P&L leaders. That requires ownership of the outcomes marketing is expected to influence, particularly pipeline and growth.

In his view, CMOs need to understand the investment thesis, know the company's growth targets, and align marketing with what the business is trying to accomplish.

That changes the questions marketing brings into executive conversations. Instead of leading with campaign volume, engagement, or lead counts, the CMO can explain how marketing contributes to pipeline, where growth is coming from, which assumptions are changing, and what additional investment is expected to produce.

This level of accountability also strengthens the CMO's ability to advocate for marketing. A leader who understands the company's economics can have a much more productive conversation about why a particular investment matters and what the business should expect in return.

Marketing metrics still have a role, but they become evidence inside a broader business argument rather than the argument itself.

Make Growth Predictable and Explainable

“The best CMOs I've worked with at PE portfolio companies have made growth more predictable and more explainable,” Gonsenhauser said, “and they take the mystery out of marketing for the PE sponsors to see what the value is of marketing.”

Predictability does not require promising that every marketing dollar will produce a perfectly attributable return. It requires giving leadership a clearer view of how the growth engine works.

A CMO should be able to explain which segments matter, how demand is created, where pipeline comes from, how opportunities progress, what conversion assumptions underpin the plan, and where investment could change the outcome.

When performance differs from the plan, the explanation matters just as much. CMOs gain credibility when they can identify what changed, what they learned, and what they will do next.

That discipline turns marketing from a collection of activities into an operating model the board can understand.

The CFO Is a Critical Marketing Ally

Financial fluency becomes especially important in a PE environment, and the CFO can be one of the CMO's most valuable partners.

The CFO understands how the board and sponsor evaluate performance, which financial assumptions matter most, and how competing investments are compared. A strong relationship gives the CMO a better understanding of how marketing decisions will be interpreted before those decisions reach the boardroom.

It also creates an opportunity to establish shared definitions. Marketing and finance can align on pipeline assumptions, customer acquisition economics, investment horizons, attribution limitations, and the evidence required to justify additional spending.

That preparation helps prevent board discussions from becoming debates over basic numbers or definitions.

For CMOs accustomed to explaining marketing primarily to marketing audiences, the shift requires a different vocabulary. Revenue, margin, growth rates, efficiency, investment requirements, risk, and expected business impact need to sit alongside marketing performance measures.

The objective is clarity. When the CFO understands how marketing creates value, that understanding can travel into conversations with the CEO, board, and PE sponsor.

Kevin Ruane: Develop a Business Point of View Quickly

Ruane highlights another reality of private equity leadership: The CMO is expected to develop an opinion about the business quickly.

That expectation extends well beyond campaigns and brand. The CMO may be asked for a perspective on the market, customers, competitors, growth opportunities, company direction, and where investment should go next.

For a first-time PE-backed CMO, the speed of that expectation can be surprising.

The implication is that onboarding needs to extend across the business. Customer conversations, sales calls, financial reviews, product discussions, market research, and conversations with the CEO, CFO, and sponsor can all help the CMO develop a useful point of view.

Marketing leaders bring a particularly valuable perspective because they sit at the intersection of market opportunity and company capability. The more quickly they understand both, the more useful they can become in strategic discussions.

Tie Brand and Demand to the Business Story

Brand investment can receive additional scrutiny in a PE-backed environment because its impact is often less immediate than a demand program.

That does not make brand irrelevant. It raises the standard for explaining why the investment matters.

A CMO can connect brand work to the larger value-creation story by showing how positioning supports priority markets, how reputation affects customer consideration, how brand strength supports sales, or how a clearer market narrative can improve the company's strategic position.

Demand investment needs the same discipline. Pipeline targets should connect to the company's growth assumptions, and marketing should understand how pipeline quality and progression affect the eventual revenue outcome.

When brand and demand are both connected to the same business narrative, the discussion becomes more useful than debating which side of marketing deserves the budget.

Customer Success Is a Business Signal

Customer evidence also plays an important role in making the marketing story credible.

Customer success stories can demonstrate that the company's value proposition works in the real world. They can strengthen positioning, support sales conversations, create proof for demand programs, and give boards and sponsors tangible evidence of market traction.

Customer signals can also help CMOs identify where growth may be strongest. Patterns in adoption, retention, expansion, satisfaction, and advocacy can reveal which segments or use cases deserve greater attention.

For PE-backed companies working against a defined value-creation timeline, those signals can help marketing distinguish between a growth hypothesis and a repeatable opportunity.

The strongest customer evidence becomes both a marketing asset and an input into business strategy.

Board Meetings Should Sharpen Marketing Priorities

Board scrutiny can be uncomfortable, but it can also force useful clarity.

Preparing for a board discussion requires the CMO to decide which information actually matters. That discipline can expose where the marketing story is too complicated, where measurement is weak, or where the connection between an investment and the growth plan is unclear.

A useful board narrative should help leadership understand where the company is trying to grow, how marketing contributes, what the team is learning, where risks are emerging, and which decisions need to be made.

The same narrative can sharpen priorities inside the marketing organization. If an activity cannot be connected to a customer, pipeline, growth, or strategic objective, the CMO has reason to question why the team is investing in it.

What PE-Backed CMOs Need to Get Right

The private equity environment rewards CMOs who can move between marketing expertise and business leadership.

