At a Glance
As third-party tracking declines, adtech teams are rethinking both creative production and audience targeting. Generative AI is enabling scalable ad creative, contextual targeting, first-party propensity modeling, and smarter budget allocation—without relying on traditional lookalike audiences. The real advantage comes from the infrastructure behind it: experimentation systems, clean room integrations, model pipelines, and workflows that make AI usable in production.
For most of the programmatic era, the dominant targeting strategy was scale through similarity — find your best customers, build lookalike audiences, and reach more people who resemble them. It was a powerful idea, and it worked well in a world where third-party data was abundant and cross-site tracking was unrestricted. That world is ending. And the targeting and creative strategies that replace it look meaningfully different.
Generative AI is reshaping adtech on two fronts simultaneously. On the creative side, it is collapsing the cost and time of producing ad variations at scale, enabling personalisation that was previously only economical for the largest advertisers. On the targeting side, it is enabling new approaches to audience modelling that do not depend on the cross-site identity infrastructure that is being dismantled. Neither shift is complete, and neither is simple to implement — but the direction is clear.
Creative at Scale: What Generative AI Actually Enables
Ad creative has always been a bottleneck. A campaign targeting five audience segments across three formats — display, social, and video — requires fifteen creative variants at minimum. With localisation, seasonal updates, and A/B test variations, the number multiplies quickly. Creative production has historically been the rate-limiting step between a good targeting strategy and its execution.
Generative AI breaks this constraint. Text-to-image models, language models fine-tuned on brand voice, and video generation tools can produce ad creative variations in minutes rather than days. For performance marketing teams, this unlocks a testing velocity that was previously impossible — running fifty headline variants against a target audience to identify which messaging frame resonates, rather than guessing based on intuition or running sequential tests over weeks.
Practical shift: The creative bottleneck moves from production to evaluation. Generative AI can produce a hundred variants; the engineering and process challenge becomes building the experimentation infrastructure to test them meaningfully and act on the results.
- Dynamic creative optimisation (DCO) systems that assemble personalised ads from generated components — headline, image, CTA — at serve time are now within reach for mid-market advertisers, not just enterprise ones
- Brand consistency remains the hardest problem: models fine-tuned on brand assets and style guides produce more consistent outputs, but human review workflows are still essential before creative goes live at scale
- Video is the frontier — short-form video ad generation is improving rapidly, but the quality bar for human-facing creative remains higher than for static display
Targeting Without Third-Party Data
The loss of cross-site tracking has forced a rethink of how audiences are built and activated. The lookalike model depended on a rich cross-site behavioural graph — your best customers could be identified across the web, their behaviour patterns extracted, and similar users found. Without that graph, the approach breaks.
The replacement strategies that are gaining traction operate on different principles. Contextual AI is the most mature. Rather than targeting users based on who they are, contextual targeting matches ads to content — but modern contextual systems go well beyond keyword matching. They use large language models to understand page content at a semantic level, infer the intent and mindset of a user reading that content, and match ads accordingly. A page about marathon training implies an audience with different purchase intent than a page that merely mentions running shoes.
Predictive audiences built on first-party signals represent the second major approach. Instead of matching against a third-party identity graph, advertisers build propensity models on their own customer data — CRM records, purchase history, engagement behaviour — and use these to score and segment their known audience. The reach is smaller than a lookalike campaign, but the signal quality is higher, and the data is owned rather than rented.
Targeting evolution: The move from third-party lookalikes to first-party propensity models is not just a technical substitution — it requires a different relationship with customer data, which means investment in data collection, consent infrastructure, and modelling capability that many advertisers have not yet made.
Clean rooms are the infrastructure bridge between these approaches. By allowing advertisers and publishers to match and model against each other’s first-party data without either party exposing raw records to the other, clean rooms enable the kind of audience collaboration that previously required data brokers and third-party cookies. Google’s Ads Data Hub, Amazon Marketing Cloud, and independent providers like Habu are operationalising this approach at scale.
AI-Powered Bidding and Budget Allocation
Beyond creative and targeting, generative and predictive AI are reshaping how budgets are allocated and bids are set. Traditional rules-based bidding — bid X for users in segment Y, reduce bids by Z% after N frequency — is being replaced by learned bidding strategies that optimise directly for business outcomes rather than proxy metrics.
- Bid shading algorithms, now standard in first-price auction environments, use ML models trained on auction price data to predict the minimum bid required to win, reducing overpayment without sacrificing win rate
- Portfolio bidding systems allocate budget dynamically across campaigns and channels, shifting spend toward where the marginal return is highest based on real-time performance signals
- Incrementality testing — measuring the true causal impact of advertising by comparing exposed and holdout groups — is the standard that sophisticated advertisers are moving toward, replacing last-touch attribution with a more honest measurement of what advertising actually drives
The Engineering Infrastructure Required
Implementing generative AI in adtech at production scale requires infrastructure that most teams underestimate. A creative generation pipeline that produces brand-consistent variants on demand needs model hosting, a brand asset store, a prompt management system, a human review workflow, and integration with the ad serving platform. A contextual AI system needs a content classification service that can process millions of URLs per day. A clean room integration requires data ingestion, privacy-preserving computation, and output delivery pipelines.
None of this is prohibitively complex, but it is real engineering work — not a prompt and an API call. The teams that are getting the most from generative AI in adtech are those that have treated it as a systems problem, not a model problem. The model is often the easiest part. The infrastructure that makes it reliable, scalable, and integrated with existing workflows is where the durable investment lives.
At Nineleaps, we help adtech and marketing teams build the engineering foundations for generative AI at scale — from creative generation pipelines to the experimentation infrastructure that proves what actually works.
