Etiqueta: AI & Automation

Since 31 August 2026, anyone with a Spanish company and a credit card can open OpenAI’s Ads Manager and buy ads inside ChatGPT. No sales team, no agency partner, no minimum spend. Self-service went live across 31 European markets at once — Spain, Germany, France, Italy, the Netherlands, the Nordics — six months after the US pilot started in February.

I have spent the last two weeks reading the actual documentation rather than the hot takes, and the honest summary is this: ChatGPT Ads is a real channel with real measurement plumbing, and it is also a channel where most of the people who see your ad are on the free tier, in a conversation you cannot see, being matched by a system you cannot audit. Both things are true. The question for a Spanish B2B SaaS or a D2C brand is not «is this the future», it is «what would a €1,500 test have to prove before I put it in the media plan».

This is how I would set that test up, what I would instrument first, and where I expect it to overlap with the Google Search budget you already run.

What actually went live in Spain on 31 August — and what did not

OpenAI’s timeline, from its own announcements: US test on 9 February 2026, logged-in adults on the Free and Go tiers only. Canada, Australia and New Zealand in spring. UK, Mexico, Brazil, Japan and South Korea on 11 August. Then the European announcement on 18 August, with self-serve access through Ads Manager confirmed on 31 August. On 31 August OpenAI also reported the platform had passed a $1 billion annualised run rate in under 200 days — which tells you the US advertisers who got in early did not leave.

What you get in the account today, per the developer docs and the September product email: CPM and CPC bidding plus a conversion-optimised objective; geo-targeting and custom audiences built from hashed emails, phones or mobile ad IDs; a JavaScript measurement pixel, a no-JS image tag and a server-side Conversions API; product-feed campaigns for retailers, which serve in a multi-product carousel; total-budget pacing; and — new on 4 September — an Ads Manager plugin that lets you build and edit campaigns from inside ChatGPT itself.

What you do not get: any view of the conversation your ad appeared in. Advertisers receive aggregate views and clicks, nothing else. That is a deliberate design choice, stated in every OpenAI ads document since February, and it is the single biggest difference between this and a search-terms report.

Who actually sees a ChatGPT ad, and why it changes your forecast

Ads are only shown to users on the Free and Go plans. Plus, Pro, Business, Enterprise and Education accounts never see them, and Free users can opt out of ads in exchange for fewer daily messages. Ads are not served to anyone under 18 or predicted to be, and they are not eligible near health, mental health or political topics.

Read that as a media planner and three things follow. First, the audience skews away from the people who pay for software — the CFO of a mid-market company evaluating a finance tool is more likely on a Business seat than on Free. Second, the exclusions are broad enough that entire categories (supplements with any health claim, for instance) will find inventory thin or unpredictable. Third, ad selection is driven by the topic of the current conversation, the user’s past chats and their past ad interactions. Your ad appears when someone is talking about your category, which is a stronger intent signal than a display impression and a weaker one than a typed search with commercial modifiers.

So the right mental model is not «Google Search but in ChatGPT». It is closer to an intent-matched mid-funnel placement with a free-tier skew. That is not a criticism; it is what you should be pricing when you set a target CPA for the test.

How to instrument it before you spend a euro

Every failed channel test I have seen failed at measurement, not at the channel. ChatGPT Ads gives you three tracking paths that all report to one Pixel ID, and the order you set them up in matters.

One more thing the docs are explicit about: a view-through conversion model is «coming soon», billed on impressions and optimised on both clicks and views. When it arrives, keep it out of the test. A first test needs a clean click-based read; view-through attribution is where new channels go to look better than they are.

A €1,500 test that proves something: B2B SaaS versus D2C

The two kinds of clients I run paid media for need different tests, because the channel treats them differently.

For a B2B SaaS — say a finance-automation tool selling to founders and CFOs — the honest hypothesis is not «ChatGPT will produce demos at a lower CPA than Google». It is «people who ask ChatGPT how to structure their company’s cash-flow reporting are in our ICP, and we can reach them before they type a branded search». So: one campaign, conversion-optimised toward a soft conversion (guide download or pricing page view, not demo booked), custom audience exclusion built from current customers, geo Spain plus your two best EU markets, three ads that each answer one specific question. Success at €1,500 is not a demo. It is a cost per qualified contact within 1.5× of your LinkedIn Ads number and a CRM flag you can follow for two months.