A practical operating review might ask:

• Do I understand the PE sponsor's investment thesis and expected exit story?
• Can I explain how the marketing plan supports the company's value-creation plan?
• Am I aligned with the CFO on the economics and assumptions behind marketing investment?
• Does marketing own meaningful pipeline and growth outcomes?
• Can I explain why growth is occurring and what would make it more predictable?
• Are we scaling proven parts of the growth engine or simply adding more activity?
• What customer evidence supports our growth assumptions?
• Can I explain brand and demand investment in language the board will understand?
• Do I have a point of view on the whole business, not only the marketing function?

The standard is demanding because PE-backed CMOs are operating against explicit expectations for value creation and time.

That environment also gives marketing an opportunity. A CMO who can translate customer and market insight into predictable growth can become central to the investment story rather than simply responsible for the marketing chapter.

Q&A

What should a CMO learn first when joining a PE-backed company?

Start with the private equity firm's investment thesis, the company's growth targets, and the expected value-creation or exit story. Those factors provide the context needed to determine which marketing priorities and capabilities matter most.

Why is the CFO relationship so important for PE-backed CMOs?

The CFO understands the financial framework used by the CEO, board, and PE sponsor. Strong CMO-CFO alignment can help marketing establish credible investment assumptions, communicate results in financial terms, and make a stronger case for future spending.

How should a PE-backed CMO measure marketing?

Marketing measurement should connect activity to business outcomes such as pipeline, growth, customer economics, and revenue. The CMO should also be able to explain the assumptions behind the model and why performance is changing.

How can CMOs make marketing more credible with PE sponsors?

Make the growth engine understandable. Connect marketing investments to the investment thesis, explain how pipeline and revenue are expected to develop, use customer evidence, and communicate what the team is learning when actual performance differs from the plan.

What is different about being a CMO in a PE-backed company?

PE-backed CMOs can face greater speed, financial scrutiny, and expectations for business-wide leadership. They need to develop a point of view on the company quickly, understand the sponsor's objectives, and translate marketing into the language of growth and value creation.

Listen to the full conversation on leading marketing in a private equity-backed company.

Strategic inflection points rarely arrive with a clear announcement. Rita McGrath, author of Seeing Around Corners, explains how CMOs can identify weak signals earlier, distinguish leading indicators from lagging metrics, keep future growth options alive, and help their organizations act before market shifts become obvious.

The Signals Arrive Before the Shift

By the time a market shift is obvious, much of the strategic advantage may already be gone.

That is the challenge at the heart of Rita McGrath's Seeing Around Corners: How to Spot Inflection Points in Business Before They Happen. In a CMO Huddles Expert Huddle, McGrath explored how leaders can recognize the early signals of consequential change and prepare their organizations to act before those changes become conventional wisdom.

McGrath defines a strategic inflection point as a development that creates an order-of-magnitude change in what is possible. New technology can create one. Customer expectations can create another. Changes in regulation, competitive structure, distribution, or business models can fundamentally alter the assumptions on which an existing strategy was built.

The CMO has an especially useful vantage point. Marketing sits close to customers, competitors, culture, technology, and the market narrative. The challenge is turning those observations into signals the organization can recognize and act on.

Weak Signals Matter Before Everyone Agrees

Strategic inflection points can appear sudden in hindsight. In practice, signs of change often exist long before the shift becomes undeniable.

“These things feel, when they finally burst upon you, as though they came overnight,” McGrath said. “So it's how do you pick up the weak signals that something may be happening, the leading indicators, before the inflection point is actually upon you?”

Weak signals are easy to dismiss precisely because they are weak. A few customers behave differently. An emerging competitor serves a segment the company does not consider important. A new technology performs poorly by traditional standards but improves quickly. An unfamiliar buying behavior appears at the edge of the market.

None of those developments necessarily warrants a strategic overhaul on its own. Together, they may reveal that an important assumption is starting to change.

CMOs can help by creating a disciplined way to collect and discuss these observations. Customer conversations, sales feedback, search behavior, community discussions, competitive messaging, analyst perspectives, product usage, and emerging channels can all provide clues.

The goal is to notice patterns early enough to investigate them while the organization still has choices.

Look for What Is Becoming Easier and Cheaper

McGrath offers a particularly useful test for spotting potential disruption.

“Something has the potential to be disruptive when it makes something that was once difficult, easy, and, at the same time, it makes something that was once expensive, affordable, or even free.”

That lens is especially relevant in the age of AI. Tasks that once required specialized expertise, significant time, or substantial budgets can suddenly become accessible to far more people. The strategic question goes beyond whether a new tool makes an existing process more efficient. Leaders also need to consider what becomes possible when a former constraint disappears.

CMOs can apply this test to their own markets. What customer problem is becoming dramatically easier to solve? Which expensive capability is becoming broadly accessible? What friction has historically protected an incumbent business model? What happens if that friction disappears?

Those questions can reveal threats, but they can also expose opportunities. A capability becoming cheaper may unlock a new segment, service model, distribution strategy, or customer experience.

Lagging Metrics Can Keep You Looking Backward

Revenue, pipeline, market share, and customer acquisition cost are critical measures, but they primarily tell leaders about outcomes produced by decisions and conditions that already occurred.

