As many employees adopt generative AI at work, companies struggle to follow suit. To capture value from current momentum, businesses must transform their processes, structures, and approach to talent.
After nearly two years of debate, the verdict is in: generative AI (gen AI) is here to stay, and its business potential is massive. We’ve already witnessed an exponential rate of gen-AI-related innovation, which promises to accelerate automation and enhance productivity, innovation, and the quality of work, as well as the employee and customer experience. The companies that fail to act and adapt now will likely struggle to catch up in the future.
Despite all the buzz, most companies have yet to scratch the surface of gen AI’s promise. A recent McKinsey Global Survey reveals that employees are far ahead of their organizations in using gen AI, 1 The online survey was in the field from February 27 to March 8, 2024, and garnered responses from 592 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 127 say they are using publicly available or internal gen AI tools almost always at work; 51 say they use public tools never or rarely and that they never use internal tools, or that internal tools are not available to them; and the other 414 say they use either internal or public tools sometimes, often, or at varying frequencies by the type of tool. as companies have been slow to adopt in ways that could realize gen AI’s trillion-dollar opportunity. To harness employees’ enthusiasm and stay ahead, companies need a holistic approach to transforming how the whole organization works with gen AI; the technology alone won’t create value. This means applying gen AI in ways that enable the business strategy: by reinventing operating models and entire domains, 2 That is, specific workflows, processes, journeys, and even functions. by reimagining talent and skilling, and by reinforcing changes through robust governance and infrastructure.
To harness employees’ enthusiasm and stay ahead, companies need a holistic approach to transforming how the whole organization works with gen AI; the technology alone won’t create value.
According to our research, employees are forging ahead with gen AI, a broadly accessible technology that puts AI’s potential at everyone’s fingertips. Nearly all respondents (91 percent) say they use gen AI for work and the vast majority are enthusiastic about it (Exhibit 1). Nine in ten also believe the tools could positively impact their work experience and most believe gen AI will help with a range of skills, from critical thinking to creativity.
In this respect, most companies are lagging behind their employees. As high as employee usage is, organizational maturity with gen AI is strikingly low. In our survey, only 13 percent of respondents’ companies have implemented multiple use cases, a group we call “early adopters” (Exhibit 2). 3 We define early adopters as those companies, according to respondents, that have implemented six or more gen AI use cases to date. Among them, there’s a larger share of heavy users: that is, employees who use either public or internal gen AI tools every day or two. Compared with others, this group is likelier to use gen AI for a range of work activities and report greater productivity gains. 4 Survey respondents were asked to rate the frequency of their use of gen AI tools, both publicly available and internally developed tools, at work—as well as their use of public tools for nonwork purposes. For all of these questions, potential responses were “never,” “rarely (that is, once per month),” “sometimes (that is, once per week),” “often (that is, two to three times per week),” and “almost always (that is, every day).”
The chief information officer of a global heavy industry company sees these trends at his own organization. Employees are experimenting with gen AI through publicly available and embedded tools, 5 Such as OpenAI’s ChatGPT and Microsoft’s Copilot. which is increasing curiosity and encouraging greater openness to experimentation. Yet he notes that there’s no easy-to-prove business case for employee-driven adoption and the piecemeal implementation of use cases.
Technology adoption for its own sake has never created value, which is also true with gen AI. Whether technology is itself the core strategy (for example, developing gen-AI-based products) or supports other business strategies, its deployment should link to value creation opportunities and measurable outcomes (for more, see sidebar “‘People led, tech powered’: Walmart’s vision for gen AI”). Our survey findings suggest that early adopters are on track: 63 percent of early-adopter respondents say their organizations’ AI and gen AI strategies align greatly with their business strategies, compared with only 17 percent of respondents at “experimenter” companies. 6 We define experimenters as those companies, according to respondents, that have implemented one to five gen AI use cases.
“People led, tech powered”: Walmart’s vision for gen AIAt Walmart, leaders have created a technology vision and strategy that aligns with its strategic focus on customer and employee experience, the two domains the company targeted with its generative AI (gen AI) implementation. For customers, Walmart introduced gen-AI-based features such as autogenerated shopping lists, “Shop with Friends” (a social shopping app), and “InHome” (an automated delivery service). For associates, Walmart invested in tools such as My Assistant, which minimizes time spent on administrative and HR tasks, and the Me@Walmart app, which includes a reality-powered feature for real-time inventory management.
