The Rise of AI Ad Buying Agents: A Marketer’s Perspective

AI-driven ad buying is not new. From Google Ads’ “set-it-and-forget-it” approach to Meta’s dynamic audience targeting, the goal has always been to automate away the tedium of bidding and targeting. But with the more recent advancements in generative AI and deep learning, the promise is morphing into something more expansive: real-time, hyper-personalized ads that match individual user contexts and behaviors. This is poised to change not just how media is bought, but who does the buying and what the marketer’s role becomes.

Below, we’ll explore the strategic implications of AI ad buying agents through several lenses, anchored in how this evolution intersects with Marketing Mix Modeling (MMM) and broader marketing strategy.

A New Era, But Not Without Its Challenges

Leveraging AI ad buying agents can increase efficiency and scale. It can deliver real-time bidding and optimization across countless user segments, providing immediate improvements in performance metrics (e.g., lower CPM, higher ROAS).

Additionally, AI can dynamically iterate creative variants to serve hyper-relevant content, potentially boosting conversion rates. It can also reduce human error as the system learns from massive datasets, cutting down on suboptimal bidding strategies that come from guesswork or fatigue.

However, with black-box AI, it can be difficult to understand exactly why certain targeting decisions are made or how creative choices are optimized. The overreliance on platforms can further lock marketers into platform ecosystems, reducing leverage and control over data and strategy. There are also privacy concerns as businesses risk potential compliance pitfalls if their data usage is unclear. It will be up to advertisers to decide whether the positives outweigh the negatives when it comes to utilizing AI ad buying agents.

AI Agents Will Impact Media Buying

AI agents are algorithmic at their core. Big ad platforms (Google, Meta, Amazon) will leverage their proprietary user data and signals to automate both bidding strategies and creative personalization at scale. As these platforms grow more capable, they’ll effectively “take over” day-to-day campaign management and creative tasks.

With the mechanics of bidding and targeting optimized by the platform’s AI, media buyers will increasingly focus on broader strategic imperatives: cross-channel mix, brand equity, and holistic performance measurement. Tactical button-pushing and real-time optimizations move from human control to machine control.

However, the biggest leap may come from creative optimization. AI agents can quickly iterate ad variations to match specific user contexts: time of day, browsing history, or even emotional state. Humans cannot match that speed or scale.

Overall, this will lead to marketers shifting from manual oversight of granular campaign details toward orchestrating the big picture which includes budget allocations, brand consistency, and synergy across multiple channels and platforms.

As a result, the major ad players will be the biggest winners as they gain even more power in the industry. With marketers giving them more control over their day-to-day tasks, they have all of the data that they would need to feed these AIs, and will push users out of their platforms as they can’t compete with automated bidding, targeting, and personalized creative.

Putting It Into Practice

Despite these concerns, many marketers will still move forward with AI buying agents in some capacity. For marketers considering using AI buying agents, they should start with testing their budgets. This helps a brand see how well the AI performs for a specific brand and audience, mitigating risk while testing for the real thing.

Additionally, AI’s capacity for constant iteration can help marketers discover pockets of growth or new audience segments faster.

Even with AI in play, however, it’s important that marketers themselves maintain control over brand guidelines and strategic messaging. Humans must ensure that the algorithmic optimizations align with brand values and long-term objectives, and that nothing is being missed when using AI.

Working with MMMs

Once an AI buying agent has been established, MMM tools can help by showing the lift each channel is driving and how AI-driven channels interplay with other marketing tactics.

While agents can optimize in-channel performance, MMMs provide a holistic view. Further. marketers still need to measure incremental impact across all channels, including those run by AI, to see the broader picture of which tactics truly move the needle.

Additionally, MMM can act as the strategic governor or referee, determining how much budget to allocate to each AI-driven channel. While the AI optimizes within a platform, MMM informs how to orchestrate spend across multiple platforms or marketing vehicles.

Incorporating MMMs with AI agents also helps provide a long-term perspective. MMM contextualizes the short-term performance from AI agents in the broader scope of brand-building, seasonal factors, and macroeconomic influences. In other words, MMM measures what AI can’t see: cross-channel synergies, brand-building effects over time, and broader market conditions.

AI ad buying isn’t a trendy flash in the pan. It’s the inevitable next step in an ongoing journey that began with simple programmatic buying, progressed to sophisticated machine learning, and is now poised for widespread adoption of generative AI. As a result, the marketer’s role evolves from micro-management to macro-strategy. Automation frees up resources to invest in creative thinking, brand development, and deeper data analysis–where human judgment and intuition are still critical. Additionally, as big tech takes up more of the process, MMMs can help maintain a semblance of normalcy as they act like governance between AI and the user.