“We were behind on automation and digitization, and we finally closed the gap. We don’t want to be left behind again, but we aren’t sure how to think about generative AI.”
That’s the sentiment shared by many global executives, given the speed with which generative artificial intelligence (gen AI)1 is advancing in the business world. The technology is accessible, ubiquitous, and promises to have a significant impact on organizations and the economy over the next decade.
Anyone can use gen AI, with little or no formal training or technical know-how. It is being embedded in everyday tools, like email, word processing applications, and meeting software, which means the technology is already positioned to radically transform how people work. And McKinsey research shows that gen AI could enable automation of up to 70 percent of business activities, across almost all occupations, between now and 2030, adding trillions of dollars in value to the global economy.2
Meanwhile, technologists keep reminding us that gen AI is only in its nascent stages of development and usage. This smart technology is only going to get more intelligent—and those who don’t learn to work with it, starting now, will be left behind.3
In this supercharged environment, how can organizations do more than just “keep up”? What strategies, structures, and talent management approaches will business leaders need to adopt to prepare their organizations for a gen-AI-driven future? We examine these and other critical questions in this article.
The situation is evolving rapidly, and there is, frankly, no one right answer to the question of how to successfully roll out gen AI in the organization—business context matters.
But to start, business leaders will need to think broadly about how the rollout of Gen AI could affect their organizations day to day—especially their people. Employees and managers should have a clear understanding of gen AI’s strengths and weaknesses and how use of the technology is linked to the organization’s strategic objectives. Given the technology’s potential to accelerate automation, senior leaders could counter employees’ prevailing fears of “replacement and loss” with messaging about gen AI’s potential for “augmentation and improvement”—and its ability to significantly enhance the employee experience. Imagine, for example, a world with fewer meetings and more time to think.
The central task for senior leaders, then, is to demystify the technology for others; that will mean taking a step back to assess the strategic implications of gen AI, or the risks and opportunities for industries and business models. As leaders build a compelling narrative for the use of gen AI, they will also need to identify two or three high-impact applications to explore and bring employees along on a value-creating journey—taking gen AI initiatives from pilot test to rapid scaling to “business as usual” status. Senior leaders will also need to commit to building the required roles, skills, and capabilities (now and for the future), so they can continually test and learn with gen AI and stay ahead of competitors.
Are you thinking broadly enough about gen AI’s potential impact?
McKinsey research suggests that, because of the emergence of gen AI, about half of today’s business activities could be automated a decade earlier than previous estimates had projected.4 Gen-AI-enabled automation has already begun—and, as the research shows, is likely to affect hours, tasks, and responsibilities for workers across wage rates and educational backgrounds. In fact, the research shows that gen AI will have an especially profound effect on professions traditionally requiring higher levels of education, such as educators and lawyers.5
Gen AI is also likely to inform discussions in the C-suite about how the company creates value and whether the addition of gen AI capabilities allows for industry or business model reinvention. As a result, leaders should ask themselves a range of critical questions relating to the “new” nature of work in gen-AI-enabled organizations, including the following:
What are the organization-wide implications of gen AI? Rather than taking a passive approach to identifying potential use cases and investments associated with gen AI, leaders should view the situation through an “attacker’s lens.” They should consider all the primary, secondary, and even tertiary effects of gen AI: Which business use cases are highest priority now—and which might be candidates for gen AI enablement in six months, 12 months, and so on? What changes will be required at a functional level to make gen AI enablement possible—for instance, how many more software engineers will the company need? And as gen AI functionality continues to be embedded in common word processing, email, and communications tools (Microsoft’s 365 Copilot, for instance), what effect will that have on ways of working across the entire organization? Could gen AI accelerate the shift to a four-day work week? And even more broadly, how might entire industries or business models be fundamentally disrupted?
Does the organization have the right technical talent and risk infrastructure in place? Leaders should consider which operating-model designs will be most effective for ensuring the long-term development of technology talent and the continued evolution of gen AI applications in the organization (see sidebar “Speeding up the search for tech talent”). They should also consider whether that same structure can satisfy the need for gen AI oversight (see sidebar “A powerful resource with potential risks”).
