The ‘A to I’ of Media Buying

Manual workflows, rooted in historical campaign performance, platform benchmarks, and broad audience segments, are giving way to AI-powered systems that predict outcomes, optimise budget allocation in real time and uncover nuanced audience behaviours. 

From my time at various media agencies, the shift is unmistakable: planners at the biggest tech giants are partnering with data scientists to design marketing experiments, with more time allocated to harnessing machine learning models, rather than relying purely on instinct or post-hoc analysis.

Central to this evolution is the ability of AI platforms to ingest and analyse vast datasets from myriad channels simultaneously. Where media mix modelling once depended on monthly or quarterly updates, AI enables continuous, automated modelling. Algorithms detect emerging channel synergies, reallocate spend dynamically and surface early indications of campaign fatigue or opportunity. This constant feedback loop not only increases efficiency but also allows us and our clients to pivot strategies mid-flight, ensuring budgets are directed towards the highest-performing tactics at any given moment.

AI is also redefining creative optimisation. Predictive scoring can forecast which ad variants will resonate most strongly with specific audiences, prompting instant adjustments to headlines, images and call-to-action messaging. Rather than waiting for standard performance reports, planners like us are becoming architects of automated systems that test, learn and iterate in rapid succession. This shift elevates the planner’s role from data interpreter to conductor of an interconnected suite of AI-driven tools, where the craft lies in designing robust models and refining inputs to extract meaningful insights.

What does all this mean?

Clients seeking to stay ahead must first cultivate a change-ready mindset. Resistance to automation and adherence to legacy processes represent the biggest roadblocks to unlocking AI’s potential. Decision-making frameworks should support rapid experimentation, with clear governance for prompt testing, model validation and iterative learning. In practice, this means fostering a culture where data literacy is on a par with creative acumen, and where cross-functional teams collaborate seamlessly to translate model outputs into actionable plans.

Equally critical is talent and upskilling. AI does not replace strategic thinkers; it amplifies their impact. 

So planners need to understand how algorithms generate recommendations, interpret confidence intervals and spot potential biases. Finally, measurement frameworks must evolve alongside AI capabilities. 

Traditional last-click metrics are increasingly insufficient. Clients should adopt multi-touch attribution, incrementality testing and unified measurement solutions that capture the full customer journey. By closing the loop between performance data and planning inputs, they can establish tighter feedback loops that accelerate learning, boost return on investment and create future-proof media strategies.

Want to health-check your approach to media planning in an AI world?