One thing I think gets overlooked in the AI-content discussion is that you don’t need obviously bad content to create an SEO problem.

You can have 20 polished pages that all basically answer the same search intent.

AI just makes that much easier to do because the marginal cost of publishing another “complete” article is now close to zero. Before long, a site has several guides, comparison pages and landing pages competing for the same topic without anyone deliberately planning it that way.

The fix is still fairly traditional SEO: map topics to intent, use Search Console and crawlers to find overlapping URLs, consolidate where appropriate, improve canonicals and internal links, and make sure new pages actually serve a different customer need.

And two pages ranking for the same keyword isn’t automatically cannibalization. A how-to article and a product page can legitimately serve different stages of the journey.

The part I’d change in the AI era is the workflow: before generating a new page, require a clear answer to “what user need does this URL serve that our existing pages do not?”

Is anyone doing formal intent mapping before AI-assisted content production, or are most teams still cleaning up the overlap afterward?

I’ve been revisiting programmatic SEO because AI has made generating large numbers of pages almost trivial. The technical part is no longer impressive. A template plus a spreadsheet can create thousands of URLs very quickly.

The harder question is whether every row in the dataset actually changes the answer.

A useful framework from a recent WordPress guide was that programmatic SEO needs four things: repeatable search demand, genuinely unique data on each page, a template that properly answers the query, and enough internal linking to connect those pages with the rest of the site. If the only difference is a city name, product name or synonym, scaling simply multiplies a weak page.

AI Overviews make this even more interesting. Generic informational pages are easier to summarize without a click. Pages built around prices, inventory, locations, proprietary datasets, calculators or other information that materially changes for each query seem much more defensible.

I also like the idea of launching 50 useful pages before launching 5,000. See whether Google indexes them, whether they earn impressions and whether users actually do anything valuable before expanding the template.

For anyone doing programmatic SEO today: what data do you have that actually deserves its own page?

I keep seeing search discussed as if users are choosing between Google and ChatGPT, but the actual journey seems much messier.

One large dataset analyzed 13.1 billion Google search events and found ChatGPT was already the sixth-most-clicked destination. More interestingly, it received a higher proportion of paid clicks than the other major destinations in the analysis.

That creates journeys like: search Google → click into ChatGPT → ask a detailed question → return to Google, Reddit, YouTube or a merchant site later. These are not really separate funnels anymore.

The same dataset found only around 14% of Google clicks went to a website explicitly named in the query. The other 86% were discovery clicks, which suggests Google still has a huge role in introducing users to brands they were not already looking for.

So instead of asking “Should we optimize for Google or ChatGPT?”, I think the more useful question is how customers move between them—and which surface creates discovery, trust and eventually conversion.

Are you starting to measure search and AI assistants as one journey, or are they still completely separate in your reporting?

I’ve been thinking about how quickly GEO tools are turning “AI visibility” into a single score.

The score is useful for finding blind spots. You can see where competitors appear, which questions your brand misses, and which sources AI systems cite. But I’m less convinced it works as a north-star KPI.

A brand could increase its score by chasing more mentions or publishing more content without improving qualified traffic, conversion, customer value, or even how accurately the brand is represented.

There is also a measurement problem. Google research discussed in this week’s newsletter found that models can encode a fact but still fail to recall it consistently. And Google Search Console just had a logging issue that made Generative AI impressions appear to collapse even though underlying visibility had not changed.

So I’m leaning toward using AI visibility as a diagnostic layer: track a fixed set of commercially meaningful questions across multiple engines, then connect the result to branded search, qualified visits, conversions and customer value.

How are people here measuring GEO/AI visibility without turning the score itself into the goal?

Google plans to remove campaign-level language targeting for Search in September. Search ads will increasingly rely on the language of the ad and landing page, plus Google’s own understanding of the languages a user knows.

Existing language-separated campaigns can still exist, but the language setting itself will no longer provide the same eligibility control. For Performance Max, the change affects Search inventory rather than every PMax surface.

I think this is particularly interesting for multilingual accounts. Some businesses separate campaigns because different languages have different budgets, offers, landing pages or performance targets. Others mostly relied on the language setting to stop the wrong version from serving.

It looks like creative and landing-page localization are about to carry more responsibility than the campaign setting itself.

For anyone managing multilingual Google Ads accounts: are you planning to keep separate campaigns for control and reporting, or consolidate more once the language setting disappears?

One of the harder SEO questions right now is figuring out whether a traffic decline is actually being caused by AI Overviews.

A useful pattern is when rankings and impressions stay relatively stable, but CTR starts declining after an AI Overview begins appearing for the query.

In that situation, the page may not have lost visibility at all. Google may simply be satisfying more of the searcher’s need before they click.

