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mdc-u-ts-9"><span> </span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j"><a data-component="mdc-c-link" href="/our-people/charlotte-relyea" class="mdc-c-link-inline___7DRrt_8032924 mdc-c-link-inline--secondary___YKoOK_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Charlotte Relyea</span></a> </span></span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j"><a data-component="mdc-c-link" href="/our-people/dana-maor" class="mdc-c-link-inline___7DRrt_8032924 mdc-c-link-inline--secondary___YKoOK_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Dana Maor</span></a><span> </span></span></span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j"><a data-component="mdc-c-link" href="/our-people/sandra-durth" class="mdc-c-link-inline___7DRrt_8032924 mdc-c-link-inline--secondary___YKoOK_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Sandra Durth</span></a></span></span><br/><span> </span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j">Jan Bouly</span></span></div><div class="mdc-u-grid-col-md-start-8 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-8 mdc-u-grid-col-lg-end-11 ArticleContent_mck-c-article-content__share-tools__kWRRw"></div></section><section class="mdc-u-grid mdc-u-grid-col-md-12 mck-u-animation-blur-in-400"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-11"><div data-component="mdc-c-description" class="mdc-c-description___SrnQP_8032924 mdc-u-ts-5"><div class="mck-u-links-inline">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.</div></div></div></section><main data-layer-region="article-body" class="mdc-u-grid mdc-u-grid-gutter-xxl"><div class="mdc-u-grid mdc-u-grid-col-1 mdc-u-grid-col-md-12"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-11"><div class="mdc-o-content-body mck-u-dropcap"> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="DownloadsSidebar_mck-c-downloads-sidebar__iFmyt mck-o-xs-right-span"><div data-layer-region="downloads-right-rail"><h3 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-c-heading--title___5qyOB_8032924 mdc-c-heading--border___K8dj3_8032924"></h3><div><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_8032924"><a data-component="mdc-c-link" href="#/download/%2F~%2Fmedia%2Fmckinsey%2Fbusiness%20functions%2Fpeople%20and%20organizational%20performance%2Four%20insights%2Fgen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation%2Fgen-ais-next-inflection-point-from-employee-experimentation-to-organizational-transformation_final.pdf%3FshouldIndex%3Dfalse" class="DownloadsSidebar_mck-c-downloads-sidebar__download-link__fPqFQ mdc-c-link___lBbY1_8032924" target="_self" data-layer-event-prefix="Download Link" data-layer-action="click" data-layer-report-type="" data-layer-file-name="gen-ais-next-inflection-point-from-employee-experimentation-to-organizational-transformation_final" data-layer-report-name="gen-ais-next-inflection-point-from-employee-experimentation-to-organizational-transformation_final>"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--size-md___yi5fA_8032924 mck-download-icon"></span><span class="mdc-c-link__label___Pfqtd_8032924"> (10 pages)</span></a></div></div></div></div> <p><strong>After nearly two years</strong> of debate, the verdict is in: generative AI (gen AI) is here to stay, and <a href="/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier">its business potential is massive</a>. We’ve already witnessed <a href="/capabilities/mckinsey-digital/our-insights/what-every-ceo-should-know-about-generative-ai">an exponential rate of gen-AI-related innovation</a>, 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.</p> <p>Despite all the buzz, most companies have yet to scratch the surface of gen AI’s promise. A recent <a href="/featured-insights/mckinsey-global-surveys">McKinsey Global Survey</a> reveals that employees are far ahead of their organizations in using gen AI,<span class="FootNote_footnote-holder__tjRqy"><a aria-label="footnote" href="javascript:void(0);" class="FootNote_footnote-wrapper__AIRwL undefined FootNote_bottom__9aoSL FootNote_inactive__VZfCp" aria-describedby="402235ff-378c-4974-8a68-969f48e559f6"><sup class="FootNote_footnotesup__e73z_">1</sup><span class="FootNote_notch-wrapper__b_5NS"><span class="FootNote_notch__omKtY"></span></span><span class="FootNote_tooltip__QtrbA mdc-u-mt-2"><span class="FootNote_footnote-content__r2OVl"><span id="402235ff-378c-4974-8a68-969f48e559f6" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">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.