For a D2C brand — a daily nutritional beverage, a coffee subscription — the channel is closer to Shopping than to Search, and the product feed is not optional: since early September, product-level results in ChatGPT come from brands that submit a feed, and feed campaigns serve in the carousel unit with per-card impression and click reporting. The test is a feed campaign on conversion optimisation with the Pixel plus Shopify server events, a custom audience of past purchasers for exclusion, and a hard rule on claims: anything that reads as a health benefit risks being ineligible near health topics, so lead with taste, ritual and convenience. Success is a blended ROAS you would accept from a Meta prospecting campaign, on a channel where the comparison is happening in the chat instead of on your PDP.

Where it cannibalises Google Search, and where it does not

The uncomfortable part of any new channel is that it rarely brings new demand; it usually moves existing demand somewhere you now have to pay for. Two checks tell you which is happening here.

Watch branded search volume. If ChatGPT exposures push people to Google your brand, your Search campaign gets the conversion and ChatGPT looks like it did nothing. The ChatGPT-sourced CRM flag plus a look at brand impressions in the two weeks after launch will catch this. It is the same pattern we described when Smart Bidding started absorbing cross-channel signals: the platform that closes claims the credit.

Watch the non-brand long tail. Search queries have been getting longer for eighteen months, and the people typing five-word questions into Google are the same people asking ChatGPT. If your Google non-brand conversions dip in the test geo while ChatGPT-sourced contacts appear, you are paying twice for the same person. That is not a reason to stop; it is a reason to set the ChatGPT target CPA at what a Google long-tail conversion costs you, not at what a display click costs.

And a caveat on the organic side: paid presence in ChatGPT does nothing for whether ChatGPT cites you in an answer. Ads are labelled, separated, and do not influence responses. If being cited is the goal, that is a content and structure problem — the same one Google AI Mode created — and the ad budget will not buy it.

Questions I keep getting asked

Test it like a channel, not like a headline

The temptation with anything that has «ChatGPT» in the name is to either dismiss it or to announce it. Neither is a media decision. The platform has shipped the pieces a serious advertiser needs — conversion bidding, server-side measurement, audience matching, feed support, an API — faster than any ad network I can remember. It has also, by design, given you less visibility into why an ad served than any channel you currently buy.

So run it the way you would run any unproven placement: instrument first, cap the budget, define the success metric before launch, flag every contact in the CRM, and read the result against your Google long-tail and your LinkedIn or Meta prospecting numbers rather than against zero. If it earns a line in the 2027 plan on those terms, it earned it. If it does not, you have spent €1,500 to know something most of your competitors are still guessing about.

Nacho HernandezMarketing & Business Consultant · Studio IdeagoLinkedIn →

On July 7, 2026, Meta shipped Muse Image — the first image-generation model out of its Superintelligence Labs — and it’s already live in the Meta AI app and powering creative on Instagram and WhatsApp. The part that matters for anyone spending money on paid social: in the coming weeks it arrives inside advertiser tools through Advantage+ creative, where it can read a full creative brief and spin one idea into dozens of on-brand variations. So the obvious question lands fast: if Meta will generate your ad creative for free, right where you build the campaign, why are you still paying for a separate AI image stack — or a designer? Here’s the honest answer.

Meta Ads · AI Creative

What Is Meta Muse Image, and What Actually Launched?

Muse Image is Meta’s own text-to-image model, built in-house by its Superintelligence Labs rather than licensed from an outside provider. At launch on July 7 it went live in the Meta AI app and started powering image experiences across Instagram and WhatsApp, with Facebook and Messenger following. For advertisers, the headline is the next step Meta has already signalled: Muse Image is coming to advertiser and agency tools through Advantage+ creative in the coming weeks — the same automation layer that already decides how your ads are assembled and delivered.

The claimed leap over earlier ad-creative tools is comprehension. Meta says Muse Image interprets a full creative brief — the intent, the mood, the product, the constraints — instead of stitching together separate keywords. It parses that brief, adjusts individual elements, swaps styles, and produces on-brand variations with fewer round trips. The practical promise: a small advertiser with no production team can turn one idea into many finished executions without opening a design tool. That’s not a new party trick; it’s Meta moving generation inside the ad account.

What Can Muse Image Do That Your Current Process Can’t?

