Artificial intelligence has arrived in the workplace and has the potential to be as transformative as the steam engine was to the 19th-century Industrial Revolution.1“Gen AI: A cognitive industrial revolution,” McKinsey, June 7, 2024. With powerful and capable large language models (LLMs) developed by Anthropic, Cohere, Google, Meta, Mistral, OpenAI, and others, we have entered a new information technology era. McKinsey research sizes the long-term AI opportunity at $4.4 trillion in added productivity growth potential from corporate use cases.2“The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023. (View Highlight)
This research report, prompted by Reid Hoffman’s book Superagency: What Could Possibly Go Right with Our AI Future,3Reid Hoffman and Greg Beato, Superagency: What Could Possibly Go Right with Our AI Future, Authors Equity, January 2025. asks a similar question: How can companies harness AI to amplify human agency and unlock new levels of creativity and productivity in the workplace? AI could drive enormous positive and disruptive change. This transformation will take some time, but leaders must not be dissuaded. Instead, they must advance boldly today to avoid becoming uncompetitive tomorrow. The history of major economic and technological shifts shows that such moments can define the rise and fall of companies. Over 40 years ago, the internet was born. Since then, companies including Alphabet, Amazon, Apple, Meta, and Microsoft have attained trillion-dollar market capitalizations. Even more profoundly, the internet changed the anatomy of work and access to information. AI now is like the internet many years ago: The risk for business leaders is not thinking too big, but rather too small. (View Highlight)
Chapter 2 delves into the attitudes and perceptions of employees and leaders. Our research shows that employees are more ready for AI than their leaders imagine. In fact, they are already using AI on a regular basis; are three times more likely than leaders realize to believe that AI will replace 30 percent of their work in the next year; and are eager to gain AI skills. Still, AI optimists are only a slight majority in the workplace; a large minority (41 percent) are more apprehensive and will need additional support. This is where millennials, who are the most familiar with AI and are often in managerial roles, can be strong advocates for change. (View Highlight)
Chapter 3 looks at the need for speed and safety in AI deployment. While leaders and employees want to move faster, trust and safety are top concerns. About half of employees worry about AI inaccuracy and cybersecurity risks. That said, employees express greater confidence that their own companies, versus other organizations, will get AI right. The onus is on business leaders to prove them right, by making bold and responsible decisions. (View Highlight)
Chapter 4 examines how companies risk losing ground in the AI race if leaders do not set bold goals. As the hype around AI subsides, companies should put a heightened focus on practical applications that empower employees in their daily jobs. These applications can create competitive moats and generate measurable ROI. Across industries, functions, and geographies, companies that invest strategically can go beyond using AI to drive incremental value and instead create transformative change. (View Highlight)
Chapter 5 looks at what is required for leaders to set their teams up for success with AI. The challenge of AI in the workplace is not a technology challenge. It is a business challenge that calls upon leaders to align teams, address AI headwinds, and rewire their companies for change. (View Highlight)
Imagine a world where machines not only perform physical labor but also think, learn, and make autonomous decisions. This world includes humans in the loop, bringing people and machines together in a state of superagency that increases personal productivity and creativity (see sidebar “AI superagency”). This is the transformative potential of AI, a technology with a potential impact poised to surpass even the biggest innovations of the past, from the printing press to the automobile. AI does not just automate tasks but goes further by automating cognitive functions. Unlike any invention before, AI-powered software can adapt, plan, guide—and even make—decisions. That’s why AI can be a catalyst for unprecedented economic growth and societal change in virtually every aspect of life. It will reshape our interaction with technology and with one another. (View Highlight)
Many breakthrough technologies, including the internet, smartphones, and cloud computing, have transformed the way we live and work. AI stands out from these inventions because it offers more than access to information. It can summarize, code, reason, engage in a dialogue, and make choices. AI can lower skill barriers, helping more people acquire proficiency in more fields, in any language and at any time. AI holds the potential to shift the way people access and use knowledge. The result will be more efficient and effective problem solving, enabling innovation that benefits everyone. (View Highlight)
Employees will be the ones to make their organizations AI powerhouses. They are more ready to embrace AI in the workplace than business leaders imagine. They are more familiar with AI tools, they want more support and training, and they are more likely to believe AI will replace at least a third of their work in the near future. Now it’s imperative that leaders step up. They have more permission space than they realize, so it’s on them to be bold and capture the value of AI. Now. (View Highlight)
In our survey, nearly all employees (94 percent) and C-suite leaders (99 percent) report having some level of familiarity with gen AI tools. Nevertheless, business leaders underestimate how extensively their employees are using gen AI. C-suite leaders estimate that only 4 percent of employees use gen AI for at least 30 percent of their daily work, when in fact that percentage is three times greater, as self-reported by employees (Exhibit 2). And while only a total of 20 percent of leaders believe employees will use gen AI for more than 30 percent of their daily tasks within a year, employees are twice as likely (47 percent) to believe they will (see sidebar “Who is using AI at work? Nearly everyone, even skeptical employees”). (View Highlight)