</span></span></span></a></span> 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 <a href="/featured-insights/mckinsey-live/webinars/never-just-tech-unlocking-the-full-value-of-gen-ai">technology alone won’t create value</a>. This means applying gen AI in ways that enable the business strategy: by reinventing operating models and <a href="/capabilities/mckinsey-digital/our-insights/choose-the-right-transformation-bite-size">entire domains</a>,<span class="FootNote_footnote-holder__tjRqy"><a aria-label="footnote" href="javascript:void(0);" class="FootNote_footnote-wrapper__AIRwL undefined FootNote_bottom__9aoSL FootNote_inactive__VZfCp" aria-describedby="ae8f94c9-1baf-4ac3-b7ae-c177fdd93c4f"><sup class="FootNote_footnotesup__e73z_">2</sup><span class="FootNote_notch-wrapper__b_5NS"><span class="FootNote_notch__omKtY"></span></span><span class="FootNote_tooltip__QtrbA mdc-u-mt-2"><span class="FootNote_footnote-content__r2OVl"><span id="ae8f94c9-1baf-4ac3-b7ae-c177fdd93c4f" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">That is, specific workflows, processes, journeys, and even functions.</span></span></span></a></span> by reimagining talent and skilling, and by reinforcing changes through robust governance and infrastructure.</p> <div data-module-category="" class="PullQuote_mck-c-pullquote__DbaQ5 mck-o-lg-center"><blockquote data-component="mdc-c-blockquote" class="PullQuote_mck-c-blockquote__6n21p PullQuote_mck-c-blockquote--hide-quotes__CpGpg mdc-c-blockquote mdc-c-blockquote--is-quotes___TozKo_8032924"><p>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.</p></blockquote></div> <h2>Employee use is at an inflection point, while their organizations lag behind</h2> <p>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.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-inline-module-container mck-o-md-center"><div class="mck-c-content-header"><div class="ContentHeader_mck-c-content-header__eyebrow__cBTe_"></div></div><div class="mck-u-inline-module-border-bottom"><picture data-component="mdc-c-picture" class="Exhibit_mck-c-exhibit__image__pyIDm"><source media="(min-width: 768px)" srcSet="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex1.svgz?cq=50&cpy=Center"/><img alt="Nearly all respondents, both generative AI users and nonusers, are optimistic about the technology’s impact on their work." src="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex1.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <p>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).<span class="FootNote_footnote-holder__tjRqy"><a aria-label="footnote" href="javascript:void(0);" class="FootNote_footnote-wrapper__AIRwL undefined FootNote_bottom__9aoSL FootNote_inactive__VZfCp" aria-describedby="40db04ca-36f3-446f-9771-c6a98f80ca67"><sup class="FootNote_footnotesup__e73z_">3</sup><span class="FootNote_notch-wrapper__b_5NS"><span class="FootNote_notch__omKtY"></span></span><span class="FootNote_tooltip__QtrbA mdc-u-mt-2"><span class="FootNote_footnote-content__r2OVl"><span id="40db04ca-36f3-446f-9771-c6a98f80ca67" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">We define early adopters as those companies, according to respondents, that have implemented six or more gen AI use cases to date.</span></span></span></a></span> 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.<span class="FootNote_footnote-holder__tjRqy"><a aria-label="footnote" href="javascript:void(0);" class="FootNote_footnote-wrapper__AIRwL undefined FootNote_bottom__9aoSL FootNote_inactive__VZfCp" aria-describedby="b8155f16-bf6a-4272-8862-a0c8e25f24d1"><sup class="FootNote_footnotesup__e73z_">4</sup><span class="FootNote_notch-wrapper__b_5NS"><span class="FootNote_notch__omKtY"></span></span><span class="FootNote_tooltip__QtrbA mdc-u-mt-2"><span class="FootNote_footnote-content__r2OVl"><span id="b8155f16-bf6a-4272-8862-a0c8e25f24d1" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">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).”</span></span></span></a></span></p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-inline-module-container mck-o-md-center"><div class="mck-c-content-header"><div class="ContentHeader_mck-c-content-header__eyebrow__cBTe_"></div></div><div class="mck-u-inline-module-border-bottom"><picture data-component="mdc-c-picture" class="Exhibit_mck-c-exhibit__image__pyIDm"><source media="(min-width: 768px)" srcSet="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex2.svgz?cq=50&cpy=Center"/><img alt="Organizations that are further along in their generative AI implementation tend to have a larger share of heavy users." src="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex2.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <p>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,<span class="FootNote_footnote-holder__tjRqy"><a aria-label="footnote" href="javascript:void(0);" class="FootNote_footnote-wrapper__AIRwL undefined FootNote_bottom__9aoSL FootNote_inactive__VZfCp" aria-describedby="6224d150-cb41-4cd3-95d7-98e1005a6675"><sup class="FootNote_footnotesup__e73z_">5</sup><span class="FootNote_notch-wrapper__b_5NS"><span class="FootNote_notch__omKtY"></span></span><span class="FootNote_tooltip__QtrbA mdc-u-mt-2"><span class="FootNote_footnote-content__r2OVl"><span id="6224d150-cb41-4cd3-95d7-98e1005a6675" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">Such as OpenAI’s ChatGPT and Microsoft’s Copilot.