The real edge isn’t image quality — external models have been good enough for a while. It’s proximity and volume. When generation lives natively inside Advantage+ creative, three frictions disappear at once. You skip the export-import shuffle between an outside tool and Ads Manager. You get variations tuned to Meta’s own placements and aspect ratios by default. And Meta’s delivery system can, in principle, generate and test more permutations than any human would bother to brief.

That last point is the strategic one. Paid social has been drifting toward volume-and-signal creative for two years: give the algorithm many variants, let delivery find the winners. We wrote about exactly this shift in why five ad variants now beat one — Muse Image is Meta pushing that logic to its conclusion, where producing the twentieth variation costs essentially nothing. If your creative bottleneck has been «we can only make three versions a week,» that bottleneck is about to move.

Does Muse Image Replace Midjourney, Your Designer, or Your Agency?

No — and being clear about what it replaces is how you avoid a costly mistake. Muse Image replaces the commodity middle of creative production: the tenth background variant, the quick resize, the «same ad, five hooks» batch, the throwaway test creative you’d never have paid a designer to make. That work was already migrating to AI. Meta just made it free and moved it inside the platform.

What it doesn’t replace is the two ends of the barbell. It doesn’t replace strategy — the brief, the offer, the angle, the audience insight that makes a creative worth generating in the first place. And it doesn’t replace distinctive brand craft — the hero asset, the art direction, the campaign idea a customer actually remembers. Forbes framed the launch as Meta «competing for ad budgets,» and that’s the tell: Meta wins if you generate more creative on Meta. It has no incentive to make your brand distinctive across channels — only to keep you producing and spending inside its walls. A free model that optimises for Meta’s business is a tool, not a strategy.

There’s also a portability cost. Creative generated inside Advantage+ is tuned for Meta placements and lives in Meta’s account. Lean on it exclusively and your creative library becomes platform-shaped — harder to repurpose for Google, email, or your site. The same discipline we argued for in building a scalable content operation with AI applies here: own your creative system, use the platform’s model as one input, not the whole pipeline.

The Bottom Line: How Should You Use Muse Image?

Turn it on when it reaches your account — but use it deliberately. Treat Muse Image as a variant engine sitting under a human brief. You still write the strategy, the offer and the core angle; Muse expands that into the volume of tests Meta’s delivery wants to see. Set your Restricted Words list before the first generation, keep at least one distinctive hero asset made outside the platform, and pull your winning concepts back out so your creative library isn’t trapped in Meta’s shape.

The wider pattern is the one running through everything the platforms shipped this year: AI keeps making the doing cheap, which makes judgement the scarce asset. When anyone can generate a hundred ads for free, the advantage isn’t generation — it’s knowing which idea deserves the hundred variants, and which brand signals to protect while you scale them.

Muse Image will make bad creative faster and good creative cheaper. Which one you get still depends entirely on the brief you bring to it.

Nacho Hernández
Marketing & Business Consultant · Studio Ideago
LinkedIn →

For years the standard LinkedIn Ads playbook was to write one tight ad, put real money behind it, and pray. On July 1, 2026 LinkedIn quietly made that playbook obsolete. It shipped a full creative suite inside Campaign Manager — Brand Kit, Draft with AI, Ads Personalization, AI ad variants and Flexible Ad Creation — and paired the launch with a number that should change how you budget: campaigns running five or more ad variants see more than 20% higher click-through rates than campaigns running a single ad. If you’re still shipping one creative per campaign, you’re now leaving measurable performance on the table. Here’s what changed and how to build a LinkedIn creative-testing system around it.

LinkedIn Ads · Creative

What Did LinkedIn Just Change in Campaign Manager?

On July 1, 2026 LinkedIn rolled out a connected set of creative tools that sit directly inside Campaign Manager, so you no longer bounce between a design file, a copy doc and the ad builder. Five pieces matter for advertisers.

Brand Kit lets you store your logos, fonts and colours once and apply them across every ad, so a rushed campaign still looks on-brand. Draft with AI generates ad copy from a short prompt or your landing page, giving you first drafts instead of a blank box. AI ad variants take one approved concept and spin out multiple versions — different headlines, angles, framings — in a few clicks. Ads Personalization tailors elements of the ad to the member seeing it. And Flexible Ad Creation lets you supply a pool of assets that LinkedIn assembles and tests per placement.