As noted at the beginning of this chapter, employees anticipate AI will have a dramatic impact on their work. Now they would like their companies to invest in the training that will help them succeed. Nearly half of employees in our survey say they want more formal training and believe it is the best way to boost AI adoption. They also would like access to AI tools in the form of betas or pilots, and they indicate that incentives such as financial rewards and recognition can improve uptake. (View Highlight)
To get a clearer picture of global AI adoption trends, we looked at trends across five countries: Australia, India, New Zealand, Singapore, and the United Kingdom. Broadly speaking, these employees and C-suite leaders—the “international” group in this report—have similar views of AI as their US peers. In some key areas, however, including the topic of training, their experiences differ. (View Highlight)
Many international employees are concerned about insufficient training, even though they report receiving far more support than US employees. Some 84 percent of international employees say they receive significant or full organizational support to learn AI skills, versus just over half of US employees. International employees also have more opportunities to participate in developing gen AI tools at work than their US counterparts, with differences of at least ten percentage points in activities such as providing feedback, beta testing, and requesting specific features (exhibit). (View Highlight)
Many millennials aged 35 to 44 are managers and team leaders in their companies. In our survey, they self-report having the most experience and enthusiasm about AI, making them natural champions of transformational change. Millennials are the most active generation of AI users. Some 62 percent of 35- to 44-year-old employees report high levels of expertise with AI, compared with 50 percent of 18- to 24-year-old Gen Zers and 22 percent of baby boomers over 65 (Exhibit 4). By tapping into that enthusiasm and expertise, leaders can help millennials play a crucial role in AI adoption. (View Highlight)
Since many millennials are managers, they can support their teams to become more adept AI users. This helps push their companies toward AI maturity. Two-thirds of managers say they field questions from their team about how to use AI tools at least once a week, and a similar percentage say they recommend AI tools to their teams to solve problems (Exhibit 5). (View Highlight)
In many transformations, employees are not ready for change, but AI is different. Employee readiness and familiarity are high, which gives business leaders the permission space to act. Leaders can listen to employees describe how they are using AI today and how they envision their work being transformed. They also can provide employees with much-needed training and empower managers to move AI use cases from pilot to scale. (View Highlight)
It’s critical that leaders meet this moment. It’s the only way to accelerate the probability that their companies will reach AI maturity. But they must move with alacrity, or they will fall behind. (View Highlight)
The majority of employees describe themselves as AI optimists; Zoomers and Bloomers make up 59 percent of the workplace. Even Gloomers, who are one of the two less-optimistic segments in our analysis, report high levels of gen AI familiarity, with over a quarter saying they plan to use AI more next year. (View Highlight)
Business leaders need to embrace this speed and optimism to ensure that their companies don’t get left behind. Yet despite all the excitement and early experimentation, 47 percent of C-suite leaders say their organizations are developing and releasing gen AI tools too slowly, citing talent skill gaps as a key reason for the delay (Exhibit 6). (View Highlight)
We are at a turning point. The initial AI excitement may be waning, but the technology is accelerating. Bold and purposeful strategies are needed to set the stage for future success. Leaders are taking the first step: One quarter of those executives we surveyed have defined a gen AI road map, while just over half have a draft that is being refined (Exhibit 7). With technology changing this fast, all road maps and plans will evolve constantly. For leaders, the key is to make some clear choices about what valuable opportunities they choose to pursue first—and how they will work together with peers, teams, and partners to deliver that value. (View Highlight)
There’s a spanner in the works: Regulation and safety often continue to be seen as insurmountable challenges rather than opportunities. Leaders want to increase AI investments and accelerate development, but they wrestle with how to make AI safe in the workplace. Data security, hallucinations, biased outputs, and misuse (for example, creating harmful content or enabling fraud) are challenges that cannot be ignored. Employees are well aware of AI’s safety challenges. Their top concerns are cybersecurity, privacy, and accuracy (Exhibit 8). But what will it take for leaders to address these concerns while also moving ahead at light speed? (View Highlight)
While employees acknowledge the risks and even the likelihood that AI may replace a considerable portion of their work, they place high trust in their own employers to deploy AI safely and ethically. Notably, 71 percent of employees trust their employers to act ethically as they develop AI. In fact, they trust their employers more than universities, large technology companies, and tech start-ups (Exhibit 9). (View Highlight)
A high percentage of international C-suite leaders we surveyed across five regions (Australia, India, New Zealand, Singapore, and the United Kingdom) are Gloomers, who favor greater regulatory oversight. Between 37 to 50 percent of international C-suite leaders self-identify as Gloomers, versus 31 percent in the United States. This may be because top-down regulation is more accepted in many countries outside the United States. Of the global C-suite leaders surveyed, half or more worry that ethical use and data privacy issues are holding back their employees from adopting gen AI. (View Highlight)