</span></span></span></a></span> 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.</p> <h2>The next inflection point: Moving from individual experimentation to strategic value capture</h2> <p>Technology <a href="/capabilities/strategy-and-corporate-finance/our-insights/in-digital-and-ai-transformations-start-with-the-problem-not-the-technology">adoption for its own sake has never created value</a>, 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.<span class="FootNote_footnote-holder__tjRqy"><a aria-label="footnote" href="javascript:void(0);" class="FootNote_footnote-wrapper__AIRwL undefined FootNote_bottom__9aoSL FootNote_inactive__VZfCp" aria-describedby="2084bd56-ed05-4265-ad9d-7b9bd487e112"><sup class="FootNote_footnotesup__e73z_">6</sup><span class="FootNote_notch-wrapper__b_5NS"><span class="FootNote_notch__omKtY"></span></span><span class="FootNote_tooltip__QtrbA mdc-u-mt-2"><span class="FootNote_footnote-content__r2OVl"><span id="2084bd56-ed05-4265-ad9d-7b9bd487e112" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">We define experimenters as those companies, according to respondents, that have implemented one to five gen AI use cases.</span></span></span></a></span></p> <div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="lightest-grey" data-module-category="" data-module-gradient-position="bottom-right" class="mck-c-inline-module-container SideBar_mck-c-sidebar__bgimg-wrapper__Qj4Dt mck-o-sm-left-span SideBar_mck-c-sidebar__sidebar-wrapper__Dpjw2 SideBar_mck-c-sidebar__sidebar-wrapper--istablet__IQ6ii mck-u-screen-only mck-c-module-wrapper" data-layer-region="sidebar"><div class="SideBar_mck-c-sidebar__epoAm mck-o-md-center"><div class="SideBar_mck-c-sidebar__share-icons-wrapper__9gB_c"><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_8032924 mdc-c-link-container--display-column___X0HDD_8032924 SideBar_mck-c-sidebar__share-icons___eQy6"><div data-component="mdc-c-dropdown-menu" class="mdc-c-drop-down"><button data-component="mdc-c-button" aria-label="" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium mdc-c-drop-down__rootmenu___yJzvz_8032924" aria-expanded="false" aria-haspopup="menu"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--default___f-hQM_8032924 mdc-c-icon--size-md___yi5fA_8032924 mck-share2-icon"></span></button><div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="white" data-module-category="" class=""></div></div><button data-component="mdc-c-button" aria-label="Expandable Sidebar" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium SideBar_mck-c-sidebar__toggle-btn__EL8iE" aria-expanded="false" data-layer-event-prefix="UI Item" data-layer-action="click" data-layer-category="sidebar" data-layer-subcategory="open" data-layer-text="open sidebar"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--radial___y3csX_8032924 mdc-c-icon--size-xxl___cL3ZT_8032924 mck-plus-no-circle-icon"></span></button></div></div><div class="SideBar_mck-c-sidebar__content-outer__UdWCq"><div class="SideBar_mck-c-sidebar__eyebrow__5GSEq"></div><div class="SideBar_mck-c-sidebar__content__raEwe"><h2 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-u-ts-3 SideBar_mck-c-sidebar__content-heading__NJekY"><div>“People led, tech powered”: Walmart’s vision for gen AI</div></h2><div class="SideBar_mck-c-sidebar__content-description__4p9iI mdc-u-ts-7"><div class="mdc-o-content-body"><p><strong>At Walmart,</strong> 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.</p></div></div></div></div></div></div> <p>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.</p> <h2>Reinvent domains by translating vision into value</h2>  <p>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.</p> <p>To start, companies should prioritize the right unit of transformation by focusing on specific domains, such as product development, marketing, and customer service. <a href="/capabilities/mckinsey-digital/our-insights/choose-the-right-transformation-bite-size">This domain-based approach</a> 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.</p> <p>Here are examples of what’s possible with a domain-based transformation, and the implications for roles and day-to-day work:</p> <ul> <li>In software development, gen AI can revolutionize work by delivering higher-quality, resilient products much faster; think days instead of months. This will require changes across the product life cycle and closer collaboration between product and engineering teams. Comprehensive product data, prompt-based proofs of concept, and automated requirements can shorten ideation-to-prototyping timelines, allowing for a greater number of iterations. The use of self-writing code, autogenerated user guidance, and continuous code testing would also transform engineers from task completers to systems designers.