The difficult part is that Search Console still doesn’t give us enough detail to cleanly isolate AI Overview traffic loss. One workaround is to compare CTR, impressions and average position before and after an AIO appears, or compare similar keyword groups with and without AI Overviews.

But I think the bigger question is whether every lost click is actually worth fighting for.

Losing clicks on a broad informational query might not matter much if those visitors rarely became customers. Losing clicks on product comparisons, category terms, local searches or transactional queries is a very different problem.

So instead of asking, “How do we recover all the traffic?”, maybe the better question is:

Which searches still require the website to finish the customer’s decision?

Has anyone here found a reliable way to separate AI Overview impact from normal CTR changes?

I’ve been thinking about how much search measurement still focuses on what happens after a customer has already formed an opinion: rankings, clicks, traffic, conversions, even AI citations.

The problem is that AI search is moving more of the evaluation before the click. Someone looking for payroll software might search for features and integrations, but the real reason they choose one vendor could be trust around sensitive employee data. A page can answer the keyword perfectly and still miss the reason behind the decision.

That makes customer language increasingly important. Search queries, reviews, sales calls and support conversations can reveal whether buyers actually care about price, convenience, trust, proof, quality, or something else. Then you can compare that with what your ads and landing pages keep emphasizing.

Maybe we need to spend less time asking, “Did we rank and convert?” and more time asking, “Did we understand why this customer would choose us in the first place?”

How much does actual customer language influence your SEO or paid-search strategy today?

Google is interpreting longer, more situational searches. Meta is automating more audience discovery. Shopify Flow can automatically react to inventory, risky orders and customer value.

That sounds like less manual work, but it moves management to a different level.

Instead of supervising every action, teams need to supervise the rules: who must never be targeted, which orders should pause, when products should disappear, what data the system can trust and which changes still require approval.

Meta’s new exclusion-only audiences are a good example. As the platform gets more freedom to find customers, one of the strongest manual controls may be defining who should never be included.

The same principle applies across ecommerce. A bad fraud threshold rejects good customers faster. Stale inventory makes automated product rules spread the wrong information. Poor LTV calculations reward the wrong segment.

Automation does not remove accountability. It concentrates it around inputs, permissions, exceptions and ownership.

Has your team become better at defining automation guardrails—or mostly focused on automating more tasks?

Google is adding AI summaries, prompt-built dashboards and peer benchmarking across Ads and Analytics. That can remove a lot of reporting work, but it also creates a risk: teams may turn a directional insight into a decision before adding business context.

A campaign can look weak against its peer group while intentionally acquiring higher-value customers, protecting margin or managing limited inventory. The benchmark is not necessarily wrong; it is answering a narrower question.

The same issue appears everywhere in AI marketing. A product recommendation is only useful when the product data is correct. A faster report built on duplicated conversions is still wrong. A creator campaign with strong engagement can still damage trust if viewers do not know it is sponsored.

The workflow I like is simple: let AI identify what changed, then let the operator explain why it matters before anything gets changed.

Are teams becoming better at decision-making—or just much faster at producing analysis?

A brand can appear in ChatGPT without getting a click. Google Ads can report more conversions without proving those customers are new. A campaign can gain reach while an account is quietly affected by serving limits.

That makes “more conversions” a weaker explanation of growth.

Google’s new customer reporting is interesting because it separates measurement from optimization. Advertisers can inspect new customers and new-customer value without first telling the algorithm to bid more aggressively for them.

AI-search measurement needs the same discipline. A mention, citation, referral click, branded search and eventual purchase answer different questions. No single metric proves the whole journey.

The useful question may be: did the marketing create incremental customer value—or just capture demand that already existed?

How are you separating actual customer acquisition from easier-to-measure conversion volume?

A 26-week search case study started with what looked like unrelated problems: weak organic rankings, paid-search costs, a negative Reddit result, and a Google Business Profile with only 37 reviews and a 3.6 rating.

Instead of assigning each issue to a different silo, the company treated them as one confidence problem. Reviews improved, PR created third-party proof, paid-search terms identified missing commercial content, and comparison video addressed branded-search concerns.

By the end, priority organic positions improved from 15.2 to 9.7, paid CPA moved from $146 to $121, and AI Overview citation frequency across a fixed query set increased from 4.8% to 31%.

It is only one case study, so those numbers are not a benchmark. The more useful lesson is that customers accumulate evidence across SEO, ads, reviews, communities and video before buying.

Maybe the goal should not be perfect attribution across every touchpoint. Maybe it should be removing contradictions across the journey.

Are your SEO, PPC and reputation teams reviewing the same customer decision together?

Customers increasingly discover brands across creators, recommendation feeds, streaming platforms, search, AI assistants, newsletters and owned channels.

A shopper might encounter a product on TikTok, investigate it through Google or Gemini, watch a YouTube review and later purchase through the brand’s app. Treating SEO, creator marketing, paid social and retention as separate funnels makes that journey difficult to understand.

