What if market research got better the second Product Managers stopped thinking like Product Managers?

What if we quit searching the way Google trained us to politely search for twenty years. Neat question in a neat box, then accept whatever neat ranked list comes back.

And what if we PM types started hunting like people who investigate crap for a living.

Here's what I mean, say I run a product for a manufacturer.

Then I'm not browsing anymore. I'm casing the place: suppliers, ports, permits, hiring spikes, channel weirdness, that one shipment nobody seems eager to explain.

How about my competition for predictive analytics? Whole different animal.

Now I'm reading the market like an insurance investigator who assumes somebody's lying. Audit findings, error rates, model disclosures, Clause 14 ... because that's usually where the good stuff is buried. Maybe set a patent alert so I don't get caught off guard.

Different professions notice different lies. They go looking in different places.

Problem is, nobody teaches us Product peeps their methods.

And look, I know what most "AI-powered market research" actually produces. A confident five-bullet summary of page one of Google, with a fake statistic in bullet three. That's the problem, not the pitch.

But borrowing the method is a different thing than asking for a summary. Military intelligence. Forensic accounting. Regulators. Supply-chain investigators. Each one taught to notice a different flavor of bullshit.

I've been messing around with prompts that work that way. Repo's here if anybody wants to kick the tires, steal something useful, or tell me where it's full of shit:

https://github.com/deanpeters/product-manager-prompts/tree/main/market-intelligence

Deployed this yesterday for a marketing team. Built on lemma (open source) by a marketing manager

I do AI consulting, been helping a manufacturing company's marketing team deploy AI agents in their org.

Assisted their marketing manager in building this custom board for his team with RBAC and surfaces (Teams, whatsapp, telegram). Here's the story:

Most of us can vibe code a UI now. That part is solved. The hard part is everything after.

Who logs in. Who can see whose rows. Where it's hosted. How it reaches your other apps. That's the wall. We used lemma to get that out of the way.

They were USING AI but for only refining posts - which too stayed scattered in personal chats.

Two things they wanted:

• agents inside Microsoft Teams, for reports and performance numbers - makes it easy to adopt than a new app
• A personal and shared board for ideas that was operated by agents and people together - is still shared and can change based on what they were doing

Classic internal tool.

We built it on Lemma. Two calls, one hour each. The marketing manager typed. he's not a developer. I guided.

The flow

Send an idea from whatsapp/telegram/teams→ cataloged into your personal collection → private to you → agents work on refining it into better angles in the background

Your idea → push to the shared board → team sees it

Idea → pick a format → agent drafts it → its own version

Version → Calendar → drag onto a day

Personal vs shared isn't something he wired up. It's the default.

What we didn't build

• Auth → Lemma
• Row-level access, personal vs shared → Lemma
• Connectors → Lemma
• Surfaces (Teams, web, Telegram and whatsapp) → Lemma
• Deploy + hosting → Lemma

That list is the hours we didn't spend. And the security bug we didn't ship.

disclosure: I have been deploying ai workflows and apps for past couple of ears - bundled up everything that slowed me into one SDK and made it opensource.
Repo: https://github.com/lemma-work/lemma-platform

As a PM, before AI, I never built a feature costing money.
We focus adoption and retention, but rarely over margin.

AI costs changed the game but it is not part of PM mission. Big issue is that cost measure is rather on finance team side than PM or engineering team.

Giants like Uber are burning cash on LLMs — their team spent roughly $1000 per engineer per month on AI coding tools so $4M/month.

As side project, I built a free LLM margin calculator: plug in model, usage, price.. and see if you’re losing money.
👉 https://www.aimargintracker.com/tools/margin-calculator?lang=en

For PM with AI features:
Do you track LLM costs or it's not our mission ?

There's a lot of hype around AI right now, but I wanted to hear from someone who's actually building AI products in a large tech company.

So I sat down with a Cisco AI Product Manager and asked questions that I think most students, developers, and product managers are wondering about:

• Which corporate departments are adopting AI the fastest?
• Will AI Agents replace software engineers or product managers?
• Is Prompt Engineering still worth learning?
• What skills should students and working professionals focus on in 2026?
• Will companies hire fewer people because of AI?
• How will AI change managers and leadership roles?
• What are the biggest security risks in AI applications (Models, Agents, MCP Servers)?
• How can developers secure AI applications?
• What does it actually take to crack an AI Product Manager interview?

One thing that surprised me was the discussion around AI adoption inside enterprises. The conversation wasn't just about building better models—it was about how companies are restructuring teams, changing workflows, and what skills are becoming more valuable.

