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McKinsey’s AI effort is more than a company chatbot rollout. The firm describes a broader transformation built around Lilli, its internal generative AI platform: changing how employees find and create knowledge, redesigning workflows, and organizing teams to make AI useful in day-to-day work. McKinsey reports high adoption and time savings on certain knowledge tasks, but those figures are company-reported—not independently verified evidence of firm-wide productivity gains or financial return.
What McKinsey built: Lilli, an internal AI platform
Lilli is McKinsey’s internal generative AI platform. The firm says it began as a knowledge-management tool and expanded to support other work. McKinsey describes employees using it for research and synthesis; testers also saw potential applications in data analysis, planning, and creative problem-solving. The firm says its QuantumBlack colleagues have adapted the underlying architecture for client-specific work. These are McKinsey’s descriptions, not independent evaluations of the platform. McKinsey’s Lilli case study
Rodney Zemmel, who led McKinsey’s AI transformation, said the firm chose to build its own capabilities rather than adopt an off-the-shelf product. He described Lilli as combining proprietary and public information, with the aim of protecting data while improving access to knowledge. In an October 2024 McKinsey podcast, he said: “We chose to build our own AI capabilities rather than take something preexisting off the shelf.”
How the rollout changed the way the firm works
Start small, learn, then expand
Erik Roth said Lilli’s initial rollout was deliberately limited to about 2,500 colleagues. McKinsey then expanded access in waves, using alpha and beta groups and an experimental research effort called LilliX to learn from users before broadening development. Roth also cautioned that models can appear to reason without reasoning in the human sense. His interview presents experimentation and restraint as part of the build process, rather than treating early user interest as proof the platform was ready for every task.
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Connect technology to business ownership
McKinsey says the transformation followed a business-led roadmap with delegated accountability and an agile operating model. The case study describes teams and parts of the firm that had not previously collaborated closely working together. The stated aim was to change how the firm manages and accesses expertise—not simply to add another piece of software. In Zemmel’s words, “Our business-led roadmap isn’t about building a tool; it’s about transforming how we manage and access knowledge and expertise, which is the foundation of our organization and competitive advantage.” McKinsey’s case study
Make adoption part of the work
McKinsey’s leaders emphasize workflow fit, user education, skill development, and internal communities as necessary to sustained use. Zemmel distinguished trying a tool once from building regular habits: “It’s relatively easy to get to trial. People are curious, and they’ll try it once. But to get persistent usage, again, it’s back to the Rewired recipe of really thinking through the user journey and being front-line-centric on how it’s going to be used, so it’s not just a one-off but is embedded into how users do their work week to week.”
The firm also acknowledges that adoption remains a work in progress; its case study says regular engagement needed further development. That caveat matters: access and initial curiosity do not automatically translate into lasting workflow change. Zemmel’s October 2024 discussion connects the approach to Rewired, McKinsey’s 2023 Wiley book on digital and AI transformation.
What McKinsey reports—and what the numbers do not establish
McKinsey’s undated case study page, accessed October 8, 2026, reports that 72 percent of the firm is “active” on Lilli, that users can save up to 30 percent of the time spent searching and synthesizing knowledge, and that the platform receives more than 500,000 prompts each month. The page’s retrieved text does not define “active” or provide an independent audit. The time-saving claim is an “up to” figure for particular knowledge tasks, not a measure of time saved by every employee or of total workforce productivity. Source: McKinsey’s Lilli case study
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →In the October 14, 2024 podcast, Zemmel said more than 70 percent of colleagues were using Lilli regularly at that point. That is a separate, earlier description: “regularly” should not be treated as interchangeable with the case study’s later “active” measure. Source: McKinsey podcast transcript
Taken together, the figures support the narrower conclusion that McKinsey reports substantial platform use and task-level benefits. The available sources do not establish an audited financial return, show that Lilli caused firm-wide gains, or demonstrate that all employees save the same amount of time. Usage volume, task-level time savings, and business value are different measures.
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Why McKinsey says the approach is working
The firm’s explanation centers on organizational choices alongside the technology: a defined business goal, a platform built around internal knowledge, cross-functional accountability, iterative testing, and attention to employee adoption and skills. The underlying idea is that a model creates value when it is connected to a real task and repeatedly used in a workflow—not merely made available.
In its October 2024 discussion of the broader Rewired framework, McKinsey warned against scattered pilots that never reach business scale. Zemmel advised leaders to choose a domain, select a consequential problem, and set a genuine business target. He also noted that productivity is difficult to capture because benefits emerge task by task, so organizations need to identify where value actually occurs. This is McKinsey’s guidance and interpretation, not proof of a universal formula or a guarantee that another organization will get the same results. He summed up the challenge this way: “There’s no question that it’s proven harder than the hype to capture value.” McKinsey podcast transcript, October 14, 2024
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