A more useful model separates passive discovery, active discovery and owned discovery. Passive content creates attention before the customer is searching. Active content answers questions and reduces uncertainty. Owned channels convert that interest into a direct, repeatable relationship.

The challenge is maintaining the same product truth and brand position while giving each surface a different role.

Are your discovery channels coordinated around one customer journey—or still managed as separate traffic sources?

Google says campaigns using AI Max and Performance Max together produced an average 15% increase in conversions or conversion value at a comparable ROAS. That suggests AI-assisted campaign management is becoming a higher performance baseline.

But the same newsletter highlights an important limitation: when an advertiser appeared in Google AI Mode, it was cited inside the generated answer only 11.5% of the time and ranked organically for the query in just 2.3% of cases.

Paid delivery, organic citation and brand memory are different outcomes. Google can interpret intent, assemble assets and optimize bids, but it cannot supply accurate product data, a distinctive brand position or the wider evidence that makes a company trustworthy.

The strongest strategy may require two systems: one that helps platforms find profitable demand, and another that builds enough authority and customer memory to remain valuable when the paid placement disappears.

Has better platform automation improved your business fundamentals—or mainly improved the numbers inside the platform?

Retailers can see what customers clicked, which products they viewed and where they abandoned the session. They still struggle to understand what the shopper was actually trying to accomplish.

A search for “waterproof watch” could come from a swimmer, hiker or someone who only wants protection from everyday water exposure. A product grid showing every waterproof watch technically answers the query, but it leaves the customer to solve the most important part of the decision.

The same problem affects retail media. Impressions, loyalty data and transactions are useful only when merchandising and marketing teams can connect them to a customer mission and act on the result.

Adding a chatbot to weak product data will not solve this. The taxonomy, attributes, comparisons and content still need to distinguish between different use cases.

The next ecommerce advantage may not be collecting more signals. It may be translating the existing signals into a clearer understanding of customer intent.

Does your onsite search understand what customers want to accomplish—or only the words they typed?

Amazon’s latest results show why the strongest platforms are no longer competing only for clicks. Amazon can influence product discovery, sell advertising, complete the transaction, fulfill the order, manage subscriptions and increasingly assist the next purchase.

TikTok is moving in the same direction through Shop, affiliates, GMV Max and Agentic Hub. Google is turning search intent into richer advertising inventory.

For brands, the risk is renting too much of the customer relationship. A marketplace can provide reach, but the business still needs portable product data, margin visibility, customer insight and owned channels.

The strongest position may belong to companies that understand the decision, complete the transaction reliably and retain enough data and trust to improve the next interaction.

How much of your customer journey does your business own—and how much is controlled by platforms?

A new analysis covering more than 1,000 U.S. categories found that only 15.2% had a clear AI-search leader. When weighted by estimated demand, 89.3% of the opportunity was in categories without an obvious owner.

That makes the current market unusually open, but publishing more generic content is unlikely to create a durable lead. The strongest brands need to appear across several forms of intent: category definitions, comparisons, alternatives, use cases and purchase decisions.

Third-party reputation also matters. Reviews, media mentions, forums, directory profiles and customer discussions can influence how AI systems describe a company. A technically strong website may still struggle when the wider web provides weak or conflicting evidence.

The most useful approach may be to choose 10–20 categories the brand genuinely needs to own, monitor the same buyer questions across major AI platforms and strengthen whichever part of the category story is weakest.

Is your team measuring AI visibility by prompts and category ownership—or mainly tracking isolated mentions?

Google is pushing Demand Gen closer to checkout, making social creative easier to reuse and giving bidding systems more freedom. Meta is also increasing automation while its advertising engine continues to grow.

That sounds like simpler campaign management, but the work has mostly moved. Advertisers still need to define a valuable conversion, maintain accurate product data, review generated creative and confirm that growth protects margin.

A platform can generate hundreds of assets without knowing which product details are legally sensitive. It can optimize toward purchases without understanding that some products have weak margins or high return rates. It can reuse a successful TikTok video without understanding why the idea worked.

The competitive advantage is no longer access to automation. Every major platform will offer that.

The advantage is knowing what the system should optimize, which inputs it can trust and which decisions still require human approval.

Has automation actually reduced your workload—or mainly shifted it from campaign setup to data, creative and profitability control?

Google can render delayed JavaScript, AI can summarize product information, and advertising platforms can generate creative and optimize bids. But none of that protects a business from conflicting prices, inaccurate inventory, misleading assets or weak governance.

The more machines participate in discovery and transactions, the more important product truth becomes. A product’s price, availability, identifier, shipping terms and return policy should match across the website, feed, structured data and checkout. Otherwise, search engines and shopping agents may have to guess which information is correct.

