Many organizations have moved quickly from experimenting with AI to deploying use cases across the enterprise. But while productivity gains are beginning to emerge, far fewer companies have successfully scaled adoption or captured meaningful value. Increasingly, leaders are realizing that the challenge is less about the technology itself and more about workflow redesign, operating models, leadership, and culture. Those AI transformation themes are central to Rewired.
In this “Rewired take”—part of a series exploring how the themes from the book are playing out at companies—Brooke Weddle, a senior partner at McKinsey and a leading contributor to Rewired, explores these themes based on what she is seeing in companies across advanced industries, technology, consumer, and healthcare sectors.
The AI trap: Lots of activity but no value
Most companies have already done something with AI. They’ve experimented with productivity enhancement, they’ve deployed a set of use cases, and they’ve generated excitement. But many are stuck in the experimentation phase.
That’s where Rewired is particularly helpful because it sharpens the reality that not all AI impact is equal. Companies need a much clearer understanding of where value is truly created, which domains matter most, and how to drive scale in a way that captures value.
For example, a CEO at a professional-services player recently told me he was proud of the progress the company had made using AI in business development. They were deploying agents to analyze prior wins and losses and increase the probability of winning future work.
But what struck him most was the opportunity they were leaving on the table. They didn’t have a domain map to guide them to the greatest opportunities for value. They had no clear way to measure progress. And they were trying to do too many things at once.
This is becoming a common realization. Organizations are exploring how to integrate data, technology, and talent simultaneously, but they often need to home in on the few domains where AI can truly transform performance. At some point, experimentation reaches diminishing returns. Our Rewired research is clear that the most successful transformers focus on just one to three domains.
Organizations are exploring how to integrate data, technology, and talent simultaneously, but they often need to home in on the few domains where AI can truly transform performance.
That focus on domains needs to include the elements to allow for scale from the very beginning, something that is often not well appreciated, which is why CFOs are becoming deeply involved in these transformations. The initial cost of adoption is often manageable. The bigger issue is maintenance and scaling over time. The best companies are now building tool kits and operating models that allow them to focus on and sustain AI adoption in areas where value is created rather than treating it as a series of isolated experiments.
Building the ‘nerve center’ for scale
One of the more counterintuitive findings is that most organizations do not want a heavily top-down answer to AI adoption. AI is inherently decentralized. People don’t want to lose empowerment. They don’t want leaders dictating exactly how they should use these tools. The organizations making progress are building business-led road maps instead.
AI fluency becomes critical in that environment. We’re seeing enormous growth in demand for AI-related skills. But adoption has to feel personally valuable to employees. People need to experience the benefit directly in their day-to-day work.
Rewired, Second Edition
This updated edition offers brand-new insights into cutting-edge AI solutions—and what it takes to implement them—as well as the new economics of digital and AI transformations.
Adoption at the ground level, however, needs to be supported and directed, or you get chaos. I often use the analogy of a nerve center. In the human body, the nerve center doesn’t perform every action itself. Instead, it gathers signals from across the system, processes information, coordinates responses, and helps the entire organism learn and adapt. The same principle applies to AI transformation. The teams closest to the work are the ones applying AI in their daily activities, but they need a mechanism that connects learning across the organization and helps successful approaches scale.
In organizations, that nerve center is the combination of business, finance, HR, technology, and operations leaders who help create lightweight but consistent ways of scaling innovation. They provide, for example, common metrics for adoption and clear ways to define measurable outcomes for every AI initiative. Every domain and every experiment should have a clear understanding of what success looks like, including the underpinning economics.
The leading organizations are also becoming much more rigorous about workflow redesign. They’re asking which roles are critical in the new workflow, how should work be reimagined, where should humans stay deeply involved, and how do we become more imaginative about what work could look like.
The leadership imperative: Building space for learning
We’re living through one of the largest cycles of innovation in business history. Companies need to actively tap into that energy while also providing structure to channel that energy effectively. That’s a leadership challenge.
We’re living through one of the largest cycles of innovation in business history. Companies need to actively tap into that energy while also providing structure to channel that energy effectively.
There’s a constant hum of stress right now. People are balancing that optimism against stress around upskilling and reskilling and a broader sense of instability in the external environment. Leaders have to acknowledge the anxiety directly while also helping employees see how AI can unlock human potential and create new ways of working.
I’m working with a leading services provider where a large portion of the transformation agenda is now AI-driven. Leaders are talking about moon shots and what’s possible, and employees respond very positively to that aspiration.
Similarly, one of the most powerful things leaders can do is create space for learning. They’re capturing experiments in smart ways and celebrating both what worked and what didn’t.
The emphasis is not only on technical capability. It’s on behaviors. Leaders are reinforcing the importance of healthy friction in debate because organizations have to invite friction in order to learn. They’re emphasizing courage, continuous learning, and selfless leadership as core leadership behaviors.
The goal is not to tell people how to use AI. It’s to create enough structure so that innovators across the organization remain connected, can learn from one another, and can scale what works. One leader at a consumer company who is driving AI transformation across the organization described how they’re creating stories for every major experiment—documenting what was learned and connecting those lessons back to their culture of innovation and customer focus. That’s powerful because it normalizes learning.
A great deal of organizational learning is ultimately behavioral. The leaders who succeed are the ones creating the conditions where people feel safe experimenting, apprenticing others, and learning in public.
The leaders who succeed are the ones creating the conditions where people feel safe experimenting, apprenticing others, and learning in public.
Culture as a competitive advantage
Books like The Geek Way (Little Brown, 2023) describe organizations that were designed around a culture of experimentation, speed, and constant iteration. Those companies are continuing to evolve their cultures even further.
But the broader point is that speed itself is no longer just an AI phenomenon. It’s becoming a defining organizational capability. If you define culture as the values of the company—and the behaviors and mindsets that support employees and leaders—then AI transformation becomes deeply cultural and personal. That last part is particularly important.
I saw this firsthand with an industrial client transforming scheduling through AI. These were frontline workers who had been working in the sector for decades. They had deeply entrenched routines and enormous pride in their work. The transformation required highly specific work at the persona level. What does the foreman, for example, do differently now? Historically, maybe that person called a supervisor because they needed help solving a problem. Now the AI system can generate multiple options and help create the plan. The challenge wasn’t just proving that the tool worked. Leaders also had to build trust with AI systems but also create space for social interaction between people.
It’s about people
We often say that an AI transformation is really a people transformation. Many organizations are focused on adoption today. But eventually the winners will be the organizations that are the fastest to understand how humans reach their full potential in an AI-enabled environment.
Eventually the winners will be the organizations that are the fastest to understand how humans reach their full potential in an AI-enabled environment.
That’s a very different framing. The leading organizations are positioning AI as something that enhances people. They acknowledge that some jobs will go away, but they also believe many more opportunities will emerge.
That’s the abundance mindset but delivered in a way that connects to competitive advantage, not pure altruism. I hear leaders say things like, “Our people are our competitive advantage.” At one consumer company, leaders talk constantly about being “human first” because the company is fundamentally about supporting families and communities.
The companies that succeed are helping employees feel that they are participating in one of the most innovative moments in history and that they have an opportunity to shape the future rather than simply react to it. Organizations cannot meet the moment with technology alone.
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