{"id":1121,"date":"2026-03-19T14:04:55","date_gmt":"2026-03-19T14:04:55","guid":{"rendered":"https:\/\/d3.harvard.edu\/future-proof-with-ai\/?p=1121"},"modified":"2026-03-23T20:55:53","modified_gmt":"2026-03-23T20:55:53","slug":"the-power-of-ai-stopping-agents","status":"publish","type":"post","link":"https:\/\/d3.harvard.edu\/future-proof-with-ai\/the-power-of-ai-stopping-agents\/","title":{"rendered":"The Power of AI Stopping Agents"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\"><em>How AI stopping agents create autonomous guardrails<\/em> &#8211; <em>When machine learning meets sales Psychology<\/em><\/h4>\n\n\n\t\t<div class=\"embed-wrapper\">\n\t\t\t<figure class=\"wp-block-embed wp-embed-aspect-16-9 wp-has-aspect-ratio\"> \n\t\t\t\t<div\n\t\t\t\t\tclass=\"rkv-video-placeholder \"\n\t\t\t\t\tstyle=\"background-image:url(https:\/\/i.vimeocdn.com\/video\/2126446526-2790e3e8ad9f6f0e0cbc0e663124a59dbc978a8fbfdb0feabd5d29e20a099a87-d_295x166?region=us);aspect-ratio:16 \/ 9\"\n\t\t\t\t\tdata-provider=\"vimeo\"\n\t\t\t\t\tdata-video-id=\"1156567179\"\n\t\t\t\t><\/div>\n\t\t\t<\/figure>\n\t\n\t\t\t\t\t<\/div>\n\t\t\n\n\n<p>Conventional sales wisdom treats persistence as virtue: stay in the conversation, overcome objections, keep the line alive. But recent research into the dynamics of sales conversations suggests that our bias toward persistence leads to a massive misallocation of resources. In \u201c<a href=\"https:\/\/arxiv.org\/abs\/2511.01181v1\" target=\"_blank\" rel=\"noreferrer noopener\">Learning When to Quit in Sales Conversations<\/a>,\u201d a team including&nbsp;<a href=\"https:\/\/www.hbs.edu\/faculty\/Pages\/profile.aspx?facId=548988\" target=\"_blank\" rel=\"noreferrer noopener\">Eva Ascarza<\/a>, Professor of Business Administration at HBS and co-founder of the&nbsp;<a href=\"https:\/\/d3.harvard.edu\/labs\/customer-intelligence-lab\/\" target=\"_blank\" rel=\"noreferrer noopener\">Customer Intelligence Lab<\/a>&nbsp;at D^3, explain how they built a generative AI \u201cstopping agent\u201d that watches sales transcripts in real time and chooses to quit or wait to maximize cumulative payoff. The result? The ability to lift expected sales by over 30%.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why This Matters<\/strong><\/h2>\n\n\n\n<p>For today\u2019s business leaders, the takeaway is clear: efficiency is not just about doing things faster, it\u2019s about choosing not to do the things that don\u2019t work. In an era where AI is increasingly viewed through the lens of total automation, this research also offers a more sophisticated model. It demonstrates that the most effective use of generative AI isn\u2019t to replace the human salesperson, but to provide them with \u201cdecision support\u201d that corrects for natural psychological biases. This methodology scales beyond sales to any domain with sequential decisions and observable outcomes. The question for leaders isn\u2019t whether their teams face similar cognitive constraints, they almost certainly do, it\u2019s whether they\u2019re ready to systematically identify and correct them.