</li> <li>In marketing, gen AI could (finally) enable the vision of true personalization at scale. Companies such as Netflix and Spotify have started on this path with hyperpersonalized video previews and personalized user playlists. These types of practices can enhance engagement and loyalty, allow brands to integrate seamlessly into customers’ lives, boost productivity of content creation, and improve ROI across the sales and marketing funnel. By doing so, the marketing organization’s silos could break down, especially between the creative and analytics teams.</li> <li>In customer service, gen AI can transform teams into centers of customer delight by proactively addressing issues and offering new products, all at reduced costs. AI-empowered humans will work with <a href="/industries/technology-media-and-telecommunications/our-insights/the-promise-and-the-reality-of-gen-ai-agents-in-the-enterprise">gen AI agents</a>, using real-time trends and customer insights to become empathetic problem solvers and supervisors of customer experience. In the process, customer service agents and supervisors will make more use of technology, apply systems thinking, balance empathetic and commercial mindsets, and work more closely with the customer experience and product teams.</li> <li>Gen AI is also set to revolutionize cross-cutting domains, such as performance management and team management. For the latter, gen AI can put coaching prompts at managers’ fingertips and make it easier to access employee resources. This can meaningfully shift the time managers spend on certain tasks: less on administrative to-dos, more on checking in with team members and developing soft skills.</li> </ul> <h2>Reimagine talent and skilling by putting people at the center</h2> <p>As the examples above highlight, gen AI’s implications for talent and skill needs are massive. <a href="https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#work-and-productivity">The technology’s potential to accelerate automation</a> and transform operating models will significantly affect the roles and skills that organizations need. According to <a href="/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier">other McKinsey research</a>, half of today’s work activities could be automated between 2030 and 2060, accelerating <a href="/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works">previous, pre-gen-AI projections</a> 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.</p> <p>Our research shows that early adopters prioritize talent and <a href="/capabilities/people-and-organizational-performance/our-insights/the-human-side-of-generative-ai-creating-a-path-to-productivity">the human side of gen AI</a> 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.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-inline-module-container mck-o-md-center"><div class="mck-c-content-header"><div class="ContentHeader_mck-c-content-header__eyebrow__cBTe_"></div></div><div class="mck-u-inline-module-border-bottom"><picture data-component="mdc-c-picture" class="Exhibit_mck-c-exhibit__image__pyIDm"><source media="(min-width: 768px)" srcSet="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex3.svgz?cq=50&cpy=Center"/><img alt="Early adopters of generative AI are better than others at addressing talent- and training-related challenges, but all companies have room to improve." src="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex3.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <p>Companies can capitalize on employees’ enthusiasm for gen AI by investing in both technology adoption <em>and</em> skills (for more, see sidebar “Taking the granular view on gen AI’s workforce implications”). As <a href="/mgi/our-research/a-new-future-of-work-the-race-to-deploy-ai-and-raise-skills-in-europe-and-beyond">previous McKinsey research</a> 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.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="lightest-grey" data-module-category="" data-module-gradient-position="bottom-right" class="mck-c-inline-module-container SideBar_mck-c-sidebar__bgimg-wrapper__Qj4Dt mck-o-sm-left-span SideBar_mck-c-sidebar__sidebar-wrapper__Dpjw2 SideBar_mck-c-sidebar__sidebar-wrapper--istablet__IQ6ii mck-u-screen-only mck-c-module-wrapper" data-layer-region="sidebar"><div class="SideBar_mck-c-sidebar__epoAm mck-o-md-center"><div class="SideBar_mck-c-sidebar__share-icons-wrapper__9gB_c"><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_8032924 mdc-c-link-container--display-column___X0HDD_8032924 SideBar_mck-c-sidebar__share-icons___eQy6"><div data-component="mdc-c-dropdown-menu" class="mdc-c-drop-down"><button