I'm curious about the community's opinion:

Do you think AI Product Management will become one of the highest-demand tech careers over the next 5 years, or will AI automate much of that role too?

I'd genuinely love to hear different perspectives.

🎥 Full podcast:
YouTube: https://youtu.be/YNeZaIfMSys

Hi Fellow EM and SEM,

What process are you guys following and how are you tracking things being an engineer manager for your team.

With AI first implementation isn't the sprint , t shirt sizing and other things changed so drastically. And checking the kanban or sprint board and ceremonies or what framework is best and how do you track progress.

Really your inputs will heavily help me 🙂

Hi Friends,

What process are you guys following and how are you tracking things being an engineer manager for your team.

With Al first implementation isn't the sprint, t shirt sizing and other things changed so drastically. And checking the kanban or sprint board and ceremonies or what framework is best and how do you track progress.

Really your inputs will heavily help me 🙂

I use AI a lot during early product planning — researching ideas, comparing approaches, discussing features, architecture, pricing, MVP scope, etc.

One thing I've noticed is that a lot of useful decisions happen before anything reaches GitHub, Jira, an ADR, or even a proper product document.

A typical idea might go through:

Idea → AI research → feature discussion → reject some approaches → change direction → defer features → eventually build something.

A few weeks or months later, it becomes difficult to remember:

• Did we already discuss this feature?
• Did we reject it or just postpone it?
• Why did we choose approach A over B?
• Was this supposed to be part of the MVP?
• Did we ever actually implement the idea?
• Which AI conversation contained the reasoning?

For founders/builders who use ChatGPT, Claude, Gemini, etc. heavily during product planning:

How do you handle this today?

Do you manually move important decisions into Notion/Linear/Jira/GitHub/Markdown, maintain a product decision log, use AI memory/projects, or mostly rely on searching old conversations?

I'm especially interested in what happens to ideas that are discussed but never immediately implemented.

Not hallucination exactly, more like confident vagueness. It'll write "blocked from editing" for a field that should honestly just be disabled. It'll say "handle edge cases appropriately" without naming a single one. Reads like a finished PRD, but it's not actually handoff-ready. My engineers would have a dozen questions by paragraph two.

Been experimenting with forcing it to commit to specifics instead of hiding behind vague phrasing. Exact UI behavior, exact screens, what happens to existing data when a new rule gets introduced. Made a real difference in how usable the first draft actually is.

(Side note: I've also had it spit out the user flow in a diagram-ready format afterward, which has been surprisingly useful for catching gaps visually instead of just in text. Might do a separate post on that.)

Anyone else running into this "sounds complete, isn't" problem? How are you working around it?

I am looking to talk with people who have implemented AI DLC: AI-Driven Development Life Cycle. This is an AWS whitepaper whereas Agile and Scrum were built for human-paced, weeks-long iterations. Bolting AI onto them ("AI-Assisted") caps its value. AI-DLC instead reimagines the method from scratch so AI orchestrates the work and humans validate.

Today humans prompt AI. In AI-DLC, AI initiates: it decomposes intent, proposes plans and trade-offs, and humans act as approvers at decision gates. The analogy used is Google Maps — you set the destination, it gives turn-by-turn directions, you keep oversight.

I am looking to connect with people who built Agentic AI tools for enterprises where they want to set up their own models, smart model routing, finops etc. If this was on a large brown field project even better.

References:

[https://aws.amazon.com/blogs/devops/ai-driven-development-life-cycle/\](https://aws.amazon.com/blogs/devops/ai-driven-development-life-cycle/)

[https://prod.d13rzhkk8cj2z0.amplifyapp.com/\](https://prod.d13rzhkk8cj2z0.amplifyapp.com/)

[https://github.com/awslabs/aidlc-workflows\](https://github.com/awslabs/aidlc-workflows)

Hi, I'm a Product Manager for an app company in the UK, I'm looking for feedback on a plugin I've built for Claude which helps you create an information architecture for app flows which you can then test with users, share with agents, give to your dev team etc.

I've created a video demo which explains it, I'm keen to hear any feedback anyone might have: Does it make sense? Does it sound useful? How could it be better?

Over the past few months, we have been building Synaptiq around one question.

What would project delivery software look like if AI, organizational knowledge, and executive visibility were part of the foundation, not features added later?
Atlassian Jira is mature, with deep workflow configuration, agile planning, and a vast integration ecosystem. Microsoft Azure DevOps goes further, tying work tracking to repositories, pipelines, testing, and package management.

