Making AI Work: Leadership, Lab, and Crowd

Companies are approaching AI transformation with incomplete information. After extensive conversations with organizations across industries, I think four key facts explain what's really happening with AI adoption: AI boosts work performance. How do we know? For one thing, workers certainly think it does. A representative study of knowledge workers in Denmark found that users thought that AI halved their working time for 41% of the tasks they do at work, and a more recent survey of Americans found that workers said using AI tripled their productivity (reducing 90-minute tasks to 30 minutes). Self-reporting is never completely accurate, but we have other data from controlled experiments that suggest gains among product development, sales, and consulting, as well as for coders, law students, and call center workers. A large percentage of people are using AI at work. That Danish study from a year ago found that 65% of marketers, 64% of journalists, and 30% of lawyers, among others, had used AI at work. The study of American workers found over 30% had used AI at work in December, 2024, a number which grew to 40% in April, 2025. And, of course, this may be an undercount in a world where ChatGPT is the fourth most visited website on the planet. There are more transformational gains available with today’s AI systems than most currently realize. Deep research reports do many hours of analytical work in a few minutes (and I have been told by many researchers that checking these reports is much faster than writing them); agents are just starting to appear that can do real work; and increasingly smart systems can produce really high-quality outcomes. These gains are not being captured by companies. Companies are typically reporting small to moderate gains from AI so far, and there is no major impact on wages or hours worked as of the end of 2024. How do we reconcile the first three points with the final one? The answer is that AI use that boosts individual performance does not naturally translate to improving organizational performance. To get organizational gains requires organizational innovation, rethinking incentives, processes, and even the nature of work. But the muscles for organizational innovation inside companies have atrophied. For decades, companies have outsourced this to consultants or enterprise software vendors who develop generalized approaches that address the issues of many companies at once. That won’t work here, at least for a while. Nobody has special information about how to best use AI at your company, or a playbook for how to integrate it into your organization. Even the major AI companies release models without knowing how they can be best used. They especially don’t know your industry, organization, or context. We are all figuring this out together. So, if you want to gain an advantage, you are going to have to figure it out faster than everyone else. And to do that, you will need to harness the efforts of Leadership, Lab, and Crowd - the three keys to AI transformation. Ultimately, AI starts as a leadership problem, where leaders recognize that AI presents urgent challenges and opportunities. One big change since I wrote about this topic months ago is that more leaders are starting to recognize the need to address AI. You can see this in two viral memos, from the CEO of Shopify and the CEO of Duolingo, establishing the importance of AI to their company’s future. But urgency alone isn't enough. These messages do a good job signaling the 'why now' but stop short of painting that crucial, vivid picture: what does the AI-powered future actually look and feel like for your organization? My colleague Andrew Carton has shown that workers are not motivated to change by leadership statements about performance gains or bottom lines, they want clear and vivid images of what the future actually looks like: What will work be like in the future? Will efficiency gains be translated into layoffs or will they be used to grow the organization? How will workers be rewarded (or punished) for how they use AI? You don’t have to know the answer with certainty, but you should have a goal that you are working towards that you are willing to share. Workers are waiting for guidance, and the nature of that guidance will impact how The Crowd adopts and uses AI. An overall vision is not enough, however, because leaders need to start to anticipate how work will change in a world of AI. While AI is not currently a replacement for most human jobs, it does replace specific tasks within those jobs. I have spoken to numerous legal professionals who see the current state of Deep Research tools as good enough to handle portions of once-expensive research tasks. Vibe coding changes how programmers allocate time and effort. And it is hard to not see changes to marketing and media work in the rapid gains in AI video. For example, Google’s new Veo 3 created this short video snippet, sound and all, from the text prompt: An advertisement for Cheesey Otters, a new snack made out of otter shaped crackers. The commercial shows a kid eating them, and the mom holds up the package and says "otterly great" Yet the ability to make a short video clip, or code faster, or get research on demand, does not equal performance gains. To do that will require decisions about where Leadership and The Lab should work together to build and test new workflows that integrate AIs and humans. It also means fundamentally rethinking why you are doing particular tasks. Companies used to pay tens of thousands of dollars for a single research report, now they can generate hundreds of those for free. What does that allow your analysts and managers to do? If hundreds of reports aren’t useful, then what was the point of research reports? I am increasingly seeing organizations start to experiment with radical new approaches to work in response to AI. For example, dispersing software engineering teams, removing them from a central IT function and instead having them work in cross-functional teams with subject matter experts and marketing experts. Together, these groups can “vibework” and independently build projects in days that would have taken months of coordination across departments. And this is just one possible future for work. Leaders need to describe the future they want, but they also don’t have to generate every idea for innovation on their own. Instead, they can turn to The Crowd and The Lab. Both innovation and performance improvements happen in The Crowd, the employees who figure out how to use AI to help get their own work done. As there is no instruction manual for AI (seriously, everyone is figuring this out together), learning to use AI well is a process of discovery that benefits experienced workers. People with a strong understanding of their job can easily assess when an AI is useful for their work through trial and error, in the way that outsiders (and even AI-savvy junior workers) cannot. Experienced AI users can then share their workflows and AI use in ways that benefit everyone. Enticed by this vision, companies (including those in highly regulated industries1) have increasingly been giving employees direct access to AI chatbots, and some basic training, in hopes of seeing The Crowd innovate. Most run into the same problem, finding that the use of official AI chatbots maxes out at 20% or so of workers, and that reported productivity gains are small. Yet over 40% of workers admit using AI at work, and they are privately reporting large performance gains. This discrepancy points to two critical dynamics: many workers are hiding their AI use, often for good reason, while others remain unsure how to effectively apply AI to their tasks, despite initial training. These are problems that can be solved by Leadership and the Lab. Solving the problem of hidden AI use (what I call “Secret Cyborgs”) is a Leadership problem. Consider the incentives of the average worker. They may have received a sca…