Future States & AI

• Future States & AI
• Article

Beyond the Recommendation: How Prompt Language Changes How AI Frames Your Brand

• Psychology of Gen AI
• ARF; MSI

Generative AI tools are not just recommending products—they are shaping how consumers perceive them. This latest issue in the Psychology of Gen AI series, the third phase of the seventh study on AI product recommendations, examines how small changes in prompt wording alter the explanations AI systems generate around the same brand. Using ChatGPT descriptions of Arm & Hammer Advance White toothpaste across multiple, shopping-related prompts, the study reveals how AI recommendations construct different product narratives, emphasize different benefits and even introduce different caveats depending on the consumer’s wording of their question.

• Targets & Segments
• Article

When AI Meets Representation: The Consumer Reality of Synthetic Diversity

• ARF; MSI

As brands increasingly use AI-generated people in advertising, a new MSI Working Paper explores how consumers respond when those synthetic models are used to increase racial representation. Across multiple experiments, the research finds that disclosure of AI-generated Black models can reduce brand evaluations by weakening perceptions of authenticity and commitment to diversity initiatives, while similar effects do not emerge for White models. The findings offer important guidance for marketers seeking to balance technological innovation with meaningful representation.

• Future States & AI
• Article

Finding Your Brand’s AI Niche: How Prompt Nuance Shapes Product Recommendations

• Psychology of Gen AI
• ARF; MSI

As generative AI becomes a key part of how consumers discover and evaluate products, a new question emerges for marketers: how can they ensure their brands show up in AI-driven recommendations? This ARF and MSI experiment, the second phase of the seventh study in the Psychology of Gen AI series, reveals that even small changes in prompt wording can significantly influence which brands appear—helping non-market dominant brands carve out visibility by aligning with specific product attributes rather than competing broadly for “best” status.

• Past Event Highlights
• Article

Commerce and Shopper Intelligence

Retail media networks and commerce media are redefining the data and measurement landscape, creating new opportunities—and complexity—for marketers and researchers alike. At Commerce & Shopper Intelligence 2026, brands, retailers, and researchers revealed how they’re adapting methodologies and frameworks to better understand increasingly fluid, data-rich shopping journeys.

• Future States & AI
• Article

Striking the Right Balance in AI-Personalized Advertising

• JOURNAL OF ADVERTISING RESEARCH

Generative AI is opening new possibilities for hyper-personalized advertising, including the ability to create AI-generated faces that closely resemble individual consumers. But how similar is too similar? This Journal of Advertising Research study finds that while moderate facial resemblance can improve advertising effectiveness, excessive similarity may backfire. The research introduces a new framework for measuring facial similarity and identifies an optimal personalization threshold that maximizes purchase intentions while avoiding consumer discomfort and resistance.

• Future States & AI
• Article

From Measurement to Decision Intelligence: The Future of Marketing Analytics

• ARF Invited Paper by Jim Spaeth and Alice K. Sylvester

Marketing effectiveness analytics is undergoing a profound transformation. As marketers face growing complexity across channels, data sources and consumer journeys, artificial intelligence is accelerating the shift from retrospective measurement toward dynamic, decision-oriented systems. This paper examines a decade of innovation in marketing analytics, highlighting the rise of integrated measurement frameworks, experimentation, machine learning and emerging AI-powered modeling approaches that promise to reshape how organizations understand and optimize marketing performance.

• Future States & AI
• Article

Better Creative, Fewer Tests: A New Framework for Efficient Self-Improving Systems

• ARF; MSI

This MSI working paper introduces TextBO, a novel AI framework designed to improve marketing decisions more efficiently by minimizing costly evaluation cycles. By combining large language models with Bayesian optimization principles, the approach enables AI systems to iteratively refine outputs—such as ad creatives—while requiring fewer real-world tests. The result: faster learning, better-performing outcomes, and a more scalable path to AI-driven decision-making.

• Targets & Segments
• Article

Rethinking AI Adoption: Who’s Really Using AI Shopping Assistants?

• ARF; MSI

As AI assistants become fully embedded within e-commerce platforms, the question becomes: how are consumers actually using them? This MSI working paper analyzes the behavior of over 31 million users on a major travel platform to uncover who adopts shopping AI, when it is used in the purchase journey, and what consumers rely on it for. The findings reveal that AI assistants complement—not replace—traditional search, helping consumers navigate complex, exploratory decisions and reshaping how discovery happens online.