As the United States moves past its 250th anniversary, artificial intelligence has emerged as one of the defining technologies of the next era of economic growth. Throughout American history, sustained economic growth has depended on broad skill shifts. The industrial era required workers who could operate increasingly sophisticated machinery. The information age demanded digital literacy across nearly every occupation. At every transition, the US economy successfully adapted and built a new economic edge.
Today, AI fluency is rapidly becoming the common language of work and a prerequisite for the next chapter of competitiveness. Workers’ practical ability to use and manage AI in their day-to-day, integrate it into workflows, evaluate its outputs critically, and, increasingly, create with it is transforming how work gets done. Unlike many earlier technological capabilities (such as cloud computing) that were relevant mainly to specialists, the demand for AI fluency is spreading across all types of workers, industries, and wage groups. What began as a technical skill is becoming a must-have capability for knowledge workers, blue-collar workers, managers, students, and everyday citizens.
This shift matters because AI’s economic potential will not be realized through technology alone. The largest gains will come when organizations redesign how work gets done—creating new forms of collaboration among people, intelligent agents, and physical automation. AI fluency is the lingua franca of that transition. As more companies and organizations successfully build fluency, the broader US economy will also gain—indeed almost every national economy could benefit from developing this skill in its people. In the following, we examine the fundamentals of fluency, its direct connection to productivity and value, the steps companies are taking to build it, its economy-wide effects, and the collaboration needed to build momentum.
From digital literacy to AI fluency
The spread of digital literacy has transformed the workforce over the past three decades. It may be hard to remember, but skills such as word processing, spreadsheet manipulation, and web design were once the province of specialists. Today, they are baseline expectations across nearly every occupation. AI fluency seems all but certain to follow a similar path—only much faster.
At its core, AI fluency is knowing when and how to use AI to achieve better outcomes—and then, once work is delegated to AI, knowing how to verify and improve the results. Critically, AI fluency also means strengthening the distinctly human capabilities that become more valuable as AI becomes more capable: framing the right problems, exercising judgment under uncertainty, synthesizing ideas into compelling narratives, understanding other people, and knowing when to challenge or override AI-generated outputs. Taken together, AI fluency combines practical AI capabilities with the human-centered practices—such as sound judgment, adaptability, collaboration, continuous learning, and responsible decision-making—that are needed to apply AI effectively. Like digital literacy before it, AI fluency is fast becoming a foundational capability that amplifies the expertise workers already possess, enabling them to do more—faster and better—when harnessed correctly.
As AI evolves, so too does fluency in its use. Early discussions focused on the ability to use AI tools effectively and manage them responsibly. Today, the definition is already expanding to include creating and developing with AI. New capabilities such as building applications through natural-language prompts, orchestrating AI agents, and even “vibe coding” are quickly becoming part of how work gets done.
Demand for AI fluency skills is almost 14 times higher than it was three years ago (exhibit). Few if any workforce needs have evolved and spread at this velocity.1 While demand remains concentrated in technical and business occupations, it is already expanding into fields ranging from engineering and skilled trades to education. It’s apparent that AI fluency is not a fixed skill set, but a rapidly evolving capability that is becoming foundational across the entire workforce.
Why AI fluency is the key to value capture
AI-powered agents and robots could unlock an estimated $2.9 trillion in annual value for the US economy by 2030.2 But as companies are learning, capturing that value requires more than deploying new technologies. It will require organizations to rethink how work fundamentally gets done.
Organizations will create value from AI in different ways. Some value comes horizontally, by enabling employees across the organization to use AI to improve their own productivity. Additional value comes vertically, by redesigning domain-specific workflows and building AI-enabled solutions tailored to particular functions or business processes.
The biggest gains from AI are unlikely to come from the first category, automating tasks one by one. Companies are already noticing that, while the majority of workers are using the tools productively, enterprise value is lost in the ether. Instead, the biggest gains will come from redesigning workflows around collaboration between people, agents, and robots. For most of the past century, organizations were designed around humans performing work, making decisions, and coordinating with one another. Technology was, at most, a tool that supported those activities. However, work will increasingly be carried out by teams that combine human judgment with machine intelligence. The challenge is not simply adopting AI—it is learning how to orchestrate these new forms of collaboration effectively.
That is where AI fluency becomes essential. AI fluency is the skill that helps workers determine what to hand off to an AI agent, what to verify before acting on a recommendation, what to escalate to a human decision-maker, and where human judgment, creativity, or accountability must remain in the loop. Ninety-four percent of employees are familiar with the tools, and many are already using AI effectively in their own work.3 Now, AI-fluent managers, champions, and advanced users will also redesign workflows and help their teams build new ways of working around AI. AI fluency will be the new language of coordination inside organizations.
A moving target
Some skills are acquired in an afternoon. AI fluency is not one of them. Instead, fluency gets built through continuous development. In that way, it’s similar to the professional knowledge that practitioners in many fields—such as medicine, law, education, engineering, and science—regularly update as regulations, technologies, and best practices evolve. What’s different is that AI fluency is new, developing at an even faster pace, and required by a much broader share of the workforce. The models keep improving, the interfaces keep changing, and new use cases emerge almost weekly. The challenge is not, or not only, achieving mastery in the traditional sense, but building the habit of continually adapting as the technology evolves. The goal is to coach the habit, not teach the tool.
This requires a different approach to skill building. Rather than treating AI fluency as a one-time skilling exercise, organizations may need to establish continuous learning loops that regularly update AI usage expectations, share emerging practices, and help employees incorporate new tools into their daily work. An ecosystem of external partners can help keep learning content current as the technology evolves. In practice, people do not become AI fluent by learning about AI; they do it by using AI as part of their everyday work. The focus should not be rote AI training as a box to tick, but continual improvement in understanding and using AI.