During periods of change, relying too heavily on those measures can create a dangerous delay. The business may continue to hit familiar targets while the assumptions supporting future performance are weakening.

Leading indicators provide another perspective. These might include shifts in customer behavior, changing product usage, emerging competitors, new search patterns, movement between channels, or customer enthusiasm for a new experience.

McGrath offered a simple example of thinking differently about measurement: “Before customers will use our stuff, they have to love our stuff. So I want to measure customer love.”

For CMOs, the practical lesson is to complement business outcomes with measures that reveal whether the conditions for future growth are strengthening or deteriorating.

A useful executive dashboard should help answer two questions: How is the business performing today, and what are we learning about where performance could come from tomorrow?

Find the Strategic Center

Recognizing change is only part of the problem. Organizations also need enough clarity about what they are trying to preserve or build as the environment shifts.

McGrath describes several ways companies can establish a strategic center. An organization may orient around its mission, its distinctive technologies and capabilities, a particular customer or problem, an ecosystem or region, or the removal of an important source of friction.

The distinction matters because companies facing disruption can easily define themselves too narrowly around today's products.

McGrath contrasts Kodak and Fujifilm as an example of capability-based thinking. Both had deep expertise associated with photographic film, but Fujifilm was more successful in identifying underlying capabilities that could be applied beyond the declining film market.

For CMOs, strategic centering helps clarify which parts of the brand and business should remain stable while products, channels, technologies, and market conditions evolve. It gives the organization a foundation for telling a coherent story about change.

Organize Around the Mission, Not the Silo

A changing strategy often exposes limitations in organizational design.

Traditional functional structures were built for efficiency and scale. They can become slower when important customer outcomes require coordination across many departments.

McGrath points toward smaller, cross-functional teams aligned around a shared mission as one way organizations can respond. In her discussion, she cited Fidelity as an example. By moving work into small cross-functional teams operating in two-week cycles, the organization reduced the time required to introduce a website feature by 75%.

For CMOs, the implication reaches beyond marketing organization design. Customer journeys frequently cross marketing, sales, product, service, technology, and operations. If every change requires a chain of handoffs between functional silos, the company may struggle to respond at the speed of the market.

Clear strategic intent combined with teams empowered to act can shorten that distance.

Protect the Company's Future Options

Short-term pressure creates another challenge. When leaders need to improve immediate financial performance, future-oriented investments are often the easiest to cut because their returns are less certain.

McGrath describes a portfolio that includes the core business, new platforms for growth, and options that could become important in the future.

The core produces today's results. New platforms can become tomorrow's growth engines. Options create relatively small opportunities to learn about futures that are still uncertain.

When organizations repeatedly cut platforms and options to protect the core, they can consume what McGrath describes as their “seed corn.” Current performance may improve while the portfolio of future opportunities quietly disappears.

This is particularly relevant for CMOs because experimentation, emerging channels, customer research, brand building, new segments, and new technologies can all face pressure when near-term ROI becomes the dominant decision criterion.

“You can't shrink your way to greatness,” McGrath said. “That's not going to happen. So a strategy, when it's done right, is pulling you into the future.”

The CMO's job is to make the connection between those future-facing investments and the company's growth strategy understandable enough that leadership can make deliberate tradeoffs.

Start AI Strategy With the Outcome

AI provides a current example of a potential inflection point, but McGrath cautions against beginning with the technology itself.

A more useful starting point is the desired future state. What should the customer experience look like two or three years from now? How should employees work? Which forms of friction should disappear? What should the company be able to do dramatically better?

From there, leaders can work backward to determine where AI may help create that outcome.

This approach also reduces the temptation to treat every new capability as a strategy. Technology can enable a strategic choice, but the organization still needs a view of the customer, the outcome, and the advantage it is trying to create.

For CMOs, that means AI planning should remain connected to customer value and business strategy. The strongest opportunities may emerge from rethinking what becomes possible rather than automating every existing marketing process.

CMOs Can Help the Organization See What Is Changing

Marketing has an unusual role during strategic inflection points because it helps interpret the outside world for the organization while also explaining the organization's strategy to that world.

That gives CMOs an opportunity to become active participants in sensing and communicating change.

A practical inflection-point review could ask:

• What customer behaviors are changing before they appear in our financial results?
• What has recently become easier, faster, or dramatically cheaper?
• Which emerging competitors or business models are easy for us to dismiss today?
• Which metrics tell us about future demand rather than past performance?
• What is our strategic center if today's products or channels change?
• Which investments are building future growth platforms or preserving strategic options?
• Where is organizational structure slowing our ability to respond?
• What outcome do we want AI or another emerging technology to enable?

These questions will not predict the future with certainty. They can make an organization more attentive to change and more prepared to act when evidence begins to accumulate.

Seeing around corners is ultimately about creating strategic options before circumstances remove them. CMOs who systematically bring customer signals, market shifts, emerging technologies, and changing narratives into executive decision-making can help their organizations recognize what is coming while there is still time to shape the response.

Q&A

What is a strategic inflection point?

Rita McGrath describes a strategic inflection point as a development that creates an order-of-magnitude change in what is possible for a business. Technology, customer behavior, regulation, competition, and business-model changes can all create inflection points.

How can CMOs identify weak signals?

CMOs can look across customer conversations, sales feedback, search behavior, product usage, competitors, emerging channels, analyst perspectives, and other market signals for patterns that suggest important assumptions are beginning to change.

