{"id":8229,"date":"2026-05-12T15:38:12","date_gmt":"2026-05-12T15:38:12","guid":{"rendered":"https:\/\/www.version1.com\/en-us\/?p=8229"},"modified":"2026-08-21T14:28:37","modified_gmt":"2026-08-21T14:28:37","slug":"five-things-a-pharma-coo-taught-me-about-making-ai-work-at-scale","status":"publish","type":"post","link":"https:\/\/www.version1.com\/en-us\/five-things-a-pharma-coo-taught-me-about-making-ai-work-at-scale\/","title":{"rendered":"Five things a pharma COO taught me about making AI\u00a0work at scale\u00a0"},"content":{"rendered":"<h2>Leadership lessons from the front lines of AI transformation<\/h2>\n<div><\/div>\n<p>Recently, I hosted a fireside chat at Version 1\u2019s Women in Tech Leadership event in Princeton, New Jersey with Rakhi Agarwal, Chief Operating and Procurement Officer at\u00a0VCVX\u00a0Holdings. Rakhi has spent 25 years across Bristol Myers Squibb (BMS), Merck, Johnson &amp; Johnson, and Sanofi. She was brilliantly candid about what organisations get wrong with <a href=\"https:\/\/www.version1.com\/ai-strategy-and-implementation\/\">AI<\/a>, and what the best leaders do differently. <\/p>\n<p><strong>Here are the five takeaways\u00a0I\u2019m already using in conversations with technology decision-makers:\u00a0<\/strong><\/p>\n<ol>\n<li><b>Get honest about your data (really honest).<\/b>The fastest way to stall an AI programme is pretending your data is in better shape than it is. Rakhi put it bluntly: \u201cIf you are in denial, AI will never work.\u201d Before anyone invests in a shiny new tool, you need a clear view of the problem you\u2019re trying to solve, what outcome you want, and what data and processes actually exist today. It\u2019s not glamorous work, but it\u2019s the work that makes everything else possible.\u00a0<\/li>\n<li><b> Standardise the process and put governance in place, or don\u2019t bother.<\/b>\u00a0Across Rakhi\u2019s career, the two most consistent blockers have been (1) non-standard, non-repeatable processes and (2) weak governance. AI dropped into a messy, inconsistent environment\u00a0can\u2019t\u00a0scale. Without clear checkpoints and feedback loops, even\u00a0a good solution\u00a0drifts away from the original intent. If\u00a0you\u2019re\u00a0a CIO\/CTO (or you sit with the teams who feel that pressure), this is your reminder that process and governance\u00a0aren\u2019t\u00a0\u201cphase two\u201d.\u00a0They are the runway.\u00a0<\/li>\n<li><b> Invest in skills, not just tools. <\/b>Rakhi shared an analogy that made everyone smile, then immediately think. Her teenage children can drive a Tesla safely\u2026 until the system fails. At that point, they need the fundamentals. It\u2019s the same with AI at work: if teams can\u2019t challenge outputs, spot errors, or understand the context behind a recommendation, the tool becomes a risk. Upskilling and enablement aren\u2019t \u201cnice to have\u201d. They turn an AI purchase into an AI capability.\u00a0<\/li>\n<li><b> Trust comes from transparency (and clear ownership). <\/b>Adoption won\u2019t stick if only a few people understand what the AI is doing and why. Rakhi\u2019s advice was practical: define ownership properly (a clear RACI helps), map escalation paths, and be transparent about what data is going in and what results are coming out. As she said, \u201cIf trust is there, that\u2019s when AI will be helpful, because even when you are challenging it, you are challenging it mindfully.\u201d\u00a0<\/li>\n<li><b> Sometimes the best answer isn\u2019t AI.<\/b>\u00a0This one landed with a lot of leaders in the room. Not every problem needs AI, and in plenty of cases a simpler automation approach is faster, cheaper, and more reliable. Being clear on the difference between AI, automation, and machine learning can save teams from overspending (or building\u00a0complexity they\u00a0don\u2019t\u00a0need). If\u00a0you\u2019re\u00a0feeling the internal pressure to \u201cdo something with AI\u201d,\u00a0this is a good reset question:\u00a0<i>what\u2019s\u00a0the simplest solution that delivers the outcome?<\/i><br \/>\n<i><\/i><i><\/i><\/li>\n<\/ol>\n<p><i>These insights come from my conversation with Rakhi Agarwal (VCVX Holdings) at Version 1\u2019s Women in Tech Leadership event in Princeton, New Jersey (March 2026).<\/i>\u00a0<\/p>\n<p><i>If\u00a0you\u2019re\u00a0exploring AI in pharma,\u00a0medtech, or life sciences and want to pressure-test readiness (data, governance, skills, and the \u2018do we even need AI?\u2019 question),\u00a0I\u2019m\u00a0always happy to compare notes\u00a0\u2013 you can\u00a0connect with me here on\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/rebeccapedley\/\">LinkedIn.\u00a0<\/a><\/i>\u00a0<\/p>\n<p>If the challenges around AI adoption in pharma feel familiar, <a href=\"https:\/\/www.version1.com\/blog\/you-have-to-come-out-of-denial-what-25-years-in-pharma-taught-one-leader-about-making-ai-actually-work\/\">this longer\u2011form piece goes deeper.<\/a> Drawing on a candid fireside conversation with Rakhi Agarwal, it explores what 25 years across BMS, Merck, J&amp;J, Sanofi, and the CDMO space reveal about why AI initiatives stall\u2014and what leaders who make it work actually do differently.<\/p>\n<p>From data honesty and governance to skills, ownership, and trust, the article offers practical leadership lessons for anyone trying to move AI beyond pilots and into real operating models.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Leadership lessons from the front lines of AI transformation Recently, I hosted a fireside chat at Version 1\u2019s Women in Tech Leadership event in Princeton, New Jersey with Rakhi Agarwal, Chief Operating and Procurement Officer at\u00a0VCVX\u00a0Holdings. Rakhi has spent 25 years across Bristol Myers Squibb (BMS), Merck, Johnson &amp; Johnson, and Sanofi. She was brilliantly [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":8230,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[101],"tags":[],"industry":[75,97],"class_list":["post-8229","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","industry-pharmaceuticals-health-and-life-sciences","industry-private-sector"],"acf":[],"translations":[{"blog_id":1,"post_id":8229,"name":"Europe","code":"EN-GB","hreflang":"en-gb","url":"https:\/\/www.version1.com\/blog\/five-things-a-pharma-coo-taught-me-about-making-ai-work-at-scale\/","is_current":false},{"blog_id":13,"post_id":8229,"name":"Americas","code":"EN-US","hreflang":"en-us","url":"https:\/\/www.version1.com\/en-us\/five-things-a-pharma-coo-taught-me-about-making-ai-work-at-scale\/","is_current":true}],"yoast_head":"<!-- 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