LinkedIn also added Auto-targeting and Draft with AI specifically for small and medium businesses, lowering the skill floor for teams without a dedicated media buyer. Read together, the release has one obvious intent: LinkedIn wants you producing and testing more creative, because its own data says volume-of-variants is now a performance lever, not a nice-to-have.

Why Do Five Ad Variants Beat One?

The 20% CTR gap isn’t magic — it’s a function of how LinkedIn’s delivery works. When you give the auction one ad, the system has nothing to optimise between; it shows that ad to everyone in your audience whether the angle lands or not. Give it five, and the algorithm can match the right message to the right member, shifting spend toward whichever variant each segment responds to. More variants means more surface area for the system to find fit.

There’s a second, slower effect: creative fatigue. On LinkedIn’s comparatively small, high-frequency B2B audiences, a single ad burns out fast — the same decision-makers see it repeatedly and stop noticing. A rotation of five keeps the impression fresh for longer, so your CTR degrades more gently over the campaign’s life instead of falling off a cliff in week two.

The catch is that «five variants» only works if they’re genuinely different. Five near-identical headlines don’t give the auction anything to choose between. The point of the new AI tools is to make real variation — distinct hooks, formats and value propositions — cheap enough that producing five is no longer a reason to skip the test.

Variety, not volume, is the real lever:
Five variants beat one because they give the auction and the audience something to choose between. Five copies of the same idea are still, functionally, one ad.

How Do You Build a LinkedIn Creative-Testing System?

Treat the new tools as an assembly line, not a magic button. A repeatable system beats occasional bursts of inspiration.

1. Set the guardrails first. Load your Brand Kit before you touch Draft with AI. If the AI is generating copy and variants, you want brand colours, fonts and logo locked so every machine-made version still looks like you — not a generic template.

2. Test angles, not adjectives. Build your five around genuinely different ideas: one on the core outcome, one on a pain point, one on a proof point or stat, one on the objection your buyers actually raise, one on a contrarian take. That’s how you learn what your market responds to — not by A/B testing two versions of the same sentence.

3. Use AI for the first draft, a human for the final cut. Draft with AI and AI ad variants are excellent at producing volume and breaking blank-page paralysis. They’re mediocre at nuance, positioning and the specific language your niche uses. Generate ten, keep the five that a human would actually approve, and edit them so they don’t read like everyone else’s AI copy.

4. Give the test room to breathe, then read the right metric. Don’t kill a variant after a day. Let LinkedIn’s delivery distribute impressions, then judge on the metric that matches your objective — CTR for awareness and engagement, but cost-per-lead or pipeline for anything down-funnel. A high-CTR ad that produces junk leads isn’t a winner. Which is exactly why your measurement framework has to sit underneath the creative test, not beside it.

The Takeaway

Ship five genuinely different angles per campaign, let AI draft them and a human approve them, and judge each on the metric that matches your goal — not on CTR alone.

The tools removed the excuse. Producing five strong variants is now cheap; running one is now a choice to underperform.

Running LinkedIn Ads with a single creative per campaign?

I help B2B teams build a LinkedIn creative-testing system — a Brand Kit that keeps AI output on-brand, a five-angle testing framework, and the measurement to tell a real winner from a vanity-metric one.

Fix my LinkedIn creative →

The Bottom Line: What Should You Do This Week?

Pick one active campaign and turn its single ad into five distinct angles using the new tools — Brand Kit locked, Draft with AI for the first pass, your own edit for the final cut. Give it two weeks, then compare CTR and cost-per-lead against the single-ad baseline you were running. LinkedIn’s own numbers say you should see the gap; your account will tell you whether it holds for your audience.

The wider pattern is the same one running through everything the platforms shipped this year: AI is making production cheap, which moves the advantage from who can make an ad to who tests and reads results well. The same shift is changing how B2B teams capture leads in the first place. The teams that win won’t be the ones generating the most creative — they’ll be the ones with a system for turning that volume into learning.

Volume is now free. Judgement is the scarce part. Build the system that supplies it.

Turn LinkedIn’s new creative tools into pipeline

I help consultants, agencies and B2B teams set up a LinkedIn Ads creative-testing system that actually moves cost-per-lead — brand guardrails, a five-angle framework, AI-assisted production with a human final cut, and measurement that separates real winners from vanity metrics.