data-component="mdc-c-button" aria-label="" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium mdc-c-drop-down__rootmenu___yJzvz_8032924" aria-expanded="false" aria-haspopup="menu"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--default___f-hQM_8032924 mdc-c-icon--size-md___yi5fA_8032924 mck-share2-icon"></span></button><div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="white" data-module-category="" class=""></div></div><button data-component="mdc-c-button" aria-label="Expandable Sidebar" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium SideBar_mck-c-sidebar__toggle-btn__EL8iE" aria-expanded="false" data-layer-event-prefix="UI Item" data-layer-action="click" data-layer-category="sidebar" data-layer-subcategory="open" data-layer-text="open sidebar"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--radial___y3csX_8032924 mdc-c-icon--size-xxl___cL3ZT_8032924 mck-plus-no-circle-icon"></span></button></div></div><div class="SideBar_mck-c-sidebar__content-outer__UdWCq"><div class="SideBar_mck-c-sidebar__eyebrow__5GSEq"></div><div class="SideBar_mck-c-sidebar__content__raEwe"><h2 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-u-ts-3 SideBar_mck-c-sidebar__content-heading__NJekY"><div>Taking the granular view on gen AI’s workforce implications</div></h2><div class="SideBar_mck-c-sidebar__content-description__4p9iI mdc-u-ts-7"><div class="mdc-o-content-body"><p><strong>A collection of Asian financial institutions</strong> 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.</p></div></div></div></div></div></div> <p>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.</p> <p>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.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-disruptor1up mck-o-md-center mck-u-inline-module-border-top mck-u-inline-module-border-bottom mck-u-screen-only" data-layer-region="disruptor-1up"><div class="mdc-u-grid mdc-u-grid-gutter-md mdc-u-grid-col-lg-12 mdc-u-grid-col-md-12 "><div class="mdc-u-grid-col-md-span-12"><header data-component="mdc-c-header" class="mdc-c-header"><div class="mdc-c-header__block___i1Lg-_8032924"><h3 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924"><div>Would you like to learn more about our <a href="/capabilities/people-and-organizational-performance/how-we-help-clients">People and Organizational Performance Practice</a>?</div></h3><div data-component="mdc-c-description" class="mdc-c-description___SrnQP_8032924 mdc-u-ts-7"><p><div><a href="/capabilities/people-and-organizational-performance/how-we-help-clients">Visit the page</a></div></p></div></div></header></div></div></div> <p>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.</p> <p>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.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="lightest-grey" data-module-category="" data-module-gradient-position="bottom-right" class="mck-c-inline-module-container SideBar_mck-c-sidebar__bgimg-wrapper__Qj4Dt mck-o-sm-left-span SideBar_mck-c-sidebar__sidebar-wrapper__Dpjw2 SideBar_mck-c-sidebar__sidebar-wrapper--istablet__IQ6ii mck-u-screen-only mck-c-module-wrapper" data-layer-region="sidebar"><div class="SideBar_mck-c-sidebar__epoAm mck-o-md-center"><div class="SideBar_mck-c-sidebar__share-icons-wrapper__9gB_c"><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_8032924 mdc-c-link-container--display-column___X0HDD_8032924 SideBar_mck-c-sidebar__share-icons___eQy6"><div data-component="mdc-c-dropdown-menu" class="mdc-c-drop-down"><button data-component="mdc-c-button" aria-label="" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium mdc-c-drop-down__rootmenu___yJzvz_8032924" aria-expanded="false" aria-haspopup="menu"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--default___f-hQM_8032924 mdc-c-icon--size-md___yi5fA_8032924 mck-share2-icon"></span></button><div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="white" data-module-category="" class=""></div></div><button data-component="mdc-c-button" aria-label="Expandable Sidebar" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium SideBar_mck-c-sidebar__toggle-btn__EL8iE" aria-expanded="false" data-layer-event-prefix="UI Item" data-layer-action="click" data-layer-category="sidebar" data-layer-subcategory="open" data-layer-text="open sidebar"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--radial___y3csX_8032924 mdc-c-icon--size-xxl___cL3ZT_8032924 mck-plus-no-circle-icon"></span></button></div></div><div class="SideBar_mck-c-sidebar__content-outer__UdWCq"><div class="SideBar_mck-c-sidebar__eyebrow__5GSEq"></div><div class="SideBar_mck-c-sidebar__content__raEwe"><h2 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-u-ts-3 SideBar_mck-c-sidebar__content-heading__NJekY"><div>About QuantumBlack, AI by McKinsey</div></h2><div class="SideBar_mck-c-sidebar__content-description__4p9iI mdc-u-ts-7"><div class="mdc-o-content-body"><p><strong>QuantumBlack,</strong> 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.