Training is only one component of encouraging AI adoption and should be complemented by broader change-management efforts that reinforce new behaviors, workflows, and ways of working. Reshaping the culture is essential to create psychological safety for experimentation and failure. Several practical actions can help push the organization in the direction of fluency. Adopting a “lighthouse” approach is one method,4 which involves selecting a few strong AI-forward teams to model the change for the wider organization. These teams have a notable record of AI impact, strong AI-enabled leadership, engaged AI champions, and compelling use cases. In practice, lighthouse teams have created both immediate business impact and lasting capabilities. Team members significantly increased their AI proficiency and continued using AI months after the engagement, while sharing successful workflows and practices that encouraged other teams across the organization to adopt and experiment with AI.
Another method is peer-based learning, through platforms for peer-to-peer coaching, knowledge sharing, and collaborative problem-solving. At McKinsey, we have put this into practice through our AI Fellows program, which equips a network of consultants with advanced AI capabilities and empowers them to act as peer coaches, helping colleagues adopt new tools and ways of working in their day-to-day activities. By embedding these AI fellows within teams, organizations can accelerate AI adoption, build confidence through hands-on learning, and spread successful practices organically across the business.
Building AI fluency is not just for frontline employees. Managers increasingly need to coach teams on effective AI usage, review AI-generated outputs, and redesign workflows as new capabilities emerge and organizational structures shift. Leaders face an additional challenge: balancing AI fluency with adaptive leadership. As AI changes how work is performed, leaders must help organizations navigate ambiguity, build trust, encourage experimentation, and make sound judgments about where human oversight remains essential. AI fluency and human-centered leadership are complementary rather than competing traits.
AI fluency as portable human capital
AI fluency will do more than help people perform better in the job they have today—it will also help them adapt to the jobs they may have tomorrow.
The US labor market is entering a significant transition. Technological change, demographic shifts, and evolving business needs will likely reshape demand across occupations, requiring millions of workers to move into new roles over the coming decade. As many as 12 million occupational transitions may be needed by 2030, with lower-wage workers and women facing a disproportionately high likelihood of needing to change occupations.5
In this environment, AI fluency can become a uniquely valuable form of human capital because it is transferable across occupations. Unlike many technical skills that are tied to a specific role or industry, AI fluency equips workers with the ability to learn new tools quickly, collaborate effectively with intelligent systems, and rethink how work is done. These are skills that remain valuable even as jobs, technologies, and industries evolve.
That portability is likely to become increasingly important as careers become less linear. Today’s workers are less likely to use the same technologies, or even remain in the same occupations, throughout their working lives. The ability to learn and work effectively alongside intelligent systems will become a source of resilience, helping workers navigate transitions into adjacent roles and emerging occupations with greater confidence.
For employers, investing in AI fluency is about more than helping employees perform better today. It is about increasing the adaptability of the entire workforce. As careers become longer and less linear, one of the greatest competitive advantages may be a workforce that can continuously learn, reinvent itself, and move where new opportunities emerge. In that sense, AI fluency is not simply a workforce capability—one that every organization needs for itself as roles and skills morph—but also a catalyst for economic mobility.
Making it happen
As AI fluency becomes a prerequisite for US economic competitiveness, a critical question arises: Who is responsible for building it?
The answer is unlikely to be any single institution. AI fluency is not a skill that flows from the top down. It starts with individual experimentation, grows through teams and organizations, and is reinforced by the institutions—commercial, academic, and governmental—that help workers learn, earn credentials, and transition throughout their careers. The result is less a training program than a living ecosystem—one that must evolve as quickly as the technology it supports.
Individuals have a responsibility to stay curious, experiment with new tools, and continuously update how they work. But individual effort alone is not enough. Companies sit at the center of the challenge because they determine how work is organized, where AI is deployed, and which capabilities create value. Their role is not only to develop and deploy training, but also to redesign workflows, set clear expectations for AI fluency, and create opportunities for employees to learn through practice. Employees are signaling that they are ready: nearly half say formal training from their organization would be the most important factor in increasing their day-to-day use of AI, yet about the same number say they are receiving only moderate or less support.6 The challenge is not a lack of employee willingness, but a lack of organizational capability building.
The institutions that support workforce development face their own adaptation challenge. Schools, universities, professional associations, credentialing bodies, and public institutions were built for a world in which skills evolved more gradually and careers followed relatively predictable paths. AI is compressing those timelines and commingling industries. Keeping pace will require much stronger linkages between employers and education providers—including universities, community and two-year colleges, vocational schools, and workforce development organizations—so that learning pathways evolve alongside changing workforce needs. To do so, these institutions will need to rethink how skills are taught, recognized, and refreshed throughout a worker’s career.
America must boldly reinvent the learning and skilling ecosystem that connects students, workers, employers, educators, institutions, and government. As AI accelerates the pace of change, learning will need to move from something people step away from work to do, to an experience embedded in work itself. AI can empower more personalized and experiential learning through adaptive digital learning platforms, simulations, immersive environments such as virtual reality, and AI-powered learning companions that tailor development to individual needs. In effect, every workplace may need to become a classroom, at least for a few moments a day. The organizations and economies that make that shift successfully may gain a lasting advantage—not because they have access to better technology, but because they are better at helping people adapt alongside it.
As the United States looks toward its next 250 years, AI fluency is an imperative for national competitiveness. Like literacy in the industrial age and digital skills in the information age, it will almost certainly become a foundational skill that shapes productivity, innovation, and economic opportunity. Building an AI-fluent workforce will not happen overnight, nor will it be the responsibility of any single institution. But the organizations and economies that succeed in making AI fluency universal may ultimately be the ones that capture the greatest value from the technologies.
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