Paid placement is the only lever in this book that works immediately. Everything else in Model Memory and Live Retrieval compounds on a clock of weeks to years; an ad shows up the day the campaign starts and disappears the day the budget stops. That timing profile, not the formats, is what decides when paying makes sense.
Pay, build, or both
Start from your position in the organic answer, which you know from the measurement work in Measuring AI Search Visibility.
If the models already recommend you, paid is defense. The conversations most worth defending are branded ones: someone asking about your product by name is exactly the context a competitor's ad can now sit under. In ChatGPT the ad unit sits below the answer, so a strong organic answer and a rival's ad can share one screen.
If you are boxed out, paid is a bridge. AI answers concentrate hard: in OppAlerts' study across 100 industries, the top five brands held 49% of all AI answer share, roughly twice the concentration of Google organic in the same industries.From the OppAlerts ranking-factors study: 403,000 prompts, 100 industries, 13 models. Method and tables in The Research Behind This Guide, part of the AI Search Visibility research. A challenger outside that head has a long organic climb, and paid presence in the same answers is the only way to be in the conversation while memory and retrieval work accumulates.
If your product is new, the models may not know it at all. Model memory is frozen at the training cutoff, so a product launched after cutoff cannot be recommended from memory, only found through retrieval. Paid placement is the one channel where being unknown costs nothing.
These are starting positions, not exclusive camps; most programs land on a blend, and Building Your AI Search Strategy treats the allocation as the strategy decision it is.
Creative for an answer environment
An ad in an AI answer arrives after the user has been given a direct, personal response, and the matching that placed it read the conversation. Creative that restates a generic search ad wastes that context. Write to the task the conversation is already in the middle of: the useful frame is the ad as a next step the answer did not include, and the landing page should continue at the same stage of the decision, not restart at a homepage.
Formats constrain this. In ChatGPT you get a labeled unit below the response. In Perplexity you sponsor a follow-up question, and the platform writes the answer; your creative decision is which question about your brand you most want asked, knowing you do not control what comes back. The format-by-format detail is in Ads in AI Answers: The Programs and the Mechanics.
Know who you cannot reach
ChatGPT ads reach only Free and Go accounts; Plus, Pro, Business, Enterprise, and Education users see no ads.OpenAI Help Center, Ads in ChatGPT: the tier eligibility rules. OpenAI does not publish the tier mix, but if your buyers skew professional, a meaningful share of them likely pays for ChatGPT and is unreachable by these ads. On Google, the reverse caveat applies: Performance Max, Shopping, and broad-match Search campaigns are automatically eligible for AI Overviews placements, so you may already be paying for this channel without having chosen it.Search Engine Land, Google expands ads in AI Overviews, AI Mode to desktop: automatic eligibility via existing campaign types. Check that before you size a test budget; part of it may already be spent.
Budgeting and kill criteria with thin benchmarks
There are no reliable public benchmarks for AI ad performance yet. The channels are pilots, reporting is young, and the numbers that circulate are vendor case studies. Budget accordingly: a pilot sized to produce a decision, not a line item sized like paid search.
Set the kill criteria before launch. Decide the maximum cost per acquisition you will tolerate, the minimum click and conversion volume that makes the data readable, and the date you will decide, and write them down. An immature channel generates plausible reasons to keep spending; a written threshold decided in advance is the defense. ChatGPT's tooling now supports the basics of this: CPC bidding, a Conversions API, and pixel measurement.OpenAI, New ways to buy ChatGPT ads: CPC bidding and the conversion measurement tools.
Measuring paid against your own organic
The question a paid AI test must answer is incremental: did paying add visibility and conversions you would not have gotten organically? You cannot answer that without an organic baseline, which is a strong reason to have the tracking system from Building a Visibility Tracking System running before the first campaign, not after.
Then structure the test so the comparison is clean. Hold your organic tracking panel constant through the flight and watch whether organic mention and recommendation rates move; a paid flight that coincides with an organic drop is telling you something different from one that rides alongside stable organic share. Where the platforms' market-by-market rollouts allow it, geographic splits work: run the campaign in one available market, hold a comparable market out, and compare branded search and direct traffic between them using the methods in Attribution and Business Impact. Platform-reported conversions are the start of the answer, not the end of it, in a channel where much of the influence arrives without a click.
Report paid and organic AI search visibility together. One answer environment, one share-of-answers number, with paid presence marked as what it is: rented.