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"Generative AI is changing how advertising creative is developed—but not by replacing human creativity. Drawing on interviews with industry leaders and a broad review of emerging research, this ARF Creative Council white paper examines how AI is being used across the creative process, from briefing and ideation to personalization and optimization. It explores where AI delivers the greatest value today, the risks organizations continue to navigate and why human judgment remains central to producing distinctive, effective advertising."

"AI-optimized, omnichannel campaigns are changing how media are planned, activated and measured. Drawing on in-depth interviews with advertisers and providers, the ARF Cross-Platform Measurement Council’s Attribution Working Group examined omnichannel campaign tools such as Google Performance Max and Meta Advantage+, exploring their potential to simplify execution, optimize across channels and improve performance. Their research also identified important tradeoffs, including transparency, testing capabilities and ongoing measurement and governance challenges."

"As privacy changes make it harder for marketers to track individual customer journeys, media and marketing mix modeling (m/MMM) is experiencing renewed interest. Open-source tools from Meta, Google and the PyMC ecosystem promise to make these methods more accessible, particularly for organizations without the resources for customized proprietary models. In this MSI working paper, Julian Runge and Koen Pauwels evaluate the leading open-source approaches, examining their capabilities, differences and tradeoffs. Their analysis shows that while automation can democratize sophisticated marketing measurement, using these tools responsibly requires careful attention to model assumptions, diagnostics, calibration and validation."

"After conducting eight studies over the past year, what has the ARF learned about the psychology of generative AI? Across experiments examining bias, identity cues, product recommendations, executive decision-making and AI-assisted research, four patterns repeatedly emerged: GenAI tends to compress human variability into dominant responses, subtle differences in prompting can meaningfully change outputs, different models bring their own constraints and tendencies and AI can imitate the language of expertise more readily than it can exercise expert judgment. Taken together, the studies suggest that GenAI’s greatest strength and its greatest risk may be the same: its ability to produce coherent, convincing answers can make compression, instability and missing judgment difficult to see."

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