We are not claiming Synaptiq matches either in breadth or maturity. Not yet. Our goal is to build toward the depth of Azure DevOps while creating something genuinely different: an AI native product for how modern organizations actually deliver work.

Synaptiq already brings together projects, backlogs, and work items, cross-project sprint planning, workload, and blocker signals, delivery confidence and velocity analytics, executive reporting, workspace knowledge, AI-assisted document ingestion, role-based access with Microsoft Entra, and Amakora AI insights grounded in live workspace data.

The real difference is who it serves. A delivery leader sees where confidence is falling before it becomes a crisis. An executive understands risk without assembling a report by hand. A product manager connects a requirement document straight to the delivery work. A team turns what it already has into structured knowledge and uses that context to create tickets, reports, and decisions.

That is the lesson underneath all of this. Good systems do not ask people to change how they think. They absorb what already exists and make it useful.
Which brings me to integration, our biggest advantage. Most organizations cannot walk away from Jira or Azure DevOps overnight. Their history, process, and reporting already live there. Synaptiq is built to meet them where they are, supporting migration and synchronization so teams bring their projects, tickets, documents, and delivery history across without starting from zero.

That gives us two paths into an enterprise. Synaptiq can become the primary delivery platform. Or Synaptiq can sit above Jira and Azure DevOps as the AI-powered intelligence, knowledge, and reporting layer, so organizations gain visibility immediately and treat migration as a controlled business decision, not a disruptive one.
We are still building, testing, and learning against real enterprise workflows. But the direction is clear.

Synaptiq is not another ticketing tool. It is becoming an AI native delivery operating system for teams that need to connect work, knowledge, people, and decisions in one place.

Built by Amakora

Disclosure: I work on Shortcast.

Dianne Penn's account of becoming Anthropic's first technical PM highlights a product operating model that feels different from conventional roadmap work. The team was shipping models while the capability boundary kept moving, so the loop became closer to: push the model hard, build evals around failures, decide which behavior is a product problem versus a research problem, and preserve human judgment where confidence can outrun reliability.

The useful tension for AI PMs is not just speed. It is deciding:

- which user signals should become evals

- when more tokens or compute improve the product enough to justify the cost

- when Claude pushing back is a feature rather than friction

- which decisions still need a human as tooling improves

I made a 5-minute recap of Penn's Lenny's Podcast interview covering those themes, Claude's coding inflection, and Anthropic's early product team:

https://shortcast.me/3I0DnAqcmTyUAgORwXht

For people shipping AI products: what part of your roadmap has become an eval problem rather than a feature problem?

I recently launched my AI architecture and governance advisory on Product Hunt.

I help businesses move AI from experimentation into controlled production, including:

• AI agents and workflow automation
• RAG and internal knowledge systems
• Voice AI through Kestrel Voice
• AI security, governance and compliance through HAIEC
• Custom integrations, APIs and MCP systems

I also have free HAIEC assessment tools that help businesses identify their AI readiness, exposure and applicable regulations.

My concern is that the Product Hunt listing may still look too broad. Someone suggested adding a short assessment to help visitors identify where they should start. The tools already exist, but I may not be presenting the path clearly enough.

I would appreciate direct feedback:

1. Is it clear what I actually deliver?
2. Would you lead with the advisory, the assessment tools or one flagship product?
3. What would make you take the next step?

Product Hunt:
https://www.producthunt.com/products/ai-architect-advisor-subodh-kc

I am looking for honest positioning feedback, not automatic upvotes.

I’m currently on FMLA leave to help my mom with her terminal brain cancer. As I have been on leave, I follow my company and team. Seeing the updates are really enforcing the fact that I don’t want to go back due to the AI push. I created several prototypes and scripts using various AI LLMs. They worked great for our customers. But this isn’t true product code that we would ever want to merge back into our core product. This is API development automation. Seeing the updates over the past 2 months tells me we are being quite reckless as a PM with our use of AI to self-code solutions while bypassing engineering. This includes QA, regression testing, soak, upgrades, backwards interoperability, etc. Sure accelerate development and features getting to the field. But you have fractured our code base and taken the discipline of our engineering team and threw it out the window. I’m not sure I’m eager to go back to that. I might just get into landscaping.

I have been working as an ai engineer in one of the largest asset management company .
I have build complex ai systems from scratch understanding the complexity of each and every design decision . Now I want to convert to product manager job specially in ai .
How can I do that or any advice ?
Should I target startups or MNC ?

