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According to recent @StatistaCharts Consumer Insights survey, getting personal advice or guidance is by far the most popular AI use case related to personal concerns and mental well-being, cited by 27% of U.S. respondents; one in five reported turning to AI tools specifically to reduce stress or anxiety, and 19% use them to learn more about mental health

Bringing brand and performance closer requires some changes in the AI era: Treating brand and analytics as equal. Integrators are more likely to inject creativity in analytic functions and vice versa, especially when it comes to consumer insights and customer experience. In the AI era, this means leveraging predictive AI to turn raw consumer data into proactive creative briefs. Linking teams with an agile marketing operating model. This means having brand and digital marketers work from the brief through all execution. By integrating GenAI workflows, teams can rapidly co-create, test, and scale asset variations while maintaining strict brand guardrails. Defining clear, unified KPIs linked across the funnel. This includes analyzing and quantifying the impact of non-conversion events such as points of awareness, increased and qualified traffic generation. Investing in an AI-powered measurement ecosystem. Moving beyond traditional 'brand lift' studies to regular read-and-react geo-specific attribution, utilizing machine learning to run continuous, automated cross-channel simulations. An updated media mix model (MMM) for integrated spending. Including a pressure-tested, AI-driven MMM based on real-time incrementality tests in market to dynamically reallocate budget between brand and performance. Before undertaking all of the above, marketers should try a few AI-assisted pilot tests to quickly demonstrate the power and increased effectiveness when these muscles work together.