To capture gen AI’s full potential, companies must consider how the technology can redefine the way the organization works. Our experience and research point to three steps to prepare for gen AI’s next inflection point: reinvent the operating model by translating vision into value, domain by domain; reimagine the talent and skilling strategy; and reinforce changes through formal and informal mechanisms that ensure continuous adaptation.
Companies can only reap gen AI’s full benefits, which range from faster innovation and enhanced productivity to improved employee and customer experience, when they use technology to make transformative changes. More specifically, this means embracing holistic changes to the operating model, including key processes, ways of working, capabilities, and culture. Because anyone can use gen AI, these tools can act as a gateway technology for all other digital and tech transformations.
To start, companies should prioritize the right unit of transformation by focusing on specific domains, such as product development, marketing, and customer service. This domain-based approach allows for end-to-end, technology-led transformation that integrates multiple use cases within a single value-creating workflow, process, journey, or, occasionally, entire function. Since domains often span organizational boundaries, implementing gen AI and other technologies at the domain level can deliver greater value than one-off solutions.
Here are examples of what’s possible with a domain-based transformation, and the implications for roles and day-to-day work:
As the examples above highlight, gen AI’s implications for talent and skill needs are massive. The technology’s potential to accelerate automation and transform operating models will significantly affect the roles and skills that organizations need. According to other McKinsey research, half of today’s work activities could be automated between 2030 and 2060, accelerating previous, pre-gen-AI projections by a decade. This puts pressure on organizations to understand their talent and skill needs quickly, adopt various strategies to close skill gaps, and invest in upskilling and reskilling. A gen-AI-based talent transformation isn’t something companies can simply hire their way out of, as it affects the entire organization and its ways of working.
Our research shows that early adopters prioritize talent and the human side of gen AI more than other companies (Exhibit 3). Our survey shows that nearly two-thirds of them have a clear view of their talent gaps and a strategy to close them, compared with just 25 percent of the experimenters. Early adopters focus heavily on upskilling and reskilling as a critical part of their talent strategies, as hiring alone isn’t enough to close gaps and outsourcing can hinder strategic-skills development. Finally, 40 percent of early-adopter respondents say their organizations provide extensive support to encourage employee adoption, versus 9 percent of experimenter respondents.
Companies can capitalize on employees’ enthusiasm for gen AI by investing in both technology adoption and skills (for more, see sidebar “Taking the granular view on gen AI’s workforce implications”). As previous McKinsey research shows, macroeconomic investments in both enable productivity gains that organizations can also see. This will require a tailored approach to reskilling and upskilling and close collaboration between business and tech leaders and HR. Given the criticality of people topics, HR plays an especially important role in gen AI and technology transformations, both by transforming the people domain and by acting as a gen AI copilot for all employees. One executive noted that for every $1 spent on technology, $5 should be spent on people.
Taking the granular view on gen AI’s workforce implicationsA collection of Asian financial institutions did a thorough assessment of generative AI’s (gen AI’s) implications on its roles and skills. They first analyzed the potential capacity that could be freed up across all roles. Then, based on gen AI’s potential impact on certain roles and cohorts, the institutions determined their upskilling, reskilling, and employee redeployment needs. Using this comprehensive fact base, these institutions defined specific interventions to prepare each cohort for gen AI’s effects on its work. For example, shifting skill proficiencies toward technical areas (such as application development and integration) and designing learning journeys for technical team members in areas such as large language model operations and responsible AI policy.
With gen AI, building capabilities across the entire enterprise is crucial. As it’s a rapidly evolving, widely accessible technology, employees must adapt to the new skills (such as prompt writing, contextualization, and data-driven decision making) that gen AI demands. While specific skills shifts will vary greatly by company, all organizations will need to take a dynamic approach to talent development, based on their operating-model transformations; building skills is an ongoing process. As gen AI and automation reshape roles, employees will also need strong cognitive, strategic thinking and social and emotional skills to handle more complex tasks that complement AI.
Within specific roles, the tech talent who are scaling gen AI and future technologies will need to build, train, and fine-tune AI models. These newer skills will require immersive learning in areas such as software development, cloud integration, and security. Tech talent must also be able to contextualize and apply their judgment when translating business needs into technology solutions. Furthermore, companies will need tech-adjacent roles to manage the governance, operational, HR, and legal aspects of AI. Some roles, such as chief AI officers, will be brand new.