Let’s talk →

Nacho Hernández
Marketing & Business Consultant · Studio Ideago
LinkedIn →

Two years ago, connecting your ad accounts to anything meant a developer, an API key and a week of your life. In 2026 you can open Claude or ChatGPT and ask, in plain English, «which of my Google Ads campaigns is bleeding budget?» — and get an answer pulled live from the account. The thing making that possible is the MCP connector: a standard way for AI assistants to read from, and increasingly write to, the platforms you already run. The catch is that there are now dozens of them, they are wildly uneven, and some will happily change your live campaigns. Here’s which ones actually exist, which to trust, and how to use them without handing an AI the keys to your client accounts.

Connectors · MCP

What Is an MCP Connector, and Why Should a Marketer Care?

MCP — the Model Context Protocol — is a shared standard that lets an AI assistant talk to an external tool through a small server. Instead of every app inventing its own bespoke plugin, MCP gives Claude, ChatGPT, Gemini, Cursor and the rest one common socket. A «connector» is simply the MCP server for a specific platform: a Google Ads connector, a Meta Ads connector, a HubSpot connector.

For a marketer, the practical payoff is that the reporting and busywork layer collapses. You stop exporting CSVs and rebuilding the same pivot every Monday, and start asking questions: which ad sets dropped below a 2x ROAS last week, which HubSpot deals went quiet, which search terms are wasting spend. The AI queries the account directly and answers. The 2026 shift is that this stopped being a demo and became infrastructure — Meta adopted MCP as the primary integration method for its Ads AI Connectors, and the major platforms now ship official servers rather than leaving it to hobbyists.

Which Marketing MCP Connectors Actually Exist in 2026?

The landscape splits cleanly into two camps: official servers shipped by the platforms themselves, and third-party servers that wrap several platforms or add write actions the official ones withhold. Both have a place.

The official ones (start here). Google open-sourced its own Google Ads MCP server in early 2026 — it’s deliberately read-only, exposing account listing and GAQL queries for diagnostics and analytics. Google also ships an official GA4 server covering 200-plus dimensions and metrics, so you can interrogate traffic, conversions and audiences by conversation. And HubSpot’s remote MCP server went generally available on April 13, 2026: it gives read and write access to core CRM records — contacts, companies, deals, tickets, line items, products — plus activities like calls, emails, notes and tasks, all over an OAuth 2.1 connection that respects each user’s existing permissions. Its honest limits: no custom objects, and if your portal has sensitive-data protection on, activity objects are blocked.

The third-party ones (fill the gaps). Where the official servers stop at read-only, independents add control. Pipeboard’s Meta Ads MCP is the most mature single-platform Meta server, with full read/write for campaigns, ad sets, creatives, targeting and budgets. Unified commercial connectors like Ryze (Google Ads, Meta, GA4 with confirmation-gated writes, ~$89/mo) or Synter (14 ad platforms, from ~$199/mo) trade a subscription for one socket across your whole stack. And Markifact launched a hosted Google Ads MCP on July 13, 2026 that adds write actions to the account — but only with a human approving each change. For pure data pulls across 350-plus sources, a reporting connector like Windsor.ai remains the pragmatic choice.

The one distinction that matters most:
Read-only connectors can only tell you things. Write-enabled connectors can change things — pause a campaign, move a budget, edit a deal. That single line decides how much you should trust a given server, and how much supervision it needs.

Read or Write? How to Choose the Right Connector

Match the connector to the job, not to the hype. For reporting, diagnostics and «what happened last week» questions, a read-only official server is almost always the right call — it cannot break anything, so you can wire it up across every client account without losing sleep. This is where 80% of the day-to-day value lives, and it’s the safest place to start.

Reach for a write-enabled connector only when the workflow genuinely needs to act — bulk-pausing losing ad sets, pushing negative keywords, updating deal stages after a call. When you do, the non-negotiable feature is a human-approval step: the AI proposes the change, you confirm it, then it executes. That’s exactly the model Markifact built its July launch around, and it’s the difference between a co-pilot and an unsupervised intern with your ad budget.