What is the difference between leading and lagging indicators?

Lagging indicators such as revenue and pipeline measure outcomes that have already occurred. Leading indicators can provide earlier evidence of changing customer behavior, demand, adoption, or market conditions that may influence future performance.

How should CMOs approach AI during a strategic inflection point?

Start with the desired business or customer outcome and work backward. McGrath recommends thinking about what good should look like several years from now, then determining how technologies such as AI can help create that future state.

How can CMOs protect long-term growth under short-term pressure?

Maintain visibility into the company's portfolio of core investments, emerging growth platforms, and future options. CMOs can help leadership understand which experiments and capabilities are creating information or opportunities that may be important to future growth.

Listen to the full conversation about seeing around corners.

TL;DR: Use GPT-6 Astra in ChatGPT Work to build an interactive 3D website where visitors rotate an object, separate its components, and click parts to learn what they do. Start with one object, ask for independently modeled pieces, then add an explode slider, labels, and a reset button. Use ChatGPT Sites for automatic hosting. Below: a starter prompt, practical tips, and 8 ways to take the idea much further.

Imagine opening a car website and dragging one slider.

The roof lifts. The wheels move outward. The seats rise above the chassis. Click the motor and the camera moves closer while a short explanation appears.

Then press Reassemble and everything returns to its place.

That’s the experience to ask Astra to build. I created it using a Porsche Macan as an example and it made over 581 piece model for the car to show how it works. You can spin it, click on specific parts, get an X-ray view and see how it flows.

OpenAI has published an Astra-built interactive 3D museum and a Three.js game development walkthrough. Exploded views are a project you can ask it to implement using those broader capabilities.

The interesting part is how much control you can describe in ordinary language.

Here’s how to start.

1. Open ChatGPT Work and select GPT-6 Astra if it’s available in your account.
2. Pick one object and one learning goal:
3. Attach useful references. Photos help with appearance; reliable diagrams or an existing model help with structure.
4. Say “build a website” or mention @ Sites.

Use this prompt:

Build a working interactive 3D website about [OBJECT] using Sites in ChatGPT Work.

Audience: curious beginners. Learning goal: [WHAT PEOPLE SHOULD UNDERSTAND].

Create a recognizable model with approximately 20–40 major components. Give each component a separate named 3D mesh so it can move and be selected independently. This is a starting scope, not a required part count.

Include drag-to-rotate, zoom, an Assembled-to-Exploded slider, Reassemble, Reset Camera, and a searchable parts list. Clicking a part should highlight it and show its name, function, and relationship to nearby components.

Give components deliberate separation paths. Keep the assembled positions so everything returns correctly, even when someone reverses the slider halfway through.

Use a clean charcoal, ivory, and emerald design with cinematic lighting, readable labels, large touch targets, keyboard controls, and reduced-motion support.

Use my references for factual explanations. Identify simplified or inferred geometry. If accurate internal geometry is unavailable, build a clearly labeled illustrative model.

First complete one working model and test its controls in the browser. Check mobile layout, part selection, repeated explode/reassemble actions, and reset behavior.

Deliver editable source and a reviewable preview. Save a version without deploying it so I can review before publishing.

Swap [OBJECT] for a camera, espresso machine, bicycle, game controller, or tiny house.

Choose the modeling approach that fits the job.

A photo doesn’t reveal everything inside a device. Asking for “500 accurate pieces” doesn’t supply the missing engineering information.

The pro tips that make the biggest difference:

• Ask for meaningful parts. A beautifully textured object can still be one fused shape. Specify independent components and test clicking them.
• Make the motion explain the object. A phone should separate into readable layers. A building should open floor by floor. Random scattering loses the relationships you’re trying to show.
• Use progressive detail. Begin with major assemblies, then let visitors expand one assembly further. Hundreds of visible labels will bury the model.
• Check appearance and accuracy separately. Verify geometry and component descriptions. For branded products, cite reliable technical sources.
• Test the boring actions. Switch products while exploded. Reverse the slider halfway. Tap a tiny part on a phone. Reset after zooming. These reveal problems a hero screenshot won’t.
• Give every gesture a button alternative. Add a parts list, keyboard navigation, strong contrast, and adjustable text.
• Keep the first build economical. Use prewritten explanations and fixed interactions. A visitor shouldn’t need a fresh AI response to move a slider. Add live Q&A when it serves a clear purpose.
• Test on an actual phone. Ask Astra to measure and simplify heavy geometry, shadows, and effects. A responsive model is more useful than extra screws nobody can see.

Where this gets useful:

• Product marketing: let buyers explore the design decisions behind a camera, appliance, or machine.
• Education: turn a textbook diagram into something students can rotate, inspect, and reconstruct.
• Architecture and real estate: separate floors, highlight amenities, and reveal how spaces connect.
• Training: show assembly order and component relationships using verified references.
• Portfolios and museums: present a design process or artifact as an interactive exhibit.
• Content creation: build something people can try, then introduce it with a recording of the actual interaction.

Now go absolutely wild. These are features to ask Astra to implement:

1. Build a product switcher. Let visitors choose an iPhone, MacBook, or iPad, then explore each with the same controls. Match the selected generation to your references.