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Bonus<\/strong><\/h3>\n\n\n\n<p>For another use case where AI doesn\u2019t replace humans, but offers the opportunity to improve judgment, break silos, and accelerate execution, check out \u201c<a href=\"https:\/\/d3.harvard.edu\/the-cybernetic-teammate-how-ai-is-reshaping-collaboration-and-expertise-in-the-workplace\/\" target=\"_blank\" rel=\"noreferrer noopener\">The Cybernetic Teammate: How AI is Reshaping Collaboration and Expertise in the Workplace<\/a>.\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>References<\/strong><\/h2>\n\n\n\n<p id=\"ref1\"><a href=\"https:\/\/d3.harvard.edu\/the-power-of-ai-stopping-agents\/#q1\">[1]<\/a>&nbsp;Manzoor, Emaad, Eva Ascarza, and Oded Netzer, \u201cLearning When to Quit in Sales Conversations,\u201d&nbsp;<em>arXiv preprint arXiv:2511.01181<\/em>&nbsp;(2025): 23.&nbsp;<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2511.01181\">https:\/\/doi.org\/10.48550\/arXiv.2511.01181<\/a>. See also&nbsp;<a href=\"https:\/\/stoppingagents.com\/\">https:\/\/stoppingagents.com\/<\/a>.&nbsp;<\/p>\n\n\n\n<p id=\"ref2\"><a href=\"https:\/\/d3.harvard.edu\/the-power-of-ai-stopping-agents\/#q2\">[2]<\/a>&nbsp;Manzoor et al., \u201cLearning When to Quit in Sales Conversations,\u201d 1.<\/p>\n\n\n\n<p id=\"ref3\"><a href=\"https:\/\/d3.harvard.edu\/the-power-of-ai-stopping-agents\/#q3\">[3]<\/a>&nbsp;Manzoor et al., \u201cLearning When to Quit in Sales Conversations,\u201d 2.<\/p>\n\n\n\n<p id=\"ref4\"><a href=\"https:\/\/d3.harvard.edu\/the-power-of-ai-stopping-agents\/#q4\">[4]<\/a>&nbsp;Manzoor et al., \u201cLearning When to Quit in Sales Conversations,\u201d 21.<\/p>\n\n\n\n<p id=\"ref5\"><a href=\"https:\/\/d3.harvard.edu\/the-power-of-ai-stopping-agents\/#q5\">[5]<\/a>&nbsp;Manzoor et al., \u201cLearning When to Quit in Sales Conversations,\u201d 2.<\/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-power-of-ai-stopping-agents\/\" target=\"_blank\" rel=\"noreferrer noopener\">Link&nbsp;to&nbsp;the&nbsp;D^3&nbsp;insight&nbsp;article<\/a><br><a href=\"https:\/\/arxiv.org\/abs\/2511.01181v1\" target=\"_blank\" rel=\"noreferrer noopener\">Link&nbsp;to&nbsp;the&nbsp;research&nbsp;paper<\/a><\/p>\n\n\n\n<p>Sign&nbsp;up&nbsp;for&nbsp;our&nbsp;newsletter&nbsp;to&nbsp;stay&nbsp;up&nbsp;to&nbsp;date&nbsp;with&nbsp;D^3&nbsp;news&nbsp;and&nbsp;research:&nbsp;<a href=\"https:\/\/d3.harvard.edu\/#join-our-community\" rel=\"noreferrer noopener\" target=\"_blank\">https:\/\/d3.harvard.edu\/#join-our-community<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>How AI stopping agents create autonomous guardrails &#8211; When machine learning meets sales Psychology<\/p>\n","protected":false},"author":19325,"featured_media":1123,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_trash_the_other_posts":false,"rkv_hide_featured_image":true,"rkv_hide_page_title":false,"rkv_hide_abstract_shapes":true,"rkv_reduce_header":false,"hbsd3_featured_media_url":"https:\/\/vimeo.com\/1156567179","hbsd3_featured_media_url_autoplay":false,"editor_notices":[],"footnotes":""},"categories":[1],"tags":[],"tax_cop":[],"rkv-people-shadow":[],"insight_type":[],"class_list":["post-1121","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","rkv-featured-image-is-hidden"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The Power of AI Stopping Agents - Future Proof with AI<\/title>\n<meta name=\"description\" content=\"Understand how AI stopping agents act as essential guardrails for autonomous AI systems. 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