Two more filters before you connect anything to a client account. First, authentication: prefer connectors that use proper OAuth and honour the permissions the user already has, like HubSpot’s official server — avoid anything asking you to paste a long-lived API key into a config file. Second, maintenance: a connector is only as good as its upkeep. A well-maintained open-source server with active commits beats an abandoned one, and a hosted commercial server beats both if you’d rather not babysit updates.

The Rule Of Thumb

Read-only by default, write only with a human in the loop. Start with the platform’s official server; add a third-party one only when you need an action the official one won’t perform.

The best connector isn’t the one with the most tools. It’s the one you can safely point at a client account and forget about.

Not sure which connectors are safe to plug into your stack?

I help teams pick the right MCP connectors for their ad and CRM accounts, wire them into a reporting and optimisation workflow, and set the guardrails so nothing changes without a human saying yes.

Map my connector stack →

The Bottom Line: Which Ones Should You Actually Use?

If you run Google Ads, Meta and a CRM, a sane 2026 starting stack looks like this: Google’s official read-only Google Ads and GA4 servers for reporting and diagnostics, HubSpot’s official server for CRM reads and the occasional supervised write, and a single write-capable ad connector — Pipeboard for Meta-heavy accounts, or a unified commercial server like Ryze or Synter if you want one socket — strictly with human approval switched on. Keep a reporting connector like Windsor.ai for the cross-channel data pulls that don’t fit any one platform.

This is the same thread running through everything platforms shipped this year: the tooling is racing ahead of the guardrails. It’s the reason AI is reshaping how you capture leads, and the same reason automated bidding keeps taking decisions out of your hands. Connectors give some of that control back — if you choose them deliberately.

Don’t connect everything because you can. Connect the few servers that earn their access, keep a human on the write actions, and let the AI do the reporting you were never going to enjoy anyway.

Build an AI-connected marketing stack that’s actually safe

I help consultants, agencies and B2B teams connect their ad platforms and CRM to AI assistants the right way — official servers first, write access gated behind human approval, and a reporting layer that finally runs itself. No abandoned open-source gambles, no keys handed to an unsupervised bot.

Let’s talk →

Nacho Hernández
Marketing & Business Consultant · Studio Ideago
LinkedIn →

For twenty years, «ranking on Google» meant one thing: earning a blue link near the top of a page of blue links. In 2026 that page barely exists. AI Mode — Google’s Gemini-powered, ChatGPT-style answer experience — stopped being an opt-in tab and quietly became the default way a growing share of people search. At the same time, a UK regulator forced Google to hand publishers an opt-out switch, and a brutal core update reshuffled who gets cited. Three moves, one direction: the search result is turning into an answer, and your job is no longer to rank on the page — it’s to be the source the answer is built from.

SEO · GEO

What Actually Changed in Google Search in 2026?

Three things happened in quick succession, and together they matter far more than any single one. First, AI Mode became the default answer experience rather than a lab experiment you had to switch on. Instead of ten links, more searches now return a synthesised answer with a handful of cited sources underneath — and most people never scroll past it. The click you used to compete for often no longer gets made.

Second, the May 2026 core update finished rolling out on June 2 after twelve days — and it hit harder than March’s. The pattern was unambiguous: sites that compile, rephrase or lightly summarise what already exists lost ground, while brands, official sources and pages with genuine first-hand data and expertise gained. Google is increasingly rewarding the thing an AI can’t generate on its own — original, verifiable substance.

Third, and most overlooked, publishers got a switch. On June 3, 2026 the UK’s Competition and Markets Authority issued a legally binding order — the first of its kind — forcing Google to let sites opt out of AI features. The result is a toggle in Google Search Console, under Settings → Search generative AI, that took effect June 17. Each property can be set to Include, Exclude or Inherit, controlling whether your content can appear in AI Overviews, AI Mode and AI Overviews in Discover — while staying fully indexed in ordinary results. It’s UK-first for now, with a global rollout promised but undated.

Should You Use the New Opt-Out Toggle?

For almost everyone, no — and it’s worth understanding why the switch is more trap than gift. On the surface it sounds empowering: pull your content out of Google’s AI answers so it can’t be «summarised for free.» The instinct is understandable. If AI Overviews answer the question without a click, why feed the machine that’s eating your traffic?

Because opting out doesn’t bring the old clicks back — it just makes you invisible in the surface that’s growing while you stay visible only in the surface that’s shrinking. AI Mode and AI Overviews are becoming where the search happens. Excluding yourself means the answer still gets written; it just gets written from your competitors’ content instead of yours. You don’t protect your authority by hiding from the place people now read — you hand it to whoever stayed.

