{"id":1327,"date":"2026-05-07T12:53:28","date_gmt":"2026-05-07T12:53:28","guid":{"rendered":"https:\/\/d3.harvard.edu\/future-proof-with-ai\/?p=1327"},"modified":"2026-05-07T12:53:29","modified_gmt":"2026-05-07T12:53:29","slug":"the-real-ai-divide-isnt-who-uses-it-most","status":"publish","type":"post","link":"https:\/\/d3.harvard.edu\/future-proof-with-ai\/the-real-ai-divide-isnt-who-uses-it-most\/","title":{"rendered":"The Real AI Divide Isn\u2019t Who Uses It Most"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\"><em>Research shows that giving AI to everyone doesn\u2019t help everyone<\/em><\/h3>\n\n\n\n<p>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\u2019s 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\u2019s 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\u2019re revisiting some important scholarship from the institute\u2019s first three years. In \u201c<a href=\"https:\/\/dx.doi.org\/10.2139\/ssrn.4671369\">The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya<\/a>,\u201d 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.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why This Matters<\/strong><\/h2>\n\n\n\n<p>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.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><a href=\"https:\/\/d3.harvard.edu\/the-real-ai-divide-isnt-who-uses-it-most\/\">Link to the HBS AI Institute Insight Article<\/a><br><a href=\"https:\/\/dx.doi.org\/10.2139\/ssrn.4671369\">Link to the Research Paper<\/a><br><a href=\"https:\/\/d3.harvard.edu\/#join-our-community\">Sign up for our newsletter to stay up to date with HBS AI Institute news and research<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research shows that giving AI to everyone doesn\u2019t help everyone; AI Divide Colleagues Research<\/p>\n","protected":false},"author":19452,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_trash_the_other_posts":false,"rkv_hide_featured_image":false,"rkv_hide_page_title":false,"rkv_hide_abstract_shapes":true,"rkv_reduce_header":false,"hbsd3_featured_media_url":"https:\/\/vimeo.com\/1189777599","hbsd3_featured_media_url_autoplay":false,"editor_notices":[],"footnotes":""},"categories":[1],"tags":[],"tax_cop":[],"rkv-people-shadow":[8],"insight_type":[],"class_list":["post-1327","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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