AI has made it easier than ever to launch a survey and to get confidently wrong answers. Businesses are asking customers direct questions that a chatbot suggested, trusting DIY panels of unknown quality, and steering major decisions with a single score. The result is data that looks clean, reassures everyone, and quietly points the wrong way. In this session, SMARI pulls back the curtain on why feedback data deceives through leading questions, unreliable audiences, comparative bias, and metrics that flatter rather than inform, and shows leaders how to tell good data from bad before it costs them. Because people aren’t rational, the way you ask determines what you learn.

Key Take-Aways

• Ask the question behind the question: Why customers can’t reliably tell you what they want, and how trade-off and choice-based thinking surfaces what they’ll actually do, so you can stop getting aspirational answers to literal questions

• A field guide to data you can trust: How to spot the red flags in DIY and AI-built research, such as leading questions, mystery audiences, false positives, and the difference between a number that reassures and a number that predicts, plus when DIY feedback is genuinely good enough.

• Retire the metrics that mislead: Why single-score measures can give a dangerously rosy picture, and what to watch instead so your data drives sound decisions rather than confident mistakes.

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