There’s a narrow exception. If your business model is genuinely built on on-page monetisation — ad impressions, gated content, affiliate clicks that only pay when someone lands on your page — and you have data showing AI citations cannibalise rather than assist that model, the toggle is a legitimate lever to test. But for consultants, agencies, SaaS and B2B brands whose site is a credibility and lead-generation engine, being cited by name inside an AI answer is the modern equivalent of ranking first. That’s not a leak to plug. It’s the goal.

How Do You Actually Get Cited in AI Answers?

Here’s the reassuring part Google keeps repeating, and it’s true: «optimising for generative AI search is optimising for the search experience — it’s still SEO.» There is no separate GEO discipline with secret levers. What changed is the weighting. The signals that make you quotable to a language model are a sharpened version of the signals that already made you rank. Concretely:

1. Answer the question in the first two sentences. AI systems extract self-contained answers. Lead each section with a direct, standalone response to a real question, then expand. Buried conclusions don’t get quoted — front-loaded ones do. Structure pages around the questions your buyers actually type, with the answer sitting right under the heading.

2. Bring first-hand data and a point of view. The May core update was a referendum on originality. Proprietary numbers, tests you ran, a named expert with an opinion, a framework you built — these are the things a model can’t synthesise from thin air, so it cites the source. Aggregating what everyone else already said is now actively penalised, not just ignored.

3. Make the machine’s job easy. Clean structure, descriptive headings, FAQ and How-To schema, clear entity names, and factual consistency across your site all raise the odds of extraction. This isn’t about gaming anything — it’s about being unambiguous. And treat AI Mode and AI Overviews as two audiences, not one: analyses suggest only a small share of citations overlap between them, so breadth of well-structured, genuinely useful pages beats one hero article.

4. Watch the new report. Now that Search Console shows AI-surface impressions and clicks, treat it like any other channel: see which pages get pulled into answers, what they have in common, and make more of that. AI visibility stopped being unmeasurable the moment Google gave you the dashboard.

The Bottom Line: Optimise to Be Quoted, Not Just Ranked

AI Mode as default, a core update that rewards originality, and an opt-out toggle most people shouldn’t touch — read together, they describe a search engine that has stopped being a list of links and become an answer machine. The brands that win the next phase aren’t fighting that shift or hiding from it. They’re making themselves the most quotable source in their category: first-hand data, direct answers, clean structure, a real point of view.

This is the same discipline behind getting cited by answer engines generally — the argument I made about AEO and agentic AI — and it sits downstream of a bigger drift: the platforms keep making their data more ephemeral and their algorithms more opaque, the same pattern behind Google’s quiet data-retention cut. The page you used to rank on keeps losing importance. The source behind the answer keeps gaining it.

Don’t opt out of the future of search. Become the thing it’s built from.

Nacho Hernández
Marketing & Business Consultant · Studio Ideago
LinkedIn →

In November 2024, Google made a promise: eleven years of Google Ads reporting data, kept and queryable. Eighteen months later, on June 1, 2026, it quietly walked most of that back. Granular performance data — the hourly, daily and weekly numbers you actually use to diagnose a campaign — now lives for just 37 months. Google filed it under «data retention policy update,» the most sleep-inducing phrase in its vocabulary. That’s the point. A change framed as housekeeping is almost never housekeeping. Read between the lines and this is a decision about who owns the memory of your campaigns — and the default answer just stopped being you.

Google Ads · Measurement

What Actually Changed on June 1, 2026?

Here’s the plain version. Starting June 1, 2026, Google Ads splits reporting data into two buckets with very different lifespans. Granular data — anything measured at hourly, daily or weekly resolution — is retained for 37 months. Aggregated data — monthly, quarterly and annual roll-ups — keeps the eleven-year horizon Google announced back in November 2024. Reach and frequency metrics get an even shorter leash: three years, after which they vanish from both the interface and the API.

On paper it sounds tidy. In practice, the bucket that got cut is the one that matters. Nobody troubleshoots a campaign using an annual roll-up. You troubleshoot with day-level and week-level data — the exact resolution now capped at 37 months. So while Google can technically say «we still keep eleven years of data,» the data you’d reach for in a real analysis is the data that now expires first.