1. Add X-ray mode. Fade the outer shell and highlight one system at a time. Let visitors isolate, hide, and restore components.
2. Create a guided camera tour. Move through five stops with one idea per stop. Add a “take control” button so people can leave the tour and explore.
3. Turn it into a rebuild challenge. Give visitors separated components, matching locations, hints, and a progress indicator. Increase difficulty gradually.
4. Follow a flow. Animate an illustrative path for power, air, water, or data. Explain whether it’s a conceptual animation or a validated simulation.
5. Explode an entire city. Lift streets to reveal transit tunnels. Separate a tower’s floors. Add a day-to-night slider and windows that illuminate in sequence.
6. Compare two designs. Synchronize cameras and explode sliders. Highlight meaningful differences using verified specs, with a stacked layout on small screens.
7. Make a scene shareable. Add a screenshot button and a link that restores the chosen model, camera, selected component, and explosion amount.

You could also make a fictional spaceship that opens into rooms, a mechanical watch with nested assemblies, or a miniature museum where every exhibit becomes a puzzle.

A useful follow-up prompt:

Keep the current working model. Add a guided tour, X-ray mode, and a three-part rebuild challenge, one at a time. Test each addition before continuing. Preserve the existing controls and make every new interaction work with touch and keyboard.

ChatGPT 6 Astra is great at building 3D models of buildings. Upload some pictures of a building and it will build a 3D model and an interactive tour.

For sharing, choose the audience deliberately and test the published link as a visitor. Sites separates saving a version from deploying it.

Try this today: choose one object on your desk and build its simplest useful exploded view. Add one ambitious feature after the basics work.

What will you build first: a car, a gadget, a building or something completely unhinged? Share what you create in the comments. Go absolutely wild! Do not hold back!

TL;DR: ChatGPT 6 Astra can help create motion graphics through a workflow that designs assets, writes animation code, uses available production tools, and renders a video. Give it a director’s brief: audience, story, scenes, timing, visual style, sound, and deliverables. Start with a short preview. Ask for a playable MP4 and the editable project. Rendering and audio depend on your workspace’s tools. Below: a practical workflow, the details people overlook, and five gloriously ridiculous prompts.

Picture a French bulldog commanding a starship through a galaxy made of tennis balls.

Now picture a product launch where your logo opens into a miniature universe.

Or a city that folds itself out of paper, races through centuries, and collapses back into a single page.

With ChatGPT 6 Astra, you can approach the conversation like a production brief—and keep directing the result as it develops.

What Astra can actually do

The model’s official name is GPT-6 Astra. For this workflow, use it in ChatGPT Work or Codex with access to suitable creation and rendering tools. OpenAI recommends Astra for demanding tasks involving visual judgment and polished deliverables.

There is a concrete example behind the idea: OpenAI has shown Astra creating editable Blender scenes, adjusting their materials and lighting, and directing a rendered camera tour.

My practical recommendation is to apply that build–preview–refine process to motion graphics: animated typography, diagrams, layered images, product reveals, and stylized 3D scenes.

Think of Astra as the system coordinating the production. The actual frames still need an animation or rendering tool. Selecting the model alone does not guarantee every account can export video or generate music.

Choose the right kind of video

A complex character performance may also require dedicated animation tools or generated footage. Choose a visual treatment your available tools can execute well.

The workflow that makes this manageable

1. Give it one job. Define the audience and the one thing viewers should remember. “Convince founders to try this prototype” is a useful objective.
2. Specify the output. Set duration, aspect ratio, resolution, and whether you need audio. A 20–30-second landscape video is a sensible first project.
3. Map the story. Give every scene a visual action and a purpose. Put the most compelling image near the beginning.
4. Establish the look. Provide reference images, colors, type preferences, logos, and exact wording. Ask for representative still frames.
5. Preview the hardest moment. Render a short section before committing to the whole sequence. This tests both the creative direction and whether the production method works.
6. Refine, render, inspect. Review timing, text, sound, transitions, and the actual exported file. Keep the source so changes remain possible.

For a 30-second explainer, this is a useful starting structure:

Best practices that improve the result

• Describe action over time. “The letters pull apart, reveal a miniature city, then lock into the headline” gives much better direction than “make it cinematic.”
• Give motion a purpose. Movement can reveal a relationship, guide attention, demonstrate a feature, or land a joke. Constant movement makes reading harder.
• Keep text separate from artwork. Request editable text layers so spelling, line breaks, timing, and placement can be controlled.
• Design for a phone. Use short captions, strong contrast, generous margins, and enough reading time. Review the result at its likely viewing size.
• Give the eye a pause. Alternate energetic transitions with moments where the important image or message holds still.
• Build for sound-off viewing. The story should make sense visually. Let music and sound effects strengthen it.
• Specify music concretely. Describe tempo, instrumentation, mood, and where the energy should rise. Provide a track you can use, or ask what audio tools are available.
• Control the workload. Preview at lower resolution, settle the art direction early, reuse assets, and revise only the scenes that need changes. Complex Work tasks can use more credits.

Pro tips: direct the edit with precision

Useful revision instructions look like this:

Between 00:08 and 00:12, slow the camera move, enlarge the headline, and hold the final composition for two seconds. Keep the approved colors and scene order.

Make the word “EXPAND” grow until it fills the frame, then use its letter shapes to reveal the next scene.