Would you like to learn more about our People and Organizational Performance Practice?For domain-based talent, many will need intensive upskilling as their roles evolve. This will include different types of on-the-job learning and formal training opportunities. For example, healthcare professionals might take courses on personalized treatment planning and AI-driven diagnostics that are supplemented with mentoring and real-world projects.
And for all employees, including leaders and managers, it’s vital that everyone learns to use gen AI effectively and safely. Examples include comprehensive learning programs that cover responsible use and effective interaction with AI, as well as more augmentation-focused trainings, such as using gen AI coaching that allows managers to practice giving feedback.
About QuantumBlack, AI by McKinseyQuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
A European telecommunications company put tailor-made reskilling into practice by implementing an AI coach for its customer service agents. By analyzing call transcripts from frontline employees, the AI coach assessed people across 20 different soft and hard skills. Both team members and leaders could access a dashboard that tracked progress on these skills and delivered real-time feedback using customer quotes and examples. The AI coach also suggested improvements and learning content based on agents’ performance and behavior, creating a hyperpersonalized learning experience. This tool resulted in a 10 percent reduction in average handling time, a 20 percent increase in customer satisfaction, and a 15 percent increase in the rate of first-time-right responses.
How, exactly, should organizations tackle these massive transformational changes? Real success with gen AI requires a comprehensive, integrated approach to creating value. Our survey indicates that the most useful enabler of future adoption is better integration of gen AI into existing systems, cited by 60 percent of respondents. To make gen AI changes stick, organizations need the right infrastructure to support continuous change and win over hearts and minds.
The first step is establishing the right governance for gen AI (for more, see sidebar “Good gen AI governance at work”). In our experience, this means creating a centralized structure that oversees the organization’s AI adoption, sometimes with a chief AI officer leading these efforts. Nearly all early-adopter respondents (91 percent) say they have implemented some governance structure for gen AI, compared with a smaller share (77 percent) of experimenters. A centralized model with a gen-AI-dedicated center of excellence helps align AI vision with execution. This model also facilitates the implementation of strategy, continuous measurement, adaptation to new insights, and further experimentation—specifically, which experiments to scale or to stop, based on priorities and risks.
Good gen AI governance at workTo enhance its productivity with generative AI (gen AI), a leading multinational bank identified the processes with the highest potential for improvement. This exercise enabled the development of a clear strategy and road map and of a business-led center of excellence, including experts in technology, AI, and risk management. The center of excellence evaluates use cases, implements AI guardrails, tracks metrics, and shares knowledge across the organization. What’s more, the bank integrated active use of gen AI into performance evaluations, ensuring a formal commitment to AI integration.
The second step is treating these changes like a true transformation. This means defining the transformation’s infrastructure, roles, and measurement criteria; ensuring accountability within business units; and implementing a regular cadence to monitor progress—and adjusting as needed.
Third is addressing employee mindsets and behaviors across the organization. We know from extensive transformation research and countless conversations with executives that changing mindsets and behaviors is vital to any successful transformation. Indeed, in our survey, early adopters focus more than others on the four tenets of the influence model that enables such changes: role modeling, fostering understanding and conviction, building capabilities, and reinforcing new ways of working (Exhibit 4).
In the gen AI context, this means:
No matter where an organization is on its gen AI journey, the time for making transformational change is now. Employees are already asking their organizations for more, and some companies have begun moving from experimentation to value capture. By gen AI’s next inflection point, the downside of lagging behind—and missing out on gen AI’s potential benefits—may be even greater. With employees’ embrace of gen AI and the technology’s rapid evolution, companies can capitalize on the current momentum by addressing organizational barriers to adoption, which requires no less than fundamentally transforming the company’s operations and preparing people for continuous change.
Charlotte Relyea is a senior partner in McKinsey’s New York office, Dana Maor is a senior partner in the Tel Aviv office, Sandra Durth is a partner in the Cologne office, and Jan Bouly is an associate partner in the Brussels office.
The authors wish to thank Alex Sukharevsky, Ariel Cohen Codar, Bryan Hancock, Cleo De Laet, Esther Wang, Federico Marafante, Joachim Talloen, Julian Raabe, Julie Goran, Kiera Jones, Michael Chui, Nina Gandhi, Rita Calvão, and Sanjna Parasrampuria for their contributions to this article.
This article was edited by Daniella Seiler, an executive editor in the Washington, DC, office.
