As consumers add AI-powered search engines to their information routines, their behavior changes with the platform and purpose of the search. An observational study of 848 real-world searches found that traditional search still dominated, while AI-powered search was used more often for advice-seeking. It drew substantially longer queries and produced fewer outbound clicks. The findings suggest that advertisers may need to rethink long-tail keyword strategies, website optimization and ad models as search behavior becomes divided across traditional and generative platforms.
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AI is transforming how organizations generate insights and forecast outcomes, but questions remain about reliability and the need for human oversight. On September 14-15 in NYC, industry and academic leaders gathered to discuss integrating AI into analytics workflows and maintaining transparency.
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As AI becomes more embedded in marketing, organizations face ethical and regulatory questions alongside implementation challenges. Drawing on a synthesis of 35 high-quality qualitative research sources, this MSI working paper proposes a “Responsible AI Marketing Framework,” centered on four pillars: transparency by design, consent architecture, algorithmic accountability and consumer empowerment.
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On September 10, DATA POEM introduced interconnected, non-linear optimization using Large Causal Models, which enable real-time, granular decision-making across all marketing levers and market scenarios. This session was designed for leaders who suspect their current optimization framework is leaving significant value on the table—and want to understand what the architectural alternative looks like in practice.
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