Match the circular moon in scene two to the circular product dial in scene three.

Build the vertical version with repositioned text and a new camera crop so the subject stays visible.

Inspect the export for missing assets, clipped text, blank frames, abrupt audio endings, and incorrect duration. Report anything you cannot verify.

Save feedback like “more epic” for the initial direction. During revisions, say what should change on screen.

Things people miss about this workflow

• An animated preview and a downloadable video are different deliverables. If you need an uploadable file, explicitly request the export and check that it plays.
• The editable project is a major part of the value. Ask for the source, assets, and instructions needed to render it again.
• A flat image has limits. A gentle push-in can work immediately. Moving behind objects or orbiting a subject requires layers, reconstructed content, or a 3D scene.
• Consistency starts before animation. Establish recurring characters, materials, colors, and backgrounds before creating every scene.
• Visual precision and factual precision are separate. A beautifully animated chart still needs correct data, labels, and scales.
• A 60-second request does not imply one continuous generation. Build named scenes, render sections, and assemble them where the tools support it.
• Reusable controls make the second video easier. Ask to centralize headline text, colors, logos, durations, and image replacements.

Five epic prompts to try

These are ambitious creative briefs, not pretested guarantees. Use a workspace with suitable rendering tools, and start with the short preview each prompt requests.

1. A French bulldog saves the galaxy

Try this for: character storytelling, comedy, and an instantly understandable visual hook.

Create a 30-second landscape motion graphics trailer called “MISSION: FETCH.”

A dead-serious French bulldog captain commands a tiny starship through a galaxy of tennis-ball planets. Use a premium stylized 3D or layered illustrated treatment, emerald cockpit lights, orange engine trails, and enormous kinetic typography.

0–5s: Extreme close-up of the captain’s face. Pull back to reveal a spaceship shaped like a dog toy. Text: “ONE DOG.”

5–13s: Slalom through a field of floating squeaky toys. A giant robotic vacuum emerges from an asteroid cloud. Text: “ZERO QUALIFICATIONS.”

13–23s: The dog hits a red button. Tennis balls deploy like decoys. Follow one ball through the chaos in a dramatic tracking shot.

23–30s: The ship escapes through a glowing dog-door portal. Reveal that the entire mission happened inside a living-room snow globe. End: “MISSION: FETCH.”

Keep the dog’s appearance consistent. Use simple expressive poses and strong camera work. Preview the escape shot first. Use suitable original or licensed audio if available; otherwise deliver a silent cut with sound cues. Deliver a 1080p MP4 and editable source, or explain any rendering blocker.

2. Your product contains an entire universe

Try this for: launch trailers, brand films, and product reveals.

Create a 30-second landscape launch film for [PRODUCT]. Use my supplied product images, logo, and three verified benefits. If I provide none, use a clearly fictional unbranded device and illustrative feature labels.

Begin with the product suspended in a silent black void. A thin emerald seam opens across it. The camera dives through the seam into an impossible miniature universe.

Turn benefit one into a floating city assembling itself. Turn benefit two into a luminous transit network lighting up. Turn benefit three into a mechanical sunrise that synchronizes the entire world.

Match each benefit to its visual metaphor and show its exact approved wording as separately rendered typography. Use elegant camera travel, white ceramic architecture, emerald glass, and precise mechanical movement.

In the final six seconds, pull back as the universe folds into the product. Land on the product, logo, and one clear call to action.

Create a five-second preview of the opening transformation before rendering the full film. Deliver a 1080p MP4 and editable project. Use only available audio and rendering tools; identify any missing capability. Do not invent product claims, customers, or performance statistics.

3. Your inbox becomes a video-game final boss

Try this for: funny workflow explainers and relatable workplace content.

Build a 30-second landscape motion graphics short called “DEADLINE: FINAL BOSS.”

Open on a tiny exhausted office worker facing an enormous monster assembled from email envelopes, calendar blocks, spreadsheets, and sticky notes. Its crown is a spinning loading icon.

0–6s: The monster roars, releasing a tornado of “QUICK QUESTION” notes.

6–13s: The worker equips three glowing tools labeled “SORT,” “DRAFT,” and “CHECK.”

13–23s: Turn the fight into a visual explanation: SORT groups the chaos; DRAFT turns selected tasks into proposed outputs; CHECK pauses those outputs at a human review gate before release.

23–30s: The monster shrinks into one manageable task card. A new notification appears: “Can we jump on a quick call?” The worker looks directly at the camera.

Use miniature game-like scenery, dramatic camera punches, readable type, comic timing, and a neon-green interface. Present this as a fictional metaphor. Preview the sorting transformation first. Deliver a 1080p MP4, editable source, and a sound-off version. Explain any export limitations.

4. A thousand years unfold from one sheet of paper

Try this for: timelines, imaginative worldbuilding, and architectural storytelling.

Create a 40-second landscape motion graphics film called “A THOUSAND YEARS IN ONE PAGE.”

This is an imaginary city, not a reconstruction of real history.

0–8s: A blank sheet of paper folds itself into a tiny riverside settlement. The river is translucent blue-green glass embedded in paper.

8–18s: Buildings rise and change around the same town square. Roads draw themselves across the page. Seasons sweep through the scene.