How can corporate culture enable or inhibit the adoption and usage of gen AI? Gen AI applications can be the catalyst for culture change—in more ways than one. The applications themselves can create more organizational transparency and connectivity. One company, for instance, is piloting a gen AI application that allows users to ask questions about operations, sales, and other topics, and the tool draws from the company’s entire collection of intellectual property to come up with answers that can guide users to the most relevant experts and data. Employees report feeling better informed and more connected. Additionally, the same cultural traits that have been crucial for organizational success during recent economic and business upheavals—such as adaptability, speed, agility, trust, integrity, learning and experimentation, innovation, and a willingness to change—will be even more important if organizations want to become truly enabled by gen AI. To understand why, consider the findings from the 2023 McKinsey Digital survey of 1,000 organizations, which found a significant synergy between organizations with strong, innovative cultures and their ability to increase value through new digital technologies, including gen AI.6 In previous iterations of that survey, respondents said the biggest obstacle to their digital success was a culture that was averse to risk and experimentation.7
How should organizations change their talent management approaches? Gen AI applications will have unprecedented effects on organizations’ approaches to talent management. Consider the inevitable impact of gen AI applications on apprenticeship, particularly in the case of knowledge work: imagine a marketing leader uses a gen AI application to write a creative brief that previously would have been developed by a more junior marketing associate. What will happen to the development and mentorship opportunities for both the leader and associate when the learning process is disintermediated by gen AI? What’s more, both the content and the delivery of skill-building programs will be affected. A chatbot could guide new employees through training on a new technology, at their own pace, on their own terms, allowing them to increase the extent and speed of their learning.8 Meanwhile, their instructor may use a gen-AI-enabled “teaching assistant” app to create engaging training modules for individuals and groups and to track the progress of both.
These are just a few key organizational considerations; many more are still evolving. Decisions on structure and operating-model design, for instance, will vary from company to company, but whatever the form, our decades-long experience with digital transformations suggests that discussions about value creation must remain at the center.9 Work processes should enable short, quick cycles of experimentation and iteration and high-quality feedback loops among employees, leaders, and the gen AI applications themselves. To that end, it can be helpful to build small cross-functional teams working end to end on projects and initiatives.
People and gen AI: Building an empowered workforce
Gen AI can be a powerful tool for employee empowerment—even among those who initially perceive it as a threat:
It can augment the employee experience. Gen AI applications can assist employees in ways that many workers may not even expect. For instance, gen AI can suggest the new lines of code required to update a financial-reporting system or outline the A and B versions of a marketing campaign or otherwise create first drafts that human employees can take and implement in live production environments. And by facilitating the training and upskilling process, gen AI applications can help employees pick up new skills more quickly. A recent study, for instance, found that software engineers completed their coding tasks up to twice as fast when using gen AI and reported more satisfaction with the process.10
It can empower middle managers. The benefits of gen AI can accrue not just to frontline workers but also to middle managers. In fact, as the people closest to employees, middle managers have a critical role to play in increasing employees’ comfort with both short-term gen-AI-enabled work and long-term collaborations with the technology.11 And as their own direct reports learn to work with gen AI, middle managers may find themselves overseeing more and different kinds of work streams, moving at a pace never seen before. At the same time, the use of gen AI can free up more capacity for middle managers, so they can shift their attention to higher-value leadership tasks, such as strategy-focused work and people management.
It can help organizations reinvent their talent management practices. The emergence of gen AI presents an opportunity for organizations to hone their approaches to attracting, retaining, and developing talent—particularly when it comes to creators and tech professionals. HR professionals could use gen AI to send personalized outreach emails to candidates and to design job search experiences for candidates in underrepresented groups; research suggests this work could dramatically increase the number and diversity of applications for various roles.12 Gen AI applications could also help companies match new hires with mentors and coaches to improve the onboarding experience, upskill talent, and streamline administrative tasks.
