Sean Betts, Chief AI & Innovation Officer, Omnicom Media UK
As AI platforms mature, questions around commercial sustainability are becoming increasingly common. User adoption is ever-increasing; however, this is largely outpacing monetisation frameworks.
AI platforms are now starting to look towards advertising as the answer. It is a tried and trusted value exchange that has powered the internet for more than twenty-five years: free services in return for attention. For decades, it has created a stable equilibrium between user experience and commercial pressure. Now every AI platform is attempting to recreate that playbook inside an environment that works nothing like the open web.
Old Logic, New Medium
The problem is that AI platforms are trying to apply traditional thinking to a completely new medium. They are looking to place ads into conversational interfaces in the same way we once placed ads into webpages and search results. The formats look familiar. The logic looks familiar. And if they continue down this path, familiar challenges, such as relevancy, may begin to surface within AI platforms.
At their core, these challenges stemmed from the same pattern. Ads are shown because they can be shown, not always because they should be shown. And consumers can be interrupted with messages that have little to do with what they want, where they are, or what they are trying to achieve in that moment.
AI platforms run the risk of treating advertising as a UI problem rather than a reasoning problem. They are sitting on something the advertising industry has never had before: an intelligence that genuinely understands what a user is trying to do. Not a keyword. Not a demographic bucket. Not a retargeting signal scraped from another tab. A real-time, contextual interpretation of intent.
Let The Model Think About The Ad
The opportunity AI platforms are currently missing is to let their models think about the advertising. Allow the model to evaluate it in the same way it evaluates any other piece of information. By introducing paid advertising opportunities into the reasoning loop, AI models can judge whether it is relevant to the task, the context, and the user’s stated or implied intent. This idea works because of what large language models already do – they evaluate information, filter options, and assemble an answer.
Paid advertising opportunities would not look like ads in the traditional sense. They would be structured inputs, including product information, visual assets, messaging, and a URL. This turns advertising into a format the model can reason with, so it can decide if it’s relevant, doing everything the advertising industry has spent twenty years managing manually.
The flow should be simple: advertisers provide structured inputs. The model evaluates them. Irrelevant options drop out. Relevant ones surface with full, 100% transparent disclosure.
To be clear, paid advertising opportunities would exist for one purpose only: to be evaluated by the model for relevance. The model decides whether the advertising content informs the answer. If it doesn’t, the user never sees it. If it does, it appears in the output with full transparent disclosure and a citation. This mirrors how AI platforms cite sources in search-style answers today – the mechanism already exists, it can simply be extended to paid content.
This approach requires a simple, transparent rule set. Any paid influence must be cited. If a sponsored option contributes to the output, the user sees that immediately. If a sponsored option is considered but rejected, the platform can log that privately so advertisers can understand why and improve future ads.
Most importantly, the integrity of the AI platform should be non-negotiable. Putting ads on the surface invites scepticism. Putting ads into the model’s reasoning, with full disclosure, invites relevance.
A Healthier System for Everyone
Advertising inside the model flips the dynamics of advertising. Brands would not win because they outbid competitors. They would win because their messaging genuinely matched the user’s intent as interpreted by the model. If AI platforms move advertising into the reasoning layer, they unlock something the industry has never had. True contextual relevance at the moment of intent. Ads that are additive. Ads that are filtered by intelligence rather than interruptive by design. Advertising would be additive rather than a distraction and have the additional benefit of not adding clutter to the user experience that could break flow, annoy consumers, and erode trust. This is a healthier system for everyone involved.
This also creates a new commercial opportunity. Some potential food for thought – we could have a cost per thought when the model evaluates a paid advertising opportunity. A cost per impression when that option is surfaced. A cost per click if the user takes action. It’s a clean hierarchy that mirrors the actual funnel of the AI model’s reasoning that will give advertisers flexibility, allow them to control budgets around different stages of decision making, and align incentives around quality instead of intrusion.
If AI platforms get this right, they can build a new kind of advertising system. One built on intelligence, context and intent. One that rewards relevance as a first principle. One that gives users better answers and gives brands better opportunities.
The future of advertising will not be won by whoever finds the cleverest place to stick an ad in a chatbot. It will be won by whoever lets their model put the most relevant content in front of consumers.
