Agents, robots, and us: Skill partnerships in the age of AI

Source: McKinsey Global Institute (Yee, Madgavkar, Smit, Krivkovich, Chui, Ramirez, Castresana); MGI report (60 pp.) · 2025-11

— Work in the future will be a partnership between people, agents, and robots—all powered by AI. Today’s technologies could theoretically automate more than half of current US work hours. This reflects how profoundly work may change, but it is not a forecast of job losses. Adoption will take time. As it unfolds, some roles will shrink, others grow or shift, while new ones emerge—with work increasingly centered on collaboration between humans and intelligent machines.

— Most human skills will endure, though they will be applied differently. More than 70 percent of the skills sought by employers today are used in both automatable and non-automatable work. This overlap means most skills remain relevant, but how and where they are used will evolve.

— Our new Skill Change Index shows which skills will be most and least exposed to automation in the next five years. Digital and information-processing skills could be most affected; those related to assisting and caring are likely to change the least.

— Demand for AI fluency—the ability to use and manage AI tools—has grown sevenfold in two years, faster than for any other skill in US job postings. The surge is visible across industries and likely marks the beginning of much bigger changes ahead.

— By 2030, about $2.9 trillion of economic value could be unlocked in the United States—if organizations prepare their people and redesign workflows, rather than individual tasks, around people, agents, and robots working together.

Introduction. Work in the future will be a partnership between people, agents, and robots—all powered by artificial intelligence. While much of the current public debate revolves around whether AI will lead to sweeping job losses, our focus is on how it will change the very building blocks of work—the skills that underpin productivity and growth. Our research suggests that although people may be shifted out of some work activities, many of their skills will remain essential. They will also be central in guiding and collaborating with AI, a change that is already redefining many roles across the economy.

In this research, we use “agents” and “robots” as broad, practical terms to describe all machines that can automate nonphysical and physical work, respectively. Many different technologies perform these functions, some based on AI and others not, with the boundaries between them fluid and changing. Using the terms in this expansive way lets us analyze how automation reshapes work overall.1 This report builds on McKinsey’s long-running research on automation and the future of work. Earlier studies examined individual activities, while this analysis also looks at how AI will transform entire workflows and what this means for skills. New forms of collaboration are emerging, creating skill partnerships between people and AI that raise demand for complementary human capabilities.

Although the analysis focuses on the United States, many of the patterns it reveals—and their implications for employers, workers, and leaders—apply broadly to other advanced economies.

We find that currently demonstrated technologies could, in theory, automate activities accounting for about 57 percent of US work hours today.2 This estimate reflects the technical potential for change in what people do, not a forecast of job losses. As technologies take on more complex sequences of tasks, people will remain vital to make them work effectively and to do what machines cannot. Our assessment reflects today’s capabilities, which will continue to evolve, and adoption may take decades.

AI will not make most human skills obsolete, but it will change how they are used. We estimate that more than 70 percent of today’s skills can be applied in both automatable and non-automatable work. With AI handling more common tasks, people will apply their skills in new contexts. Workers will spend less time preparing documents and doing basic research, for example, and more time framing questions and interpreting results. Employers may increasingly prize skills that add value to AI.

To measure how skills could evolve, we developed a Skill Change Index (SCI), a time-weighted measure of automation’s potential impact on each skill used in today’s workforce. Nearly every occupation will experience skill shifts by 2030. Highly specialized, automatable skills such as accounting and coding could face the greatest disruption, while interpersonal skills like negotiation and coaching may change the least. Most others, including widely applicable skills such as problem solving and communication, may evolve as part of a growing partnership with agents and robots.

Employers are already adjusting. Demand for AI fluency—the ability to use and manage AI tools— has jumped nearly sevenfold in two years. The need for technical AI skills employed to develop and govern AI systems is also growing, though at a slower pace. About eight million people in the United States work in occupations where job postings already call for at least one AI-related skill—a fraction of what may be needed in the years ahead. Demand is also rising for complementary skills such as quality assurance, process optimization, and teaching, as well as for some physical skills such as nursing and electrical work. In contrast, job post mentions are declining for routine writing and research, both areas where AI already performs well, although these skills remain essential for much of the workforce.

In our midpoint scenario of automation adoption by 2030, AI-powered agents and robots could generate about $2.9 trillion in US economic value per year.3 Capturing this may depend less on new technological breakthroughs than on how organizations redesign workflows—especially complex, high-value ones that rely on unstructured data—and how quickly human skills adapt. Integrating AI will not be a simple technology rollout but a reimagining of work itself—redesigning processes, roles, skills, culture, and metrics so people, agents, and robots create more value together.

Leaders will play a central role in shaping this partnership. The most effective will engage directly with AI rather than delegating, invest in the human skills that matter most, and balance gains with responsibility, safety, and trust. The outcomes for firms, workers, and communities will ultimately depend on how organizations and institutions work together to prepare people for the jobs of the future.

Related work. The impact of AI on jobs remains uncertain. While many studies attempt to estimate potential job gains or losses, our focus is on how technology is changing the content of work and the skills people need, rather than on how many jobs may ultimately be gained or lost.

History suggests that although technology has displaced workers in the short term, the economy has generated additional demand for labor, including new roles and industries, over time. The breadth of AI’s capabilities—its reach into reasoning, communication, and judgment—has heightened concern about the future of work. To frame the debate, we explore what the current research can and cannot tell us through four guiding questions. and moral judgment, and take responsibility for outcomes, all at acceptable cost and risk.2 And beyond the technical automation potential, actual adoption rates depend on factors such as solution timelines, technology versus labor costs, and the speed at which technologies diffuse from introduction to widespread use.

