How to Scale GenAI in the Workplace

Authors: Michael Wade, Konstantinos Trantopoulos, Mark Navas, and Anders Romare

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.

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