AI Makes Advertising Less Transparent and Harder to Justify

As AI becomes more deeply embedded in paid media, marketing leaders face new limitations on their ability to confidently measure and defend advertising investments.

At the same time, AI changes the economics of advertising, driving up expenditures and increasing marketer ad platform dependence. Audiences remain conflicted about how brands use AI in customer-facing communications, and they aim to spend less time on AI-powered social media platforms.

From the Gartner Marketing Symposium/Xpo in London, we spoke with Eric Schmitt, VP Analyst in the Gartner Marketing Practice, about why AI is making advertising harder to justify and what marketing leaders get right and wrong when it comes to AI paid media strategy.

Journalists who would like to speak with Eric regarding this topic can contact Jordan.Brackenbury@gartner.com. Members of the media can reference this material in articles with proper attribution to Gartner.

A: AI makes advertising harder to justify in part because it accelerates the concentration of spend captured by three large platforms. 

As more paid media execution moves into automated systems, marketers are finding themselves with less control over targeting, placement and creative delivery while still being expected to explain outcomes with confidence.

The challenge is not simply that advertising is changing, it is that marketers are being asked to invest in, and share, their proprietary data with AI algorithms that are first and foremost optimized for ad seller revenue and margin. That makes paid media more difficult to govern internally and more difficult to defend as a strategic investment.

A: AI is changing control by moving more decision-making into platform algorithms. That affects core areas, such as audience targeting, ad placement and creative optimization, all of which are increasingly shaped by systems over which marketers have less control and transparency.

At the same time, limits around measurement and interoperability can make it harder for marketers to compare performance consistently or validate mix decisions on their own terms. The measurement challenges of AI-driven advertising are arguably greatest for brand spend, which is inherently geared to longer-term time horizons and often especially vulnerable to fraud.

That loss of control also creates more volatility. When media performance depends heavily on opaque algorithmic systems, marketers can be exposed to sudden swings in delivery or outcomes that are difficult to predict and even harder to diagnose.

We observe an increasing number of direct-to-consumer performance-oriented marketers living through that volatility now. For marketing leaders, that uncertainty is not just a campaign issue — it can weaken planning, forecasting and confidence in broader media strategy.

A: Audiences are ambivalent about AI-powered media platforms, and AI-generated content. According to a Gartner survey of 1,539 US consumers conducted in October 2025, 50% of consumers said they would prefer to give their business to brands that do not use GenAI in their messaging and communications. That means marketers cannot assume that audience engagement with AI-powered platforms automatically signals approval of AI-led brand interactions.

For marketers, the implication is clear: they need to separate channel behavior from brand preference. Brands still need to reach audiences where they are, but they also need to be thoughtful about when AI enhances communications, and when it risks undermining authenticity, trust or credibility. 

A: Marketing leaders should take a selective, portfolio-based approach to AI in paid media. A useful framework is to harness, hack and hedge (see Figure 1).

Harness AI within individual platforms to improve performance by playing the game the way the platform algos are structuring it to be played. Google’s Performance Max and AI Max are two examples. At the same time, hack together multi-platform solutions where opportunity allows, such as creative production and campaign analytics, which can be used to pick up efficiencies and insights across multiple lines in the paid media plan. Finally, don’t be afraid to hedge bets by shifting a portion of paid media budgets out of advertising altogether, and into other marketing AI priorities, such as answer engine optimization.

Not every AI advertising use case is equally worthwhile. The most effective marketers will be the ones who know when to work with platform AI, when to develop more tailored approaches, and when to redirect paid media to strengthen owned media, discoverability and engagement.

Gartner clients can read more in the report “AI Use-Case Assessment for Advertising”.