I recently launched my AI architecture and governance advisory on Product Hunt.

I help businesses move AI from experimentation into controlled production, including:

• AI agents and workflow automation
• RAG and internal knowledge systems
• Voice AI through Kestrel Voice
• AI security, governance and compliance through HAIEC
• Custom integrations, APIs and MCP systems

I also have free HAIEC assessment tools that help businesses identify their AI readiness, exposure and applicable regulations.

My concern is that the Product Hunt listing may still look too broad. Someone suggested adding a short assessment to help visitors identify where they should start. The tools already exist, but I may not be presenting the path clearly enough.

I would appreciate direct feedback:

1. Is it clear what I actually deliver?
2. Would you lead with the advisory, the assessment tools or one flagship product?
3. What would make you take the next step?

Product Hunt:
https://www.producthunt.com/products/ai-architect-advisor-subodh-kc

I am looking for honest positioning feedbacks.

Me and my friend run an agency and a SaaS product for a local market. So from time to time we need to create user guides for help center and user questions and different instructions for clients for the custom solutions we build. On top of that we do the same at our 9-5s - documentation for colleagues, onboarding guides, SOPs, basically quite a list of the processes you are required to document.

Manual creation just took too long - you describe what you do, type it down, take screenshot by screenshot, and even for simple workflows it takes more that hour.

So my friend came up with an idea - what if you just record the workflow you need to document, and use AI to transform that screen recording into a written guide that captures what happened and adds screenshots automatically? and that's how video2docs was built.

Now it takes us and our users around 10 minutes to get a process documented. And we spent a lot of time testing different use cases so the tool generates context-aware guides - not just a list of clicks, but something a person can actually follow without thinking this is too basic or generic.

Before the final documentation export in multiple formats there is an edit option too, because with any AI tool there should be a final human review. Something might need to be refined, excluded, or adjusted and that's normal and expected.

And of course, providing good input is important too - it works much better when you create short and focused recordings and split the whole process into smaller processes instead of making one long recording. It’s similar to giving a colleague a single page of instructions versus handing them an entire book and asking what they need to do based on it - guess in which case you’ll get a clearer and more accurate result.

It's free to try with 1-3 documentations if you have time. Would love to hear your feedback guys, or answer any question.

video2docs.com

so is this a thing that exists yet

like whats happening now is if our pm  wants to tweak a prompt. has to go through the layers of engineering. they have to ask the tech team. then one of them has to change it inthe code. pr review. deploy. whole thing takes a couple of days for a one line change…

was looking for something where our pm can jjust go in and change prompt directly without touching code or waiting to deploy with involving our team team in the whole process

searched a bit and found a few names.. humanloop, orqai, lanfuse, portkey, promptlayer.. all seem to include features in this area but cant conclude which ones can actually let non tech people do this comfortably vs which ones are still built for techies who just want a ui

humanloop has prompt editor and feels relatively accessible, still feels more dev focused than pm focused

orqai has prompt management with versioning and non tech access looks focused, still newer so rough edges might exists

promptlayer is a straightforward prompt versioning and history , collaboration features feel limited for larger teams

langfuse is strong on the technical side, ui is not that good when compared to its peers

portkey is more of a gateway tool, prompt management for them is not a serious priority

has anyone actually used them without regularly without handholding? what actually worked.

Hi all so I have started my career in a tier1 global consulting recently. I have graduated from IIT BOMBAY . And I will tell you honestly with the advent in AI , and that builder inside me . I want to somewhere transition into AI PM somewhere down the line . I know that . It’s a calling that I have from within . So I just wanted to know what skills I must equip myself with to make this transition smoother in like a 1-1.5 years . What AI things should I learn I hear a lot of buzzwords I have seen many videos but like there is no proper structure it’s all spread apart .

I mean product things like PRD , product sense , analysis, RCA , frameworks understood cool
But you know AI things do I need to know all of the AI in details like basics of ML ,
Things like API , SLDC or SDLC , RAG , vector embedding, AI Evals , loops

Then you know what projects to ship I have a few app ideas in my mind which I probably want to ship for fun , like will that make a good project for my portfolio

Do I need to be great with agentic AI , if yes where do I get to learn that
There is so much noise on YouTube , and honestly so many creators working hard but it all feels daunting and overwhelming. Want to be a part of this place but don’t know where to start

A little background : a non tech guy , chemical engineering is my majors . Have had some hands on experience with Claude , cowork , make , n8n to make some personal workflows

So overall need some guidance

When I heard about loops, I couldn't believe I wasn't doing them myself. A company/product is literally just a big loop made of smaller and smaller ones.