Early evidence suggests that AI may follow that familiar trajectory. Hiring has reportedly slowed for entry-level programmers and analysts—in other words, workers whose tasks AI is particularly adept at performing.5 At the same time, new forms of work are emerging. Companies are hiring agent product managers, AI evaluation writers, and “human in the loop” validators to guide machine output. New markets are also expanding, from data center construction to AI infrastructure maintenance, while broader structural trends are generating jobs in sectors where automation faces natural limits, such as healthcare and personal services.6 Will we adapt fast enough? Studies suggest that retraining programs, social safety nets, and education systems are not yet ready to handle widespread automation. According to the OECD, participation in adult-learning and reskilling programs is flat or falling in many countries.10 Although 77 percent of companies say they intend to launch upskilling or reskilling initiatives, follow-through may be limited because employers recognize that workers often move on after gaining new skills.11 Overall US demand for labor has remained strong through multiple waves of automation, with new activities having been created faster than technology replaced existing ones.12 Yet AI’s broad reach raises concern that this time may be different. The outcome will depend on whether new demand, industries, and roles emerge to absorb displaced workers—a question beyond the scope of this research. If history is a guide, employment is likely to evolve rather than contract, although there is no certainty that AI will follow the same pattern (see sidebar “Framing the jobs debate as AI reshapes work”).

Method. In this research, we use “agents” and “robots” as broad, practical terms to describe all machines that can automate nonphysical and physical work, respectively. Many different technologies perform these functions, some based on AI and others not, with the boundaries between them fluid and changing. Using the terms in this expansive way lets us analyze how automation reshapes work overall.1 This report builds on McKinsey’s long-running research on automation and the future of work. Earlier studies examined individual activities, while this analysis also looks at how AI will transform entire workflows and what this means for skills. New forms of collaboration are emerging, creating skill partnerships between people and AI that raise demand for complementary human capabilities.

To measure how skills could evolve, we developed a Skill Change Index (SCI), a time-weighted measure of automation’s potential impact on each skill used in today’s workforce. Nearly every occupation will experience skill shifts by 2030. Highly specialized, automatable skills such as accounting and coding could face the greatest disruption, while interpersonal skills like negotiation and coaching may change the least. Most others, including widely applicable skills such as problem solving and communication, may evolve as part of a growing partnership with agents and robots.

We estimate that today’s technology could, in theory, automate about 57 percent of current US work hours (Exhibit 1). This figure compares the capabilities of existing technologies, including those demonstrated in a lab, with the level of human proficiency required for different work tasks.6 As technology advances, the picture will continue to evolve and should be updated regularly.

To understand how AI could reshape demand for human skills, we analyzed job postings, which offer the most up-to-date view of what employers are seeking.15 Lightcast data, widely used by labor economists, provide a detailed and consistent record of the language employers use to describe roles and skills. While postings reflect hiring intentions rather than the actual work people do, they offer the most comprehensive picture of skill demand.

From this source, we identified roughly 6,800 skills cited frequently in more than 11 million job postings, providing a representative snapshot of the US labor market.16 We then examined how employer requirements differ across occupations.17

Discussion. AI-powered automation will change work, but people remain indispensable At current levels of capability, agents could perform tasks that occupy 44 percent of US work hours today, and robots 13 percent (Exhibit 2).8 This means that automation could, in theory, take on a majority of the work now done by people in the United States. That does not mean half of all jobs would disappear; many would change as specific tasks are automated, shifting what people do rather than eliminating the work itself.

In addition, work that draws heavily on social and emotional skills remains largely beyond the reach of automation even under a full-adoption scenario. This is because many tasks require real-time awareness such as a teacher reading a student’s expression or a salesperson sensing when a client is losing interest. People also provide oversight, quality control, and the human presence that customers, students, and patients often prefer.

Extending automation further would require technologies that can match a range of human capabilities currently unmatched. Agents would need to interpret intention and emotion. Robots would need to master fine motor control, such as grasping delicate objects or manipulating instruments in surgery.

11 Agents, robots, and us: Skill partnerships in the age of AI As technology advances, the work requiring people will also change. Some roles will shrink, others expand or shift focus, and new ones will be created. Recent developments in radiology illustrate this dynamic. Between 2017 and 2024, radiologist employment grew by about 3 percent per year despite rapid advances in AI, and it is expected to continue growing.9 AI augmented radiologists’ work, improving accuracy and efficiency while enabling doctors to focus on complex decisionmaking and patient care.10 The Mayo Clinic, for example, has expanded its radiology staff by more than 50 percent since 2016 while deploying hundreds of AI models to support image analysis.11 AI is also creating other new types of work and roles. Software engineers are building and refining agents, while designers and creators are using generative tools to produce new content.

Occupations with the lowest automation potential were classified as people-centric, while those with high shares of automatable tasks were labeled agent-centric or robot-centric. Roles with a more even balance were grouped into mixed or hybrid archetypes that combine substantial shares of two or all three (Exhibit 3).

This framework applies across labor markets and can help leaders see where change may come first and how workforce transitions could unfold, highlighting roles that may evolve into human–agent– robot coworker models and those likely to be largely automated by agents or robots under human supervision. For workers, it offers a view of how their own roles might change.

At one end of the spectrum are roles that remain largely human. These people-centric occupations— found, for example, in healthcare and in building and maintenance—make up about one-third of US jobs and pay an average of $71,000 a year. Physical activity that current technologies cannot replicate accounts for about half of the work hours in these occupations.14 At the other end of the spectrum are roles with the highest potential for automation by agents or robots. These occupations make up about 40 percent of total jobs. With average pay of $70,000, most are agent-centric roles in legal and administrative services. They involve large shares of cognitive tasks—such as drafting documents—that could technically be handled by AI systems. Some of this work may end up being fully automated, but people will still be needed to guide, supervise, and verify.