Table of Contents
The promise of AI in advertising has been seductively simple: more efficiency, more personalisation, better targeting, and better outcomes — all delivered by algorithms that know more about audiences and buying patterns than any human media planner could.
The promise has been partially fulfilled. AI-driven media buying does produce efficiency gains. AI-powered targeting does improve relevance. The platforms’ algorithms do get smarter over time.
But Gartner’s VP Analyst Eric Schmitt, presenting at the Marketing Symposium/Xpo in London, articulated a problem that sits alongside these gains and doesn’t cancel them but significantly complicates them: AI is simultaneously making advertising less transparent, harder to measure independently, and more difficult to justify to the leadership teams that fund it.
“AI is changing control by moving more decision-making into platform algorithms. For marketing leaders, that uncertainty is not just a campaign issue — it can weaken planning, forecasting, and confidence in broader media strategy.” — Eric Schmitt, VP Analyst, Gartner Marketing Practice
This isn’t an argument against AI in advertising. It’s an argument for understanding what AI in advertising actually changes about your relationship to your media investment — and what governance capabilities marketing leaders need to maintain as AI takes on more of the decision-making.
The Transparency Problem: What AI Obscures
Advertising transparency has never been perfect. The history of media buying is full of opacity — agency markups, undisclosed rebates, viewability fraud, brand safety violations, and the perennial gap between what publishers report and what advertisers can independently verify. These problems existed before AI and will exist alongside it.
What AI changes is the nature and scale of the opacity.
The Black Box Decision Problem
When an advertiser uses AI-powered campaign management tools — Google’s Performance Max, Meta’s Advantage+ Shopping, or Amazon’s equivalent — they specify a budget and a goal.
The algorithm decides where ads appear, to whom they’re shown, at what times, in what creative combinations, and at what bid levels.
The advertiser approves the campaign; the algorithm makes thousands of tactical decisions per hour that the advertiser cannot directly observe or override.
This is a meaningful shift from the era of manually managed campaigns, where a media planner set specific bids on specific placements to specific audiences and could account for every pound spent at a detailed level.
The AI campaign produces results — often better results — but the pathway from budget to outcome runs through a system the advertiser doesn’t control and often can’t fully audit.
The Platform Concentration Problem
AI is accelerating the concentration of advertising spend at three major platforms: Google, Meta, and Amazon. The economic logic is straightforward: AI media buying tools work best where data is most abundant, and data is most abundant on the platforms with the largest audiences.
AI-optimised campaigns naturally flow toward Google, Meta, and Amazon because those platforms have the richest audience data and the most sophisticated AI optimisation infrastructure.
The consequence is reduced advertiser leverage and reduced ability to diversify risk. When three platforms capture the majority of your media investment, you are dependent on their algorithms, their reporting, and their pricing decisions.
The cost of challenging a platform or reducing spend is high; the ability to independently verify their performance claims is limited; and the concentration of budget creates a negotiating position that advantages the platform, not the advertiser.
The Reporting Reliability Problem
AI-powered campaigns are evaluated using metrics provided primarily by the platforms running them. Platform-attributed conversions, reach estimates, frequency data, and audience composition reports are generated by the same systems that benefit commercially from demonstrating strong performance.
This creates a structural conflict that isn’t resolved by the sophistication of the AI involved. Attribution models that credit platform-driven conversions may overestimate the incremental contribution of advertising compared to what would have happened without it.
View-through attribution, last-click models, and platform-defined conversion windows all shape what the dashboard reports in ways that aren’t necessarily aligned with advertiser interests.
The Cost Problem: AI Is Making Advertising More Expensive
Beyond transparency, Gartner’s analysis identifies a direct economic concern: AI is contributing to rising advertising costs on the major platforms.
The mechanism is competitive: as more advertisers deploy AI-powered bidding tools that automatically optimise toward conversion, and as those tools compete in the same auctions for the same audiences, auction prices rise. AI doesn’t create supply — it increases demand efficiency.
When every major advertiser in a category is using AI to bid more precisely for the most valuable audiences, the price of reaching those audiences increases for everyone.
