INSIGHTS / RESEARCH NOTES
We document research on AI, data, behavioral science, and organizations, including methods worth testing in practice. Each note shares not only conclusions, but also hypotheses, observations, and limitations.
69 RESEARCH NOTES
6 / 6 PAGE
Average effects of AI coding are not enough for investment decisions. Research from laboratories, enterprises, and mature open-source projects reveals the work, personnel, and measurement conditions that determine whether development speeds up.
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Even if AI assistance improves individual proposals, if the organization's overall proposals become similar, the exploration range narrows. Based on creativity research, we consider methods to manage quality and collective diversity separately.
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Solving a problem with generative AI is not the same as solving it independently. Drawing on meta-analyses and educational experiments, we examine learning designs that separate supported performance, independent reproduction, and long-term retention.
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A meta-analysis of 106 experiments and management studies helps distinguish extension—outperforming humans alone—from true synergy, where collaboration exceeds the best human or AI performance, and identifies when collaboration is viable.
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We synthesize results from randomized trials and clinical studies involving physicians to clarify the conditions under which generative AI advice improves judgment, and why mere access does not yield effects.
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Data volume and decision quality are not the same. We examine the conditions for a usable data foundation, considering metric meaning, causality versus correlation, update frequency, and on-the-ground context.
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Proper support helps; flawed support amplifies errors. Drawing on research into automation bias and critical thinking, we consider how to build organizations that can question, stop, and revert.
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Distributing tools alone does not build organizational capability. Drawing on field research, this article outlines how to redesign work, learning, expertise, and control.
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Do not equate productivity gains demonstrated by research directly with business value. We propose designing ROI by linking time, quality, accuracy, and usage costs at the business process level.
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