Research shows that giving AI to everyone doesn’t help everyone
Lately, you may have noticed some colleagues seem to use AI for everything. They ask it to sharpen emails, produce meeting notes, brainstorm strategy, and pressure-test campaigns. Watching them, it’s tempting to think the advantage lies in sheer volume: maybe the people getting the most from AI are simply the ones using it more often. But in the back of your mind, you might be thinking about the human-made business decisions happening in the space between the AI’s output and work in the real world. If AI provides us with five ideas, and we pick the wrong one, are we better off than if we had no AI at all? This dynamic sits at the heart of research from early in the generative AI era. As the Digital Data Design Institute at Harvard (D^3) becomes the Harvard Business School AI Institute, we’re revisiting some important scholarship from the institute’s first three years. In “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” a research team including HBS AI Institute PI Rembrand Koning followed over 300 entrepreneurs in Kenya as they integrated AI into their daily operations. They discovered that the impact of AI is far from uniform.
Why This Matters
For business leaders and executives, this research is an important reminder that using AI is not enough. If AI can produce both strong and weak recommendations from the same prompt and conversation, then advantage will come from knowing how to distinguish between good and bad advice, how to test it, and how to translate good advice into action. This has concrete implications for how companies design AI-assisted workflows, how they train people to work with AI tools, and how they measure whether AI is delivering real results or just superfluous activity.
Link to the HBS AI Institute Insight Article
Link to the Research Paper
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