However, our research shows that attitudes about regulation are not inhibiting the economic expectations of business leaders outside the United States. More than half of the international executives (versus 41 percent of US executives) indicate they want their companies to be among the first adopters of AI, with those in India and Singapore being especially bullish (exhibit). The desire of international business leaders to be AI first movers can be explained by the revenue they expect from their AI deployments. Some 31 percent of international C-suite leaders say they expect AI to deliver a revenue uplift of more than 10 percent in the next three years, versus just 17 percent of US leaders. Indian executives are the most optimistic, with 55 percent expecting a revenue uplift of 10 percent or more over the next three years. (View Highlight)
To assess how far along companies are in this shift, we examined three categories of AI applications: personal use, business use, and societal use (see sidebar “AI’s potential to enhance our personal lives”). We mapped over 250 applications from our work and publicly shared examples to understand the spectrum of impact levels, from localized use cases to transformations with more universal impact. Our conclusion? Given that most companies are early in their AI journeys, most AI applications are localized use cases still in the pilot stages (Exhibit 17). (View Highlight)
In many cases, that’s perfectly appropriate. But creating AI applications that can revolutionize industries and create transformative value requires something more. Robotics in manufacturing, predictive AI in renewable energy, drug development in life sciences, and personalized AI tutors in education—these are the kinds of transformative efforts that can drive the greatest returns.17Dario Amodei, “Machines of loving grace: How AI could transform the world for the better,” Dario Amodei website, October 2024. These weren’t created from a reactive mindset. They are the result of inspirational leadership, a unique concept of the future, and a commitment to transformational impact. This is the kind of courage needed to develop AI applications that can revolutionize industries. (View Highlight)
To truly harness the potential of AI, companies must challenge themselves to envision and implement more breakthrough initiatives. Success in the era of AI hinges not just on technology deployment or employee willingness but also on visionary leadership. The ingredients are here. The technology is already highly capable and rapidly advancing, and employees are more ready than leaders think. Leaders have more permission space than they realize to deploy AI quickly in the workplace. To do so, leaders need to stretch their ambitions toward systematic change, laying the foundation for real competitive differentiation. If they want to be more ambitious about AI, companies must increase the proportion of transformational initiatives in their portfolios. The next chapter examines the headwinds that leaders must overcome—and how they can do so. (View Highlight)
Securing consensus from senior leaders on a strategy-led gen AI road map is no simple task. The key to meeting this challenge is first recognizing that leadership alignment cannot be oversimplified or assumed. The process requires ongoing engagement from senior leaders across business domains, each of which may have distinct objectives and risk appetites. Together, leaders must clearly define where value lies, how AI will drive this value, and how risk will be mitigated. They must collectively establish metrics for performance evaluation and investment recalibration. To facilitate alignment, they may want to appoint a gen AI value and risk leader or institute an enterprise-wide leadership and orchestration function. These actions can enhance collaboration among business, technology, and risk teams. Although challenging, aligning leadership is a crucial step to ensure that AI projects are not disparate, avoid liability, and deliver transformative business outcomes. (View Highlight)
There is still a world of uncertainty to manage. Employers do not know how many AI experts they will need with what type of skills, whether that talent bench even exists, how quickly they can source people, and how they can remain an attractive employer for in-demand hires after they come aboard. On the other hand, they do not know how fast AI may depress demand for other skills and thus require workforce rebalancing and retraining. (View Highlight)
Fragile supply chains can expose enterprises to disruptions and technical, regulatory, and legal challenges. The AI supply chain is global, with significant R&D concentrated in China, Europe, and North America and with semiconductor and hardware manufacturing concentrated in East Asia and the United States. Today’s geopolitics are complex. Furthermore, models and applications are increasingly created in open-source forums spanning many countries. (View Highlight)
McKinsey’s Rewired framework includes six foundational elements to guide sustained digital transformation: road map, talent, operating model, technology, data, and scaling (Exhibit 18). When companies implement this playbook successfully, they cultivate a culture of autonomy, leverage modern cloud practices, and assemble multidisciplinary agile teams. (View Highlight)
Adaptability. AI technology is advancing so rapidly that organizations must adopt new best practices quickly to stay ahead of the competition. Best practices may come in the form of new technologies, talent, business models, or products. For example, a modular approach helps future-proof tech stacks. As natural language becomes a medium for integration, AI systems are becoming more compatible, allowing businesses to swap, upgrade, and integrate models and tools with less friction. This modularity allows enterprises to avoid vendor lock-in and put new AI advancements to use quickly without constantly reinventing their tech stacks. (View Highlight)