</p></div></div></div></div></div></div> <p>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.</p> <h2>Reinforce the changes to continue transforming</h2> <p>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.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-disruptor1up mck-o-md-center mck-u-inline-module-border-top mck-u-inline-module-border-bottom mck-u-screen-only" data-layer-region="disruptor-1up"><div class="mdc-u-grid mdc-u-grid-gutter-md mdc-u-grid-col-lg-12 mdc-u-grid-col-md-12 "><div class="mdc-u-grid-col-lg-span-4 mdc-u-grid-col-md-span-5 Disruptor1Up_mck-c-disruptor1up__image___2Gc4"><picture data-component="mdc-c-picture"><img alt="McKinsey Global Surveys" src="/~/media/mckinsey/featured%20insights/mckinsey%20global%20surveys/mck-global-survey-landing-1284442931-thumb-1536x1536.jpg?cq=50&mh=145&car=16:9&cpy=Center" loading="lazy"/></picture></div><div class="mdc-u-grid-col-lg-span-8 mdc-u-grid-col-md-span-7"><header data-component="mdc-c-header" class="mdc-c-header"><div class="mdc-c-header__block___i1Lg-_8032924"><h3 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924"><div>McKinsey’s original survey research</div></h3></div></header><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_8032924 mdc-c-link-container--display-column___X0HDD_8032924 mck-c-disruptor1up__content Disruptor1Up_mck-c-disruptor1up__content--links__VV4lE mdc-u-grid-gutter-md"><a data-component="mdc-c-link" href="/featured-insights/mckinsey-global-surveys" class="mdc-c-link-cta___NBQVi_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Read more</span><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mck-link-arrow-right-icon"></span></a></div></div></div></div> <p>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.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="lightest-grey" data-module-category="" data-module-gradient-position="bottom-right" class="mck-c-inline-module-container SideBar_mck-c-sidebar__bgimg-wrapper__Qj4Dt mck-o-sm-left-span SideBar_mck-c-sidebar__sidebar-wrapper__Dpjw2 SideBar_mck-c-sidebar__sidebar-wrapper--istablet__IQ6ii mck-u-screen-only mck-c-module-wrapper" data-layer-region="sidebar"><div class="SideBar_mck-c-sidebar__epoAm mck-o-md-center"><div class="SideBar_mck-c-sidebar__share-icons-wrapper__9gB_c"><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_8032924 mdc-c-link-container--display-column___X0HDD_8032924 SideBar_mck-c-sidebar__share-icons___eQy6"><div data-component="mdc-c-dropdown-menu" class="mdc-c-drop-down"><button data-component="mdc-c-button" aria-label="" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium mdc-c-drop-down__rootmenu___yJzvz_8032924" aria-expanded="false" aria-haspopup="menu"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--default___f-hQM_8032924 mdc-c-icon--size-md___yi5fA_8032924 mck-share2-icon"></span></button><div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="white" data-module-category="" class=""></div></div><button data-component="mdc-c-button" aria-label="Expandable Sidebar" type="button" id="button_id" class="mdc-c-button___U4iY2_8032924 mdc-c-button--ghost mdc-c-button--size-medium SideBar_mck-c-sidebar__toggle-btn__EL8iE" aria-expanded="false" data-layer-event-prefix="UI Item" data-layer-action="click" data-layer-category="sidebar" data-layer-subcategory="open" data-layer-text="open sidebar"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mdc-c-icon--radial___y3csX_8032924 mdc-c-icon--size-xxl___cL3ZT_8032924 mck-plus-no-circle-icon"></span></button></div></div><div class="SideBar_mck-c-sidebar__content-outer__UdWCq"><div class="SideBar_mck-c-sidebar__eyebrow__5GSEq"></div><div class="SideBar_mck-c-sidebar__content__raEwe"><h2 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-u-ts-3 SideBar_mck-c-sidebar__content-heading__NJekY"><div>Good gen AI governance at work</div></h2><div class="SideBar_mck-c-sidebar__content-description__4p9iI mdc-u-ts-7"><div class="mdc-o-content-body"><p><strong>To enhance its productivity</strong> 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.</p></div></div></div></div></div></div> <p>The second step is treating these changes like <a href="/capabilities/mckinsey-digital/our-insights/rewired-to-outcompete">a true transformation</a>. 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.</p> <p>Third is addressing employee mindsets and behaviors across the organization. We know from <a href="/quarterly/the-five-fifty/five-fifty-what-it-takes-to-create-a-winning-transformation">extensive transformation research</a> 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 <a href="/capabilities/people-and-organizational-performance/our-insights/the-four-building-blocks--of-change">the influence model</a> that enables such changes: role modeling, fostering understanding and conviction, building capabilities, and reinforcing new ways of working (Exhibit 4).