It can prompt senior leaders to lead differently. Senior leaders face the dual responsibility of quickly implementing gen AI today and anticipating future versions of gen AI technologies and their implications. More than anyone else in the organization, they will need to be evangelists for gen AI, encouraging the development and adoption of the technology organization wide. That will mean working with other business unit and technology leaders to allocate resources to update technology infrastructure and take any interim process steps required to facilitate the gen AI rollout—for instance, moving applications to private cloud-hosted environments. In fact, a central task for senior leaders will be to find ways to forge stronger connections between technology leaders and the business units. One company, for example, launched a Slack channel devoted to ongoing discussion of gen AI pilots. Through such forums, employees, product developers, and other business and technology leaders can share stories about their experiences with gen AI, whether and how their daily tasks have changed, and their thoughts on the gen AI journey so far.
As they would when introducing any new technology, senior leaders should speak clearly about the business objectives of gen AI, communicating early and often about gen AI’s role in “augmenting versus replacing” jobs. They should paint a compelling picture of how various aspects of the organization will be rewired through gen AI—technically, financially, culturally, and so on.
Of course, if senior leaders don’t understand the technology themselves, it will be more difficult to make this case for, and lead their teams into, a gen-AI-enabled future. One way for leaders to stay plugged in is to establish forums that provide ongoing professional education on advances in AI technology and applications. Another approach is to carve out time during planning meetings to consider forward-looking questions such as, “Is our approach to gen AI today flexible enough to accommodate the next iteration, and the one after that?” and “Which process steps or roles will we be able to reinvent with the next iteration of gen AI?”
Time to flex your gen AI muscle
Although generative AI burst onto the scene seemingly overnight, CEOs and other business leaders can ill afford to take an overly cautious approach to introducing it in their organizations. If ever a business opportunity demanded a bias for action, this is it. By taking the following three steps simultaneously, and with a sense of urgency, leaders can do more than just “keep up”—they can capture early gains and stay ahead of competitors.
Demystify gen AI for everyone. Senior leaders themselves should develop a deep understanding of gen AI and associated capabilities themselves so they can help to demystify the technology for the rest of the organization. They can then help to introduce mechanisms for managing uncertainties about gen AI where they exist—for instance, establishing clear guidance regarding the use of gen AI tools in hiring and recruiting where AI model biases could emerge.
Identify two or three high-impact use cases—and just get started. Senior leaders should carefully consider their investments in gen AI pilots, and “go big” on those that show the greatest promise of scalability and long-term value—whether it’s an application that simplifies financial reporting or one that enhances onboarding for new hires. As part of this vetting process, senior leaders should consider the business or industry risks or opportunities associated with implementing the gen AI pilot, as well as how hard or easy it will be to move the pilot into production and make it a part of employees’ day-to-day experiences. Once that vetting has happened, senior leaders should steer resources accordingly and take care to monitor and measure the outputs from gen AI initiatives and pilots. Remember, some gen AI initiatives may show impact in the next 12 months, while others may require investment now to yield results in two to five years. The longer-term goal, then, should be to set up a sustainable engine for the rapid upskilling of employees and scaling of gen AI and other digital capabilities.
Commit to building the necessary roles, skills, and capabilities—now and in the future. Senior leaders should commit to building employees’ gen AI skills so they can use the technology judiciously and successfully in their day-to-day work. It’s not a one-and-done process; leaders will need to continually assess how and when tasks are performed, who is performing them, how long tasks typically take, and how critical different tasks are. Through this process, leaders can better understand current and future talent needs and determine how best to redeploy and upskill talent. Indeed, upskilling programs will take on greater importance than ever, as employees will need to learn to manage and work with gen AI tools that are themselves ever evolving. Leaders should also keep in mind that gen AI itself may facilitate the creation of content for, and automated or personalized delivery of, such upskilling programs.
In the time it took to read this article, gen AI applications have already gotten that much smarter. Leaders can put that intelligence to good use. It’s clear that much of the value of gen AI will come from tailoring it to organization-specific use cases—but the successful integration of gen AI requires experimentation and iteration. There is no time to sit back and learn from others’ mistakes. Invest deliberately. Get your hands dirty. Start now.