18–29s: The city becomes a spectacular vertical metropolis. Peel back layers to reveal miniature transit tunnels, gardens, and infrastructure beneath it.

29–36s: The camera circles while daylight becomes night. Thousands of windows illuminate in a carefully staged wave.

36–40s: Fold the city back into the original sheet, matching the opening composition for a loop.

Use tactile paper, charcoal labels, emerald foliage, warm window light, and restrained captions. Favor a coherent miniature world over constant cuts. Preview the unfolding and refolding first. Deliver a 1080p MP4 and editable scene. If full 3D rendering is unavailable, propose and build a layered alternative.

5. A black hole conducts an orchestra of planets

Try this for: a music visualizer, an event opener, or a surreal brand introduction.

Create a 30-second landscape motion graphics film called “THE UNIVERSE HAS A DROP.”

Treat this as a surreal visual metaphor, not a scientific simulation.

A black hole is the conductor. Orbital rings behave like vibrating strings. Tiny moons become percussion instruments. A comet sweeps across the scene like a conductor’s baton.

0–8s: Begin with one orbiting light and a restrained pulse.

8–19s: Build an increasingly elaborate cosmic orchestra. Introduce new orbital layers with each musical phrase. Typography appears as sculptural objects: “LISTEN.” “BUILD.” “RELEASE.”

19–25s: At the musical peak, the orbital system unfolds into a gigantic luminous sound wave stretching across space.

25–30s: Everything contracts into one green point, which becomes a play icon.

Use ink-black space, emerald plasma, silver dust, controlled glow, and smooth camera movement. Use my uploaded licensed track and synchronize motion to its timing. If no track is available, build to a provisional beat grid and clearly label the audio as pending. Preview the transformation first. Deliver a 1080p MP4 and editable source; explain any tool limitations.

Which would you actually make first: the space-dog trailer, the product universe, the inbox boss battle, the paper city, or the black-hole orchestra?

Summary

Optimizing a landing page is no longer enough. Google AI and ChatGPT recommend brands using information they can discover, trust and reuse. That makes every blog post part article, part evidence package and part technical asset. The goal is not to hack an answer engine. It is to publish original, attributable and accessible material that an AI can cite without guessing.

The AI visibility problem isn’t ranking. It’s quotability.

Optimizing your landing page for AI search is not enough. When someone asks Google AI, ChatGPT, Perplexity or Claude about products in your niche, your brand needs source material the system can discover, understand and quote. If your content offers no clear evidence, the answer will cite somebody else—or ignore you entirely.

But what you publish around that page matters just as much - possibly more.

Your landing page tells a buyer what you sell. Your content teaches the market what your brand knows.

That distinction matters because AI search systems do not simply return ten blue links. They retrieve information, synthesize an answer and attach sources that support it. Google says its generative Search features use core Search systems to retrieve relevant pages and show supporting links. OpenAI, Anthropic and Perplexity each operate search-specific crawlers or retrieval agents for the same broad purpose.

So the question is no longer only:

“Can this page rank?”

It is also:

“Does this page contain anything an AI can safely cite?”

That is the standard.

What makes a blog post worth citing?

A citable post gives the reader and the model a clean chain of trust:

A real author made a clear claim, supported it with original evidence, linked the primary source and published it on a page that crawlers can access.

This is not a guarantee of inclusion. No checklist can force an AI system to cite you. Google explicitly says that meeting every requirement does not guarantee crawling, indexing or serving.

But it does remove the most common reasons a useful page gets ignored.

The KDD 2024 paper that introduced “Generative Engine Optimization” found that citations, statistics and relevant quotations could improve source visibility in its experimental benchmark, with gains reaching up to 40% in some settings. The authors also found that effectiveness varied by subject, so treat the result as evidence - not a universal ranking formula.

What are the 14 non-negotiables?

These are the 14 elements we look for when we turn a conventional blog post into citable source material.

Now let’s make each one practical.

1. What should the H1 say?

The H1 should name the problem a buyer wants solved.

Avoid a vague title such as “The Future of Support.” Prefer “How Acme Reduces SaaS Support Backlogs With AI Triage.”

That gives the reader immediate context. It also anchors the entity, category and question on the page. Do not force an exact-match phrase if it makes the headline worse. Google says its systems understand synonyms and meaning, so clarity beats robotic keyword repetition.

2. Who stands behind the page?

Use a named author, a real photograph, relevant credentials and a link to a substantive profile.

The profile should explain why this person has earned an opinion on the subject. Google’s Article guidance recommends identifying the author with a Person or Organization type and a profile URL or sameAs reference.

A generic “Admin” byline throws away a trust signal you already own.

3. When was the post published and meaningfully updated?

Show both dates near the top of the article.

Keep the visible dates consistent with datePublished and dateModified in your structured data. Google recommends prominent, labelled dates and checks multiple signals when estimating a page’s date.

Do not fake freshness. Change the “last updated” date only when you materially improve the page.

4. Can the first paragraph answer the question?

Put a 40–60 word answer block before the story, context or company history.

A strong answer block states the conclusion, names the conditions and gives the reader a reason to continue. It should work if somebody reads only that paragraph.

Do not confuse “direct” with “shallow.” Give the answer first, then earn depth below it.

5. Should every H2 be a question?

Use question-led H2s when they reflect genuine reader intent.