</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-inline-module-container mck-o-md-center"><div class="mck-c-content-header"><div class="ContentHeader_mck-c-content-header__eyebrow__cBTe_"></div></div><div class="mck-u-inline-module-border-bottom"><picture data-component="mdc-c-picture" class="Exhibit_mck-c-exhibit__image__pyIDm"><source media="(min-width: 768px)" srcSet="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex4.svgz?cq=50&cpy=Center"/><img alt="Early adopters of generative AI are more likely than others to focus on all tenets of the influence model, which enables mindset and behavior changes." src="/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/gen%20ais%20next%20inflection%20point%20from%20employee%20experimentation%20to%20organizational%20transformation/svgz_genaiinflection_ex4.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <p>In the gen AI context, this means:</p> <ul> <li><em>Role modeling.</em> Leaders should visibly adopt generative AI in their own ways of working. For instance, using AI tools to generate insights and make data-driven decisions showcases the technology’s benefits. It sets a strong example when a CEO uses AI to streamline workflows or a senior executive uses AI-driven analytics for business reviews, encouraging others to follow suit.</li> <li><em>Fostering understanding and conviction.</em> Organizations should communicate the reasons behind implementing gen-AI-related changes through internal communications, town hall meetings, and training sessions. Highlighting AI’s potential to improve efficiency, accuracy, and decision making aligns the team with the new direction. Informative content such as video tutorials and success stories can build collective conviction in AI’s advantages.</li> <li><em>Building capabilities. </em>Successful AI adoption requires comprehensive training programs. This includes training on data analysis, machine learning algorithms, and understanding AI-generated outputs. Collaborating with online education platforms to provide courses and setting up internal AI boot camps for hands-on experience ensures proficiency in AI technologies.</li> <li><em>Reinforcing new ways of working. </em>Companies should integrate AI goals into performance metrics and evaluation processes. They can set targets related to AI adoption, measure AI’s impact on key performance indicators, and recognize employees who effectively incorporate AI into their work. For instance, sales teams could set targets that include leveraging AI for customer segmentation and lead generation, with bonuses tied to successful AI-driven strategies. Tracking and celebrating milestones such as efficiency gains or innovative AI applications embeds these practices within the organization’s fabric.</li> </ul> <hr/> <p>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.</p></div><div class="container-placeholder"></div></div></div><div class="mdc-u-grid mdc-u-grid-gutter-xl"><section role="contentinfo" data-layer-region="article-about-authors" class="mdc-u-grid mdc-u-grid-col-md-12 AboutAuthor_mck-c-about-author__nRJzu"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-11"><h5 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-c-heading--title___5qyOB_8032924 mdc-c-heading--border___K8dj3_8032924 mdc-u-align-center"></h5><div data-component="mdc-c-description" class="mdc-c-description___SrnQP_8032924 mdc-u-ts-8 mck-u-links-inline mck-u-links-inline--secondary mdc-u-mt-5"><div><p><strong><a href="/our-people/charlotte-relyea">Charlotte Relyea</a></strong> is a senior partner in McKinsey’s New York office,<strong> <a href="/our-people/dana-maor">Dana Maor</a></strong> is a senior partner in the Tel Aviv office, <strong><a href="/our-people/sandra-durth">Sandra Durth</a></strong> is a partner in the Cologne office, and <strong>Jan Bouly</strong> is an associate partner in the Brussels office.</p> <p>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.</p> <hr /> <p>This article was edited by Daniella Seiler, an executive editor in the Washington, DC, office.