Observed Signal · May 22, 2026 · Conference Presentation · Source: Digiday · Impact: 2/5 · Sentiment: Negative
WPP AI Chief: Agentic AI Still in Early Hype Phase
At the IAB U.K. AI Growth Summit on May 22, 2026, WPP’s chief AI officer, Dr Daniel Hulm, argued that agentic AI in advertising is currently in a hype-driven, early stage where announcements outpace real operational deployment. He warned that deploying large numbers of autonomous agents will require extensive testing — estimating that "at least 80%" of the work is testing — and highlighted a "second-order gap": agents trained on historical campaign data change market behaviour when deployed, which can quickly invalidate their models. Hulm said current industry work is often sophisticated rule-following rather than genuinely adaptive systems that observe outcomes and self-improve. His remarks positioned agentic AI as inevitable but cautioned marketers about the engineering, validation and monitoring effort required for production-scale adoption.
Comments from a major agency executive highlight realistic deployment challenges for agentic AI in advertising; useful industry perspective but not a product launch or policy change.
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Key Takeaways & Evidence Grounding
- Dr Daniel Hulm, described as WPP’s chief AI officer, spoke at the IAB U.K. AI Growth Summit on 2026-05-22.
- Hulm said that "at least 80% of the energy that you need to build agents is testing."
- He characterised the industry's agentic AI development as a 'teenage sex phase'—widespread claims but limited operational deployment.
- Hulm warned of a 'second-order gap' where agent-driven decisions change market behaviour, potentially making models obsolete once deployed.
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Agentic AI in Advertising: Progress, But Not Transformative Yet
Karsten Weide of W Media Research provides a measured assessment of agentic AI in advertising, arguing the technology's promise outstrips current production reality. Deployments exist—largely in planning, troubleshooting, and optimization—but fully autonomous buying is rare and typically constrained by human-in-the-loop governance, fragmented data, legacy stacks, and trust issues. PubMatic, Viant, and Yahoo are highlighted as leaders/early adopters; Amazon, Google, and Meta run internal agentic capabilities with tight controls. Startups and vendors are experimenting with agent-led planning and optimization, and products like HUMAN Security’s Agentic Trust aim to increase visibility. Near-term adoption is expected to be gradual, favoring semi-autonomous workflows until governance, interoperability, and clear ROI improve.
Agentic AI for the Open Web: Potential, but Scaling Stalls
An industry analysis of 200 marketing leaders (mostly at enterprises with >1,000 employees) finds strong belief but slow adoption of agentic AI on the open web: 82% see it as a growth opportunity, yet just 17% have scaled it as a proven performance driver. The main barrier is operational integration — fitting agentic systems into existing workflows, approval chains, reporting and attribution — rather than budget or technology skepticism. While Google and Meta have embedded agentic capabilities into closed ecosystems, emerging vendors are extending comparable automation to publisher inventory outside walled gardens. Early adopters that treat agentic AI as an operational transformation are gaining a first-mover competitive lead, with high paid-search and paid-social budgets driving interest in open-web expansion.
AI in Media Buying: Expectations Outpace Experience
A new IAB Europe study reveals that while 58% of industry professionals expect agentic ad buying to become mainstream within a year, actual adoption lags significantly. Of 47 respondents, 36 have either not yet deployed agentic systems or use technologies where humans still control planning and execution. The 'Impact of AI on Digital Advertising Report 2026', based on 50 interviews, shows that AI is widely used for reporting and campaign analysis (86% overall), but only 11 out of 29 detailed respondents use it for agentic buying and selling. A complementary survey by StackAdapt (500 marketers, 6 countries) found similar patterns, with half citing fragmented data pipelines as a barrier. Companies are cautious about granting full autonomy, with most preferring human-in-the-loop models. The average performance rating for AI in operational advertising was only 2.76 out of 5. The industry is still defining governance, training, and integration practices to bridge the gap between expectations and real-world experience.
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