In this guest article, Danny Holmes, Consulting Partner, Media and Agency at Experian Marketing Services, explains why AI works well with curated marketplaces, and how it’s transforming how agencies plan, target, and optimise campaigns.
Media buying has always evolved alongside technology, but the current shift is deeper. AI has moved upstream, beyond bid optimisation and budget allocation, now influencing how plans are built, audiences are defined, and decisions are made in real-time.
At the same time, curated marketplaces are becoming more common, combining privacy-safe audience signals with rich contextual insight for a more stable, efficient route-to-market. Yet as marketers enter this new reality of new technologies and platforms, one consistent remains: quality data sits at the heart. Without it, brands open themselves up to poor decision-making which ultimately can lead to weak audience connections, diluted campaign impact and budgets being spent in the wrong places.
The Rise of Curation
For a long time, open marketplaces delivered scale and flexibility. But their effectiveness depended on a level of visibility that has gradually eroded over time, driven by weakening third-party signals, fragmented cross-channel measurement, and stricter regulations. What this means is that while buyers still want relevance and accountability, the ways to achieve that are less straightforward than they once were.
Curated marketplaces emerged from that frustration. Recent agency research shows growing trend towards curated buying models. Yet there’s a catch: when asked to define what curation means in practice, fewer than half of agency respondents were able to do so accurately, with many offering only partial definitions. This lack of clarity appears consistently across agency types and sizes, suggesting that while curated buying is gaining momentum, its strategic role is not always well understood.
Whilst some buyers think of curation simply as pre-packaged inventory, true curation goes further. It offers the strategic optimisation that the open marketplace now fails to provide; instead aligning audience data, contextual signals, and supply-side connectivity to create greater control, consistency, and relevancy.
Whilst understanding of curations component parts may vary, the enthusiasm for it does not. The research shows that 89 percent of agencies believe curated marketplaces will become a key strategic driver by 2030, and that 70 percent of those already using curated strategies benefit from improved ROI and relevance. The evidence is clear: agencies are looking for an alternative to open marketplaces, and curation offers a solution.
Yet a word of caution: curation in practice is not always consistently defined or operationalised. Whilst promising, curated buying models alone do not resolve the industry’s search for greater control and clarity. This is where AI begins to change the equation.
AI Steps Into the Planning Room
Although AI has long operated quietly in the background, supporting bidding algorithms and pacing tools, its influence now spans the entire programmatic lifecycle. Advanced AI systems are influencing creative planning, audience strategy, and cross-channel investment decisions. The research shows 93 percent of agencies are using AI across their planning and targeting processes, with growing adoption across campaign management and bidding.
Adoption, however, has been uneven. Large agency groups have moved faster, supported by deeper data infrastructure and investment capacity, while smaller agencies have faced tougher choices around technology and resource. That gap is beginning to shape how competitive advantage forms.
This matters because AI systems improve through exposure. They learn from volume, feedback, and iteration. Agencies that adopt earlier build understanding alongside capability. Those arriving later may find themselves adapting to a market shaped by decisions made at machine speed, without having built the same internal fluency.
What is interesting to note is that most agencies (85 percent) expect AI to drive most media decisions by the end of the decade. That expectation is already changing behaviour. Planning cycles are shorter, optimisation is continuous, and decisions that once happened at fixed points are now unfolding in real time.
The Convergence of AI and Curation
The convergence of AI and curated marketplaces is not accidental. Both exist to reduce complexity and improve efficiency, and each strengthens the other. This union makes sense. Curation stabilises the input, AI scales the operation. Together, they create conditions where optimisation can happen faster and with greater confidence.
AI offers a path through the fragmentation facing both open and curated buying. It can connect disparate signals, detect patterns across channels, and optimise at a pace no manual process can match. But its effectiveness depends entirely on what it is trained on and how it is governed.
Data Quality: the Foundation Beneath Automation
Whilst the opportunities AI and curation offer are vast, fundamentally both are only as good as the data that feeds them. To truly unlock value, accurate, transparent, quality data must form the foundation that underpins each.
AI learns from, and amplifies, the data it ingests. When decisions are automated, bias can scale quickly, becoming embedded into systems, processes and models in a way that can be difficult to untangle. The same is true for curated marketplaces, automation can accelerate assumptions about inventory quality, context, or audience signals at speed. Curation built from weak audience data or inconsistent publisher signals risk undermining relevance, rather than enhancing it.
Yet, automation does not remove responsibility. Agencies remain accountable for outcomes, regardless of how decisions are made. The research shows that trust and data integrity remain the most important consideration when buyers evaluate partners and platforms. As AI takes on greater influence, human oversight becomes more important, not less. Clear governance, regular auditing, and a working understanding of model inputs and processes are essential to avoid blind reliance on automation. Teams need to know when to step in, and why.
Ultimately, data quality underpins everything. As AI and curation gain momentum, accuracy, and consistency matter more than scale alone. Where definitions are loose and inputs fragmented, automation simply accelerates uncertainty rather than resolving it.
What Media Buying Looks Like Next
As we start to adapt to new technologies, the next phase of media buying won’t feel so dramatic, it will be more controlled. Curated marketplaces will become increasingly familiar. AI will handle much of the optimisation and prediction. People will spend more time interpreting outcomes and shaping direction than executing individual tasks.
Competitive advantage will not come from automation alone. It will come from how well agencies balance speed with judgement, and efficiency with responsibility. AI and curation are reprogramming the mechanics of media buying. But the fundamentals – quality, transparency and integrity – firmly remain. The agencies that take care with data, oversight, and intent will be best placed to navigate what comes next.