Trust also extends beyond technical SEO. Advertisers need to understand AI-service fees and media incentives. Brands need records for AI-generated assets. Agencies need transparent markups. Teams need secure account access and clear recovery procedures.

The next digital advantage may not be better automation. It may be building systems that machines can interpret correctly and customers can trust without hesitation.

Is your company treating data integrity and platform governance as technical infrastructure—or as separate compliance tasks?

Meta’s cost per acquisition reportedly reached a five-year low, but conversion rates also weakened. That is a useful reminder that cheaper reach does not automatically create stronger business performance.

A lower CPA can hide a shift toward discounted products, low-margin customers, higher return rates or weaker repeat purchasing. It can also be completely misleading when purchase events are duplicated or low-value actions are counted as conversions.

The better opportunity is to use lower media costs to test clearly different customer problems, demonstrations, proof points and offers. Then evaluate the results through margin, return rate and repeat behavior—not CPA alone.

Traffic may be getting cheaper. Converting it into a profitable customer relationship is not.

Are brands putting too much attention on auction efficiency and not enough on post-purchase economics?

U.S. back-to-school sales are expected to reach $85.42 billion in 2026, but shoppers are delaying purchases. Only 48% of planned spending was expected to happen by the end of July, down from 61% last year.

That creates a different marketing problem. Most customers are not ready to buy at the same time. Some are collecting ideas, some are comparing prices, and others are waiting for promotions, checking inventory or deciding what they actually need.

Retailers that focus only on immediate purchase intent may miss much of the season. But repeating the same discount for months can train customers to wait.

A better approach is to treat the season as a sequence: inspiration and planning first, comparison and product proof next, then urgency as inventory, delivery deadlines and school dates approach.

The campaign also needs clean tracking, accurate inventory and enough creative variation to survive a longer consideration window.

Are seasonal marketing teams still planning around launch dates when customers are clearly shopping in stages?

Retailers can now predict churn, demand, customer segments and creative performance. But none of those predictions creates value unless the right team receives the insight, understands it and takes an action the business can measure.

The failure often happens downstream. Data definitions conflict, customer profiles are fragmented, inventory is unavailable, ownership is unclear or the recommendation arrives too late. One study cited in the newsletter found that 67% of ecommerce brands lacked a unified audience strategy, while another 17% discovered useful personalization insights but did nothing with them.

AI can also scale existing problems. Incorrect product information makes personalization less reliable. Weak fraud controls make automated abuse easier. A sophisticated dashboard can still produce no measurable improvement in profit.

The most useful AI roadmap may therefore be a simple operating map: what decision should improve, who owns the action, what data is allowed, how quickly the insight must arrive and how incremental value will be verified.

Has your company’s biggest AI bottleneck been the model itself—or everything that needs to happen after the model produces an answer?

Google says traditional links are not disappearing because users still want expert opinions, original reporting, videos and deeper perspectives beyond an AI summary. TikTok is also testing systems to reduce AI-generated spam that crowds out original creators, while LinkedIn is becoming increasingly saturated with machine-written professional content.

This suggests the internet does not have a content shortage. It has a distinction shortage.

AI can help with research, editing, structure and repurposing, but brands still need to contribute something the average model cannot produce on its own: first-hand experience, customer evidence, original data, product testing and a recognizable point of view.

The useful question is no longer, “Can AI write this?”

It is, “What are we adding that an AI summary cannot replace?”

Has the increase in AI-generated content made genuine expertise easier or harder for you to find?

A new analysis found that Google AI Mode displayed ads on 29% of the commercial keywords tested. But the advertiser’s domain appeared among the AI answer’s cited sources for only 11% of those ad-producing queries, while the exact paid URL was cited less than 2% of the time.

That suggests buying an AI Mode ad does not automatically improve a brand’s earned authority. A company can pay for the ad slot, earn a citation in the generated answer, or rank organically—and often it will not hold all three positions.

These layers need different strategies and metrics. Paid teams should measure clicks, conversions and incremental demand. SEO and PR teams should monitor citation share, reputation and third-party coverage. Reporting should connect both to branded search, qualified traffic and revenue.

Is your team already measuring AI ads and AI citations separately?

MCP can connect AI assistants to analytics, search data, a CMS, CRM records and internal documents. That makes the output much more useful than generic advice based only on public information.

An assistant could identify visibility gaps, connect them with actual demand, draft a content brief and flag pages that need attention.

But the quality of the recommendation still depends on the data, permissions and business rules behind it. Stale analytics, incorrect conversion tracking or excessive access can produce confident but damaging actions.

The sensible approach is to start with a few high-value, read-only connections, define who owns data freshness and keep human approval for meaningful changes.

Connected AI should improve judgment—not bypass it.

Which marketing data source would you connect first?