Federated governance models. Managing data and models can give teams autonomy to develop new AI tools while centrally controlling risk. Leaders can directly oversee high-risk or high-visibility issues, such as setting policies and processes to monitor models and outputs for fairness, safety, and explainability. But they can set direction and delegate other monitoring to business units, including measuring performance-based criteria such as accuracy, speed, and scalability. (View Highlight)
Budget agility. Given technological advances across models, as well as the opportunity to curate an optimal mix of LLMs, small language models (SLMs), and agents, business leaders should keep their budgets flexible. This helps enterprises optimize their AI deployments simultaneously for costs and performance. (View Highlight)
AI benchmarks. These tools can serve as powerful means to quantitatively assess, compare, and improve the performance of different AI models, algorithms, and systems. If technologists come together to adopt standardized public benchmarks—and if more C-level executives start employing benchmarks, including ethical ones—model transparency and accountability will improve and AI adoption will increase, even among more skeptical employees. (View Highlight)
AI-specific skill gaps. Notably, 46 percent of leaders identify skill gaps in their workforces as a significant barrier to AI adoption. Leaders will need to attract and hire top-level talent, including AI/ML engineers, data scientists, and AI integration specialists. They will also need to commit to creating an environment that is attractive to technologists. For example, this can mean providing them with plenty of time to experiment, offering access to cutting-edge tools, creating opportunities to engage in open-source communities, and promoting a collaborative engineering culture. Upskilling existing employees is just as critical: Research from McKinsey’s People and Organizational Performance Practice underscores the importance of tailoring training to specific roles, such as offering technical team members bootcamps on library creation while offering prompt engineering classes to specific functional teams.19People & Organization Blog, “Upskilling and reskilling priorities for the gen AI era,” blog entry by Sandra Durth, Kiera Jones, Lisa Christensen, and Naveed Rashid, McKinsey, September 30, 2024. (View Highlight)
Human centricity. To guarantee both fairness and impartiality, it is important that business leaders incorporate diverse perspectives early and often in the AI development process and maintain transparent communication with their teams. As it stands, less than half of C-suite leaders (48 percent) say they would involve nontechnical employees in the early development stages of AI tools, specifically ideation and requirement gathering. Agile pods and human-centric development practices such as design-thinking and reinforcement learning from human feedback (RLHF) will help leaders and developers create AI solutions that all people want to use. In agile pods, technical team members sit alongside employees from business functions such as HR, sales, and product, and from support functions such as legal and compliance. Further, leaders can empathize with employees’ uneasiness about AI’s impacts on potential job losses by being honest about new skill requirements and head count changes. Forums where employees can provide input on AI applications, voice concerns, and share ideas are valuable for maintaining a transparent, human-first culture. (View Highlight)
This is the moment for leaders to set bold AI commitments and to meet employee needs with on-the-job training and human-centric development. As leaders and employees work together to reimagine their businesses from the bottom up, AI can evolve from a productivity enhancer into a transformative superpower—an effective partner that increases human agency. Leaders who can replace fear of uncertainty with imagination of possibility will discover new applications for AI, not only as a tool to optimize existing workflows but also as a catalyst to solve bigger business and human challenges. Early stages of AI experimentation focused on proving technical feasibility through narrow use cases, such as automating routine tasks. Now the horizon has shifted: AI is poised to unlock unprecedented innovation and drive systemic change that delivers real value. (View Highlight)
To meet this more ambitious era, leaders and employees must ask themselves big questions. How should leaders define their strategic priorities and steer their companies effectively amid disruption? How can employees ensure they are ready for the AI transition coming to their workplaces? Questions like the following ones will shape a company’s AI future: (View Highlight)
For business leaders:
• Is your strategy ambitious enough? Do you want to transform your whole business? How can you reimagine traditional cost centers as value-driven functions? How do you gain a competitive advantage by investing in AI?
• What does successful AI adoption look like for your organization? What success indicators will you use to evaluate whether your investments are yielding desired ROI?
• What skills define an AI-native workforce? How can you create opportunities for employees to develop these skills on the job? (View Highlight)
For employees:
• What does achieving AI mastery mean for you? Does it extend to confidently using AI for personal productivity tasks such as research, planning, and brainstorming?
• How do you plan to expand your understanding of AI? Which news sources, podcasts, and video channels can you follow to remain informed about the rapid evolution of AI?
• How can you rethink your own work? Some of the most innovative ideas often emerge from within teams, rather than being handed down from leadership. How would you redesign your work to drive bottom-up innovation? (View Highlight)
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