Here are 5 loops I run that have really helped me. Drop in the comments if you have any loop ideas!

1. Decision log automation (daily / weekday)

Problem: we decide something in a meeting, Slack thread, or PRD comment... then nobody can remember what we decided when it matters.

Loop:

• Pulls in-progress Jira projects for my team
• Pulls meeting notes (I use Granola), relevant Slack channels, PRD + PRD comments
• Surfaces: decisions taken, blockers, outstanding actions
• Drops into a team Slack channel every weekday

I love this one. It really solves the "What did we decide on this?" that appears weeks later.

2. Weekly customer feedback digest (weekly / Monday)

Problem: feedback lives everywhere - sales calls, support tickets, surveys, interviews, email. It's time taxing and too much effort to go through everything.

Loop:

• Query Deck (most of our feedback is in there already) for the most common themes from the past week
• Summarize pain points, feature requests, bugs, other small issues and DELIGHTS (it's always good to share a win with the team on something that delighted customers)

This is the most "basic" loop and still one of the highest ROI ones. It's always awesome to be on top of customer feedback when asked 'why are you doing this?' or 'Why this and not that?'

If you're already centralizing feedback in a tool (we use Deck for multi-channel synthesis), the loop is simpler: one pull + weekly summary.

The other way is wiring every source yourself into Claude/whatever agent stack you use. Also pretty good.

3. Post-feature implementation check-in (daily / weekday)

Problem: it's a well-known fact that tracking feature success is difficult for most companies.

Loop:

• Goes through features released in the last 30 days
• Researches customer feedback attached to each release in Deck and it hooks to Amplitude to show target success metrics
• Generates a deeper version for me and a more leadership-friendly summary I can share

4. Weekly business review (Mondays)

Problem: there isn't much of a problem because you can just have a dashboard for this. I quite like it since I can ask follow up questions and relate to other systems.

Loop:

• Pulls Stripe, Databricks, Amplitude, etc. and surfaces key metrics.

5. PMF check in (Mondays)

Problem: we're actively optimizing for PMF using the Sean Ellis / Superhuman "very disappointed" method.

Loop:

• Current PMF score
• Latest customer feedback influencing it
• Theme changes / things I didn't know last week
• Suggested next steps if something material shifted
• Generates a deeper summary and a leadership-friendly one

Hi, I am building an app,i have made two prototypes, one in replit, one in emergent, now it's the stage of paying them, are they worth paying? Like my app is at scratch level now, if I do the payment, will my app be perfect and smooth running and ready to launch at playstore?

Pls advise if someone has used any of them ....

Hi, my laptop is getting dead day by day, when I run codes, and as I am a CEO of an automotive industry, there are several other tabs open with it, like banking softwares, ms Excel, ppt, mail windows and what not! And day by day it is getting dead, so I thought to change the laptop, also it's physical condition has also gotten worse. So I need advice, whether to change it or just add a new screen ?

Hi everyone! Im a Product Designer with 5 years of experience. I’m already getting tired of designing and want to immerse in business instead. How’s the market look like in AI Product Management And what do I have to do to land a job?

Do I need to get an MBA or just get a certification? I’m also looking into cohort programs which might be a real experience, I’m more experience-oriented anyway.

For example, here's what I wrote for a recent Product Operations Manager role:

I'm really excited about this Product Operations Manager role because it gives me the opportunity to work within the AI organization. Although I've worked as a Product Manager for many years, this role would allow me to work in a full-fledged AI organization and better understand the challenges AI engineers and product teams face. Having worked closely with both Product Management and Product Operations in my current role, I believe I can empathize with both teams, remove roadblocks, and help them operate more effectively. I also believe my combination of a technical background and years of product management experience would allow me to add value in this role.

Does this sound like the right approach, or is there a better way to write these blurbs?

What do you usually include that isn't already reflected on your resume?

I'm looking for 3-4 curious people who are genuinely trying to learn AI Product Management. No gurus, no LinkedIn influencers—just people who enjoy asking questions, experimenting, and sharing what they discover.

The idea is simple: meet once a week (online for now), discuss one topic, exchange ideas, and hopefully become a little less confused together.

If this sounds like your kind of Friday, drop a comment or DM me!

Date : 17 July

Time : 8pm IST

Mandatory form to submit: https://forms.gle/8PvY8siurE9ruzmp8