This cost inflation occurs alongside the transparency reduction, which creates a particularly difficult situation for marketing leaders trying to justify advertising investment: you’re paying more, and understanding less about what you’re getting.
The Consumer Trust Dimension
Gartner’s analysis connects AI’s advertising transparency problem to the consumer trust data that Schmitt presented alongside it. A Gartner survey of 1,539 US consumers conducted in October 2025 found that 50% of consumers say they would prefer to give their business to brands that do not use GenAI in their consumer-facing messaging and communications.
This finding introduces a different kind of transparency challenge. AI-powered advertising is often invisible to consumers as AI — the ad doesn’t announce that its creative was AI-generated, its targeting was AI-determined, and its placement was AI-selected. But consumer scepticism about AI-generated content extends to advertising content even when that AI origin isn’t disclosed.
The 50% consumer preference for non-AI brands means that advertising that uses AI tools to optimise reach is potentially being served to audiences who, if they knew AI was involved, would prefer a competitor. This isn’t a theoretical concern — it’s a measurable audience segment with stated preferences that advertising AI is not designed to accommodate.
Schmitt’s recommendation: marketers need to be transparent with consumers about when and how AI is used in advertising and marketing communications, both as an ethical commitment and as a practical response to the demonstrated consumer preference for authenticity.
What Governance Capabilities Marketing Leaders Need
The response to AI’s transparency and justification challenges isn’t to reject AI in advertising — the efficiency gains are real and the platforms aren’t going back to fully manual bidding. The response is to build the independent measurement and governance capabilities that allow marketing leaders to verify what AI is delivering and make defensible decisions about advertising investment.
Independent Measurement Infrastructure
Marketing mix modelling (MMM) provides a channel-agnostic, methodology-independent view of how media investment drives business outcomes. MMM can assess the incremental contribution of AI-managed campaigns in context with the full media mix, without relying on platform-reported attribution.
For CMOs who need to defend advertising investment to a CFO who is rightly sceptical of platform-reported returns, MMM provides the independent evidence base.
Incrementality Testing
Controlled incrementality experiments — where identical audiences are randomly split between exposed and unexposed groups — test whether AI-driven campaigns are actually driving incremental conversions or capturing conversions that would have happened anyway. This methodology is more operationally complex than platform attribution but far more reliable as evidence of advertising effectiveness.
Platform Concentration Audits
Regularly reviewing the concentration of media investment across platforms and identifying the degree to which performance reporting depends on self-reported platform data is a basic governance step. Setting explicit thresholds for platform concentration and building the internal capability to evaluate alternative or complementary channels reduces dependency risk.
AI Transparency Policies
Given the consumer preference data, developing clear internal policies about when AI is used in consumer-facing advertising and how that usage is disclosed — or not — is both a governance and a brand decision. Brands that move proactively on AI transparency positioning will be better prepared for the regulatory and consumer expectation environment that is developing rapidly.
Trusting the Algorithm Is Not a Strategy, It’s a Governance Gap
AI in advertising is neither the saviour that its advocates promised nor the disaster that its critics feared. It’s a tool with real benefits and real costs — and the costs are concentrated in areas that marketing leaders have historically underinvested in: measurement independence, governance infrastructure, and the ability to explain, in plain language, to a sceptical CFO exactly what the advertising investment is delivering and why.
The brands that navigate this moment well will be those that use AI’s optimisation capabilities while building the independent measurement infrastructure that allows them to verify, challenge, and understand what AI is doing with their money.
The brands that don’t will find themselves in an increasingly uncomfortable position: spending more on advertising, understanding less about it, and being unable to defend the investment when the CFO asks.
AI makes advertising more powerful. It also makes it harder to see. The solution is to build better eyes — not to trust the algorithm and hope for the best.
Struggling to justify your advertising investment in an AI-driven media environment?
The Brisk Digital helps brands build measurement frameworks, independent verification approaches, and transparent advertising strategies that hold up under CFO scrutiny — and deliver real business outcomes.
Let’s build advertising that you can defend, not just deploy.
No Comments