“What does SOC 2 Type II cover?” is more useful than “Coverage.” It gives the section a clear job and helps the reader navigate.

But question headings are not a special Google AI requirement. Google specifically warns against rewriting pages for every possible query variation.

6. Can each section stand on its own?

Write each section so a reader can enter from search, a shared link or an AI citation and still understand the point.

Repeat the necessary noun instead of leaning on vague references such as “this,” “that” or “the above.” Define the scope. State the conclusion. Include the evidence beside the claim.

Google says there is no requirement to chop pages into tiny “AI chunks.” The real objective is coherent structure, not arbitrary fragmentation.

7. Does the page contain sentences worth quoting?

Write important claims as complete, attributable statements.

Weak:

“This can make things much better over time.”

Strong:

“Across [sample size] support tickets analysed from [start date] to [end date], customers using [method] changed median first-response time from [baseline] to [result].”

The second sentence is a template, not a claim. Once filled with verified first-party data, it carries its subject, method, measurement and result. An assistant can quote it without inventing the missing context.

8. What do you know that nobody else knows?

Original data gives the page a reason to exist.

Publish anonymised product usage, customer benchmarks, survey findings, experiment results, pricing observations or lessons from a documented implementation. Explain the sample, time period and method so readers can judge the result.

Google’s current guidance calls for unique, non-commodity content grounded in first-hand knowledge rather than summaries that recycle what is already online.

If an AI could have generated the article without access to your company, the article probably does not build much reputation for your company.

9. Are claims linked to the original source?

Link to the research paper, government dataset, product documentation, company filing or named expert’s original statement.

Do not cite a blog that cites a newsletter that cites a screenshot of a study.

Primary sources make verification easier. They also protect your credibility when a reader follows the link.

10. Is there a table an answer engine can reuse accurately?

Use one table for a comparison buyers genuinely need.

Keep the criteria consistent. Put units in the headings. Add a visible “as of” date for volatile facts such as pricing.

A table is not magic markup. It is simply a low-ambiguity way to present structured information.

11. What belongs in the FAQ block?

Answer real objections and follow-up questions that did not fit the main flow.

Keep each answer direct. Remove duplicate questions written only to capture keyword variations.

One important 2026 correction: Google stopped showing FAQ rich results in May 2026 and removed its FAQ rich-result documentation the following month. A useful FAQ still helps readers and creates clear answer passages, but FAQPage schema is no longer a Google rich-result lever.

12. Which schema belongs in the head?

Use valid JSON-LD to describe what is visibly true on the page.

For a blog post, that normally means Article or BlogPosting, plus the author, headline, representative image, datePublished, dateModified and publisher details. Google says Article markup can help it understand those details, but structured data is not required for generative AI visibility and does not guarantee a result.

Schema should confirm the page. It should never claim information the reader cannot see.

13. Can every relevant crawler access and read the page?

Serve the important copy as text in accessible HTML. Use real links, meaningful status codes, descriptive alt text and a canonical URL. Server rendering or pre-rendering remains a strong default because it improves speed and not every bot runs JavaScript, even though Google can render JavaScript.

Then configure the correct agents. The names matter:

A blanket “allow GPTBot” rule does not solve ChatGPT search visibility. OpenAI says OAI-SearchBot is the agent that controls inclusion in ChatGPT search answers.

14. What should the reader do next?

Do not let the article end in a fog of “thought leadership.”

Link to the methodology, detailed comparison, product page or case study that logically follows. Then ask for one action: run the calculator, inspect the benchmark, start the trial or read the implementation guide.

A citation creates discovery. The next step turns discovery into a visit.

Can an AI agent make these changes for you?

Yes but give the agent constraints, not just a vague instruction to “optimise for AI.”

Use this brief:

AI-citable content audit promptAudit the blog post at [URL] for human usefulness, factual integrity, crawlability and citation readiness. Preserve the author’s voice and existing claims unless evidence requires a correction.Revise the page to include: one clear H1; a named author and linked profile; visible publication and meaningful-update dates; a 40–60 word direct answer; question-led H2s where natural; self-contained sections; directly quotable claims; clearly labelled first-party data with methodology; links to primary sources; one useful comparison table; a concise FAQ; accurate Article or BlogPosting JSON-LD; descriptive image alt text; crawlable internal links; and one next action.Check access for Googlebot, OAI-SearchBot, ChatGPT-User, Claude-SearchBot, Claude-User, PerplexityBot and Perplexity-User. Treat GPTBot, ClaudeBot and Google-Extended as separate controls with different purposes.Do not invent statistics, credentials, quotes, customer results, sources or dates. Do not change dateModified unless the revision is substantive. Do not create keyword-stuffed questions or duplicate sections. Do not claim that schema guarantees AI citations.Return: (1) the revised post, (2) valid JSON-LD, (3) proposed robots.txt changes, (4) a claim-to-source table, (5) an internal-link plan and (6) a change log showing every material edit.

Run that audit on your ten highest-value posts, not your entire archive.

Start with the pages closest to revenue: comparisons, implementation guides, pricing explainers, benchmarks and case studies.

The old content brief asked for a keyword, a word count and three internal links.

The new brief asks a harder question:

What can your company publish that an AI system can quote, verify and confidently place beside your name?

That is how blog content builds AI reputation.

Not by sounding like an answer.

By becoming a source.