How to Scale GenAI in the Workplace
Enthusiasm plateaus. Training gaps. AI shaming. Pharma giant Novo Nordisk faced and beat all these challenges while growing generative AI tool usage.
Carolyn Geason-Beissel/MIT SMR | Getty Images
Novo Nordisk’s generative AI deployment to 20,000 employees has useful insights for companies working to scale their own AI efforts. Its leaders navigated three key challenges: a midcycle enthusiasm dip that can kill GenAI adoption, function-specific needs that demand tailored training, and cultural resistance. Novo Nordisk’s solution combined champion networks, targeted enablement, and adaptive governance — with senior employees serving as unexpected change agents.
As generative AI’s evolution continues, the next challenge for leaders is clear: making GenAI scale and deliver measurable value across their organizations.1
As companies move from experimentation to enterprisewide adoption, many struggle not with the tools themselves but with the organizational transformation required to integrate them meaningfully into people’s daily work. Tools will keep evolving: It is the human side of the equation that determines whether GenAI initiatives truly succeed.
We studied one of the largest real-world generative AI deployments to date — at multinational pharmaceutical company Novo Nordisk. Its experience shows that success hinges not just on infrastructure but on how people think, adapt, and collaborate with AI. One critical lesson: While GenAI adoption and broader digital transformations have common roots, generative AI is uniquely disruptive, reshaping the nature of work itself in unprecedented ways.
Like many organizations, Novo Nordisk began with a familiar expectation: that GenAI would primarily drive productivity.2 Guided by the leadership principle “time is the ultimate currency” and a campaign dubbed “Make Your Time Count,” the company launched its enterprisewide rollout of Microsoft’s Copilot GenAI tool in early 2024, with the goal of saving time and improving efficiency. And in many ways, the company hit that goal.
Each employee saved 2.17 hours per week, on average, once they began using the tool. But something unexpected also happened: Those hours weren’t what employees valued most. Employee satisfaction with Copilot was three times more strongly correlated with perceived improvements in work quality than with time saved. Employees reported quality enhancements in content summarization, content creation, and ideation. Interestingly, many employees reinvested the time they saved into people interactions, strategic planning, and creative work. As one put it, “I can spend more time and energy dedicated to strategizing and planning the rollout of my project.”
This insight challenges a central assumption of many GenAI rollouts: that the main value of the technology lies in raw efficiency. In practice, the promise of generative AI is broader and more human-centered.
Novo Nordisk’s rollout experience, growing from a few hundred Copilot users in January 2024 to 20,000 in February 2025, offers important lessons for leaders who are grappling with scaling GenAI.
References
1. T.H. Davenport and R. Bean, “Five Trends in AI and Data Science for 2025,” MIT Sloan Management Review, April 15, 2025, https://dev02.mitsmr.io.
2. E. Brynjolfsson, D. Li, and L. Raymond, “Generative AI at Work,” The Quarterly Journal of Economics 140, no. 2 (May 2025): 889-942.
3. P. Puranam, “Re-Humanize: How to Build Human-Centric Organizations in the Age of Algorithms” (Singapore: Penguin Random House, 2025).
4. Y. Sun, D. Sheng, Z. Zhou, et al., “AI Hallucination: Towards a Comprehensive Classification of Distorted Information in Artificial Intelligence-Generated Content,” Humanities and Social Sciences Communications 11, no. 1 (September 2024): 1-14.
5. S. Raisch and S. Krakowski, “Artificial Intelligence and Management: The Automation-Augmentation Paradox,” Academy of Management Review 46, no. 1 (January 2021): 192-210.