Why Would Google Cut From 11 Years to 3 in 18 Months?

This is the question the announcement doesn’t answer, so let’s answer it honestly. Google will cite storage cost and «simplification.» Maybe. But you don’t stand up an eleven-year retention promise in late 2024 and gut it a year and a half later because the storage bill surprised you. Something in the strategy changed, and the timing is the giveaway.

Look at what else happened in the same window. Google spent 2025 and 2026 pushing advertisers hard toward Smart Bidding, Performance Max and AI-driven automation — systems that decide where your money goes without showing you the working. The entire pitch is «trust the algorithm.» Now consider what long, granular history is for: it’s the raw material you’d use to independently audit whether that algorithm is actually delivering, to reverse-engineer why performance shifted, to calibrate your own attribution or marketing-mix models against Google’s black box.

Shorten that history and you quietly weaken every one of those checks. It’s harder to prove PMax underperformed last year if last year’s day-level data is gone. It’s harder to challenge a bidding recommendation when you can’t pull the granular baseline it’s deviating from. Less independent history means fewer ways to question the automation — which means more reliance on the automation. That’s not a conspiracy theory; it’s just the direction the incentive points.

Who Gets Hurt — and Who Won’t Even Notice?

Most advertisers running a couple of Search campaigns will feel nothing for years. If you never look past a 90-day window, a 37-month cap is invisible. That’s exactly why the change slid through with barely a ripple — the people it hurts are a minority, but it’s a consequential minority.

Notice the through-line: the losers are precisely the people trying to do rigorous, independent measurement — the ones most likely to catch an automation underperforming. The casual advertiser who just trusts the recommendations loses nothing, because they were never auditing anything. The change is regressive in a very specific way: it taxes scrutiny.

What Should You Do Before Your History Expires?

The defensive move is boring, cheap and urgent: stop letting Google be the sole custodian of your campaign history. If your only copy of granular performance data lives inside Google Ads, you’ve outsourced your own memory to a company that just proved it’ll shorten the lease whenever its strategy shifts. Here’s the practical sequence.

1. Export what’s already at risk, now. Anything older than roughly 34 months is inside the danger zone. Pull day-level campaign, ad group, keyword and search-term reports going back as far as the account allows, before the oldest slices drop off. This is a one-time rescue you can’t do retroactively — once it’s gone, it’s gone.

2. Stand up an ongoing pipe. Connect Google Ads to a warehouse — BigQuery is the native path, but a connector into any store you control works — and schedule a daily or weekly export of granular data. The goal is simple: your own copy accrues in parallel, so retention limits never touch the numbers you rely on. Yes, there’s a mild irony in the fix nudging you deeper into Google’s own BigQuery; the answer is to land it somewhere you genuinely control, in a portable format.

3. Treat this as part of your first-party data strategy, not a side chore. Owning your ad history is the same discipline as owning your customer data — the infrastructure argument I made in First-Party Data in the AI Era. The platforms are steadily making their data more ephemeral and their algorithms more opaque. The counter-move is to build a durable, independent layer you own, so your measurement and your leverage don’t depend on their retention settings.

None of this is expensive or hard. A basic export pipeline is an afternoon of setup and a few dollars a month in storage. What it buys you is independence — the ability to audit, to compare across years, and to challenge an automated recommendation with your own evidence. In a world of black boxes, that’s not a nice-to-have. It’s the whole game.

The Bottom Line: Read the Fine Print, Own the Data

Google’s 37-month cut is a small change with a big tell. It’s not the end of the world, and for most advertisers it’s not even a bad day. But it’s a clear signal of where the platforms are heading: less transparency, shorter memory, more «just trust the AI.» The updates that matter most are rarely the flashy ones with a keynote — they’re the ones filed under «policy» and released on a quiet Monday.

The advertisers who’ll thrive in this next phase aren’t the ones who fight automation — that ship has sailed. They’re the ones who keep their own receipts: their own granular history, their own baselines, their own ability to check the platform’s work. Export your data, own your measurement, and you keep the one thing the algorithm can’t optimize away — leverage.

Google shortened the lease on your campaign memory. The fix isn’t to complain. It’s to hold your own copy of the keys.

Nacho Hernández
Marketing & Business Consultant · Studio Ideago
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