AI Video Adoption Splits by Brand Size — and Leaves Smaller Brands Behind

What the 2026 IAB data shows about AI video adoption

AI video adoption in 2026 splits sharply by brand size. Smaller marketing teams lean hardest into generative creative production, while enterprise buyers point the same technology at inventory discovery and measurement. The result is two very different AI video strategies running in parallel across the market.

The 2026 IAB Digital Video Ad Spend & Strategy report, based on a survey of 360 media executives conducted in February and March, shows generative AI is now mainstream in video production. Nearly two-thirds of buyers use it for digital video creative, up from half in 2025, and about a third of all ad assets will leverage GenAI this year, a share the report projects will reach 43% by 2027. But the headline average hides the split. When the data is segmented by spender size, the patterns diverge sharply, and that divergence explains why some teams feel they are winning with AI video while others feel stuck. Teams building annual plans should pair this split with the {{link}} that track overall market direction.

Teams building annual plans should pair this split with the 2026 video marketing benchmarks that track overall market direction.

Smaller brands lean hardest into generative creative

Smaller and mid-size buyers are the most aggressive adopters of generative video creative. The IAB report finds they lean into AI for creative testing, pre-planning, and performance analysis, treating generation as a way to close the production gap with much larger competitors. For a team without an in-house studio, the ability to spin up dozens of variant cuts from a single brief changes the math on how much creative it can actually ship in a quarter. In practice that means a five-person brand can field a testing matrix of hooks, lengths, and openings that a year ago would have required an external production company and a four-figure budget per round.

This is also where AI video overlaps with the broader move toward in-housing. Smaller brands that cannot outspend rivals on agency retainers use generative tools to keep production in-house and iterate quickly. The risk is that speed becomes the only metric, and the creative that ships is wide but shallow. Without a clear testing framework, a high volume of AI-generated cuts can still fail to move the metrics that matter, leaving the team with a lot of output and little evidence.

The practical takeaway for a small team is not to generate more, but to generate against a hypothesis. A handful of variant cuts tied to one clear performance question beats a warehouse of undirected content, because only the hypothesis-driven set can tell you what actually changed.

Enterprise buyers point AI at inventory and measurement

Large spenders tell a different story. The same IAB data shows enterprise buyers are more likely to apply AI to inventory discovery, deal evaluation, and measurement rather than to hands-on creative generation. Running campaigns with many deal types, partners, and markets, they treat AI as an orchestration and analysis layer on top of existing production rather than a replacement for it. Their constraint is not creative volume but coordination: with dozens of supply paths and regional variants in flight, the highest-value use of AI is sorting signal from noise across the whole plan, not producing one more asset.

That is why enterprise roadmaps still treat {{link}} as the system of record for where ads actually run. The priority is less about making one more cut and more about knowing which inventory is valid, where the ad appears, and how much of the delivery is non-human traffic. For large buyers, AI video maturity is measured in confidence about the buy, not just in creative output, and that confidence is what protects the budget when signal loss erodes audience fidelity.

That is why enterprise roadmaps still treat video delivery and inventory platforms as the system of record for where ads actually run.

Why smaller buyers feel least equipped

The paradox is that the buyers leaning hardest into generative creative are also the least satisfied with their tooling. The IAB Full Report states that among smaller buyers, 96% are not satisfied with their current level of GenAI use for creative ad production. More than four in ten want more proof of performance and easier integrations with the platforms and DSPs they already run. The capability gap is not interest; it is infrastructure and evidence. Being unsatisfied rarely means the output is bad; it means the team cannot connect the output to a number the CFO will accept, so every new cut feels like a guess rather than a measured bet.

Epsilon's 2026 benchmark study adds context: while 100% of surveyed marketers now use AI, only 9% apply it primarily to revenue, and just 46% measure AI by revenue outcomes at all. Seniority widens the perception gap further, with 67% of C-level leaders calling their organization extremely mature against 33% of senior managers. Closing it starts with the discipline to {{link}} before scaling production. Smaller teams that connect AI video to a revenue or efficiency metric, not just output volume, close the gap fastest because they can show what the spend bought.

Closing it starts with the discipline to prove AI video budget impact before scaling production.

The targeting shift that widened the gap

A second force compounds the size split. The IAB report shows targeting overtook content quality as the top criterion for TV and video investment, rising ten points year over year. Small and mid-size spenders drove the shift, gaining twenty-three points, and they are also the most exposed to open-market identity challenges as signal loss erodes audience fidelity. In other words, the buyers with the least mature measurement stack are now betting their video strategy on targeting precision they cannot fully verify. The cruel part is timing: the same auction that rewards fresh creative also punishes stale creative within two or three weeks, so the teams with the weakest measurement are forced to refresh fastest.

The same pressure is visible in the {{link}} that now treats AI production as always-on. Consumer packaged goods, the largest video ad category at 16.9 billion dollars in the IAB data, and retail alongside it, are shifting budget toward continuous, AI-assisted production because the auction rewards fresh creative faster than ever. Smaller brands in these categories feel the squeeze most acutely: they must match the cadence without the measurement bench of an enterprise.

The same pressure is visible in the CPG and retail video ad shift that now treats AI production as always-on.

Agentic buying raises the human-in-the-loop question

The size split also shapes how far each team is willing to let AI act on its own. The IAB Full Report finds two-thirds of digital video buyers are already live, testing, or planning agentic AI for campaigns in 2026, and 96% agree there is a role for it in programmatic. But consensus on that role breaks down by size: about half of small and mid-size spenders feel strongly that humans must stay in the loop, compared with 40% of buyers overall. That caution is healthy. An agent that can reallocate spend at machine speed is a feature for a team with a control tower and a liability for one without a named owner of the outcome.

The demand for controls is concrete. Thirty-six percent of buyers want an audit trail that explains what an AI agent did, and 31% want hard guardrails on what it can do. For smaller brands, these safeguards matter more because they lack the dedicated ops and legal bench that enterprises use to absorb automation risk. Agentic buying will arrive fastest where the team can prove the agent's decisions, not where it simply moves the fastest.

What each team should do with AI video now

The size split is not a verdict but a map. Smaller brands should resist the temptation to out-produce larger rivals and instead pick one metric, connect AI video to it, and build a small variant library they can actually measure. Enterprise teams should keep pushing AI into the buying and measurement layers where their scale creates the most leverage, and resist declaring victory on creative output alone. Neither side benefits from mimicking the other; the advantage comes from going deeper on the layer where its size already creates leverage.

Both groups meet at the same truth: AI video adoption only compounds when the organization can prove what it changed. The brands that pull ahead in 2026 will be the ones that matched their AI video strategy to their actual size, instead of copying a playbook built for a budget they do not have.