Paid Media on AI Surfaces

Why this module exists

Paid media is where the gap between what is announced and what you can actually buy is widest.

Every quarter brings news of advertising formats on AI surfaces. Some are live and billable. Some are pilots in one market. Some are a slide. An agency that cannot tell the difference — or has an incentive not to — will build you a plan that spends against the third category.

This module gives you the distinction, and the harder strategic question underneath it: what performance marketing becomes when a growing share of demand is mediated by something that does not click.

10.1 The classification

Before anything is bought, classify. Four questions, all answerable in a week:

Question	If no
Can we buy it today, in our market, in our category?	It is not a plan line
Is it billable with reporting, or a managed pilot?	It is a test with a real cost
Is there any targeting or measurement control?	Budget it as brand, not performance
Can we turn it off independently?	It is a bundled placement, price it as such

The most concrete example of a genuine transition is Amazon's: conversational placements inside its shopping assistant moved to a billable CPC format in March 2026 — that is, from an experiment to a line you can plan, with the reporting and controls following behind rather than arriving with it. That sequence is typical, and it is the sequence to look for.

10.2 How paid and organic interact here

On classical search, paid and organic were separate lanes on one page. On generative surfaces the relationship is different in a way that matters for budget.

The answer is synthesised from organic-ish material. What a model says about your product is grounded largely in retrieved content — your feed, your pages, third-party sources. Paid placement can put you in front of an answer; it does not usually change what the answer says about you. If the synthesis says your returns policy is short and your sizing runs small, paid spend buys attention for that.

Which means visibility work is upstream of paid efficiency. This inverts the usual sequencing argument. On generative surfaces, Modules 3 and 4 are not an alternative to paid — they are a prerequisite for it, because they determine the content of the answer your paid impression sits beside.

Feeds are increasingly the shared substrate. The same product data drives organic answers, agent surfaces and paid formats. A feed problem is simultaneously an organic problem, an agentic problem and a media-efficiency problem — which is why Module 4 keeps reappearing.

10.3 What changes about performance marketing

Four adjustments, in decreasing order of how quickly you should make them.

Attribution windows should widen. A shopper who researches with an assistant and buys three days later breaks a short window. If your bidding optimises to a click that no longer exists in the journey, it optimises to a shrinking sample. Module 6's measurement work is the input here.

Landing experiences meet a better-informed visitor. Someone arriving from a generative answer has often already compared. Adobe's May 2026 US retail data puts AI-referred visitors at 54% more likely to convert, spending 53% longer on site and viewing 23% more pages than other traffic — a visitor who is further along, not merely a different colour of traffic. Landing pages designed to educate from zero waste that.

Note the reversal: the same series had AI-referred traffic converting worse than average in early 2025. The visitor did not change; the population did, as the surfaces moved from early adopters to the mainstream. Which is the Module 1 habit again — the number is only meaningful with its date.

Brand terms behave differently. Generative discovery pushes demand downstream into brand search. Rising brand-term volume may be an output of your visibility work rather than an independent channel — which changes both how you value it and how you argue about its incrementality.

Creative for the human, data for the machine. The split is clean. The machine matched on your attributes; the human converts on your creative. Teams that respond to "AI changes everything" by degrading creative are optimising the wrong half.

10.4 Retail media and marketplaces

For most retailers this is where AI-adjacent paid money is real today, and it is frequently owned by a team not in the conversation at all — the pattern Module 2 flagged.

Three practical points:

On-platform assistants are already influencing organic placement inside marketplaces. Your listing quality — attributes, reviews, Q&A, structured content — feeds the assistant. That is unpaid work with paid consequences, since a better-matched listing lowers the cost of the paid placement beside it.

Conversational ad formats are early and moving fast. Expect reporting and controls to lag availability. Budget accordingly: treat early spend as learning with a cap, not as performance with a target.

If you operate a retail media network, the same questions arrive from the other side. Your advertisers will ask what you can offer on your own AI surfaces, and the honest answer will likely be "nothing yet, and here is the roadmap." Saying so is better than the alternative, because they will find out.

Marlowe & Finch — the plan that removed two lines

The agency brings a proposal: four AI-surface placements, €340,000 across three quarters, described as "securing early advantage in AI search."

The classification takes an afternoon.

Placement	Buyable today, our market and category?	Billable with reporting?	Verdict
Marketplace conversational placements	Yes	Yes	Fund. €120k, with a learning cap
Sponsored answers on a general assistant	Pilot, US only, not our categories	Managed, no self-serve	Remove. Revisit at the QBR
"AI visibility package" from a vendor	N/A — this is a tool subscription, not media	N/A	Reclassify as tooling, halve it
Standard shopping campaigns with improved feed	Yes	Yes	Fund and increase. €140k

Two lines removed, one reclassified, and the fourth increased — because the improved feed from Module 4 was expected to lower cost per acquisition on placements they were already buying.

The agency's response is the useful part of the story. Asked to show a live reporting interface for the sponsored-answer line, they could not. The relationship survived, and the next proposal arrived pre-classified.

10.5 Briefing an agency

Four requirements, which together make vapour visible without an argument:

Every line classified — live, pilot or announced — with evidence for anything marked live.
Reporting interface named for each live line.
Learning budget stated separately from performance budget, with a cap and a decision date.
A pre-registered claim for each performance line: the metric, the baseline, and the result that would mean stopping.

An agency that can produce all four is worth keeping. One that cannot is charging you to find out.

Exercise — Build the plan

Time: 90 minutes. Produces the artefact for this module.

List every AI-adjacent paid opportunity currently proposed to you, internally or by an agency.
Run the four classification questions on each. Mark live, pilot, announced.
Separate the budget into performance and learning, with the learning portion capped and dated.
Identify your feed dependency — which paid lines get cheaper if Module 4's work lands. Say so explicitly; it is usually the strongest argument for that work.
Write the agency brief using the four requirements above.
Check who owns marketplace AI placements in your organisation, and whether they are in this plan.

Self-check

What four questions classify an AI advertising placement, and which one most often exposes a pilot?
Why is visibility work upstream of paid efficiency on generative surfaces rather than an alternative to it?
Your attribution window is seven days. What does the research-then-buy pattern do to it?
Why might rising brand-search volume be an output of your visibility work rather than a channel result?
Which of your current paid lines would get cheaper if your feed improved, and by what mechanism?