</p></div></div></div></section><section class="mdc-u-grid mdc-u-grid-col-md-12 mck-u-screen-only"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-5 mdc-u-grid-col-lg-end-9"><h5 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-c-heading--title___5qyOB_8032924 mdc-c-heading--border___K8dj3_8032924 mdc-u-align-center">Explore a career with us</h5><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_8032924 mdc-c-link-container--align-center___ar3mu_8032924"><a data-component="mdc-c-link" href="/careers/search-jobs" class="mdc-c-button___U4iY2_8032924 mdc-c-button--secondary___Boipq_8032924 mdc-c-button--size-large___jwpUy_8032924" aria-label="Search Openings" data-layer-event-prefix="CTA Link" data-layer-action="click" data-layer-category="careers" data-layer-subcategory="search" data-layer-text="Search Openings"><span class="mdc-c-link__label___Pfqtd_8032924">Search Openings</span></a></div></div></section></div></main></div><div data-component="mdc-c-module-wrapper" data-module-theme="light" data-module-background="lightest-grey" data-module-category="StandalonePromo" class="RelatedArticle_mck-c-article-related__GGA76 mck-u-screen-only" data-layer-region="related-articles"><div class="mdc-o-container__wrapper is-wrapped mdc-u-spaced-mobile"><h5 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-c-heading--title___5qyOB_8032924 mdc-u-align-center">Related Articles</h5><div class="mdc-u-grid mdc-u-grid-col-md-3 RelatedArticle_items-container__s2uD0"><div class="mdc-u-grid mdc-u-grid-gutter-lg Card_card__diA2r Card_hover-effect__RGb9b"><div class="Card_wrapper-image__8b4P6"><a data-component="mdc-c-link" href="/capabilities/people-and-organizational-performance/our-insights/the-human-side-of-generative-ai-creating-a-path-to-productivity" class="mdc-c-link___lBbY1_8032924"><picture data-component="mdc-c-picture" class="Card_wrapper-image__8b4P6"><style>.picture-uniqueKey-theh-0 { aspect-ratio: 16/9 }</style><img alt="A man and woman in office attire made out of flowing digital wires. 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To capture value from current momentum, businesses must transform their processes, structures, and approach to talent."}},"sEODescription":{"value":"As employee adoption of gen AI grows, companies are struggling to follow suit. We look at how organizations can transform their approach to talent and AI."},"displayDate":{"jsonValue":{"value":"2024-08-07T00:00:00Z"}},"body":{"value":"[[audio 1]]\n[[DownloadsSidebar]]\n\u003cp\u003e\u003cstrong\u003eAfter nearly two years\u003c/strong\u003e of debate, the verdict is in: generative AI (gen AI) is here to stay, and \u003ca href=\"/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier\"\u003eits business potential is massive\u003c/a\u003e. We\u0026rsquo;ve already witnessed \u003ca href=\"/capabilities/mckinsey-digital/our-insights/what-every-ceo-should-know-about-generative-ai\"\u003ean exponential rate of gen-AI-related innovation\u003c/a\u003e, 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.\u003c/p\u003e\n\u003cp\u003eDespite all the buzz, most companies have yet to scratch the surface of gen AI\u0026rsquo;s promise. A recent \u003ca href=\"/featured-insights/mckinsey-global-surveys\"\u003eMcKinsey Global Survey\u003c/a\u003e\u0026nbsp;reveals that employees are far ahead of their organizations in using gen AI,[[footnote 1]] as companies have been slow to adopt in ways that could realize gen AI\u0026rsquo;s trillion-dollar opportunity. To harness employees\u0026rsquo; enthusiasm and stay ahead, companies need a holistic approach to transforming how the whole organization works with gen AI; the \u003ca href=\"/featured-insights/mckinsey-live/webinars/never-just-tech-unlocking-the-full-value-of-gen-ai\"\u003etechnology alone won\u0026rsquo;t create value\u003c/a\u003e. This means applying gen AI in ways that enable the business strategy: by reinventing operating models and \u003ca href=\"/capabilities/mckinsey-digital/our-insights/choose-the-right-transformation-bite-size\"\u003eentire domains\u003c/a\u003e,[[footnote 2]] by reimagining talent and skilling, and by reinforcing changes through robust governance and infrastructure.\u003c/p\u003e\n[[pullquote 1]]\n\u003ch2\u003eEmployee use is at an inflection point, while their organizations lag behind\u003c/h2\u003e\n\u003cp\u003eAccording to our research, employees are forging ahead with gen AI, a broadly accessible technology that puts AI\u0026rsquo;s potential at everyone\u0026rsquo;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.\u003c/p\u003e\n[[exhibit 1]]\n\u003cp\u003eIn 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\u0026rsquo; companies have implemented multiple use cases, a group we call \u0026ldquo;early adopters\u0026rdquo; (Exhibit 2).[[footnote 3]] Among them, there\u0026rsquo;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.[[footnote 4]]\u003c/p\u003e\n[[exhibit 2]]\n\u003cp\u003eThe 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,[[footnote 5]] which is increasing curiosity and encouraging greater openness to experimentation. Yet he notes that there\u0026rsquo;s no easy-to-prove business case for employee-driven adoption and the piecemeal implementation of use cases.\u003c/p\u003e\n\u003ch2\u003eThe next inflection point: Moving from individual experimentation to strategic value capture\u003c/h2\u003e\n\u003cp\u003eTechnology \u003ca href=\"/capabilities/strategy-and-corporate-finance/our-insights/in-digital-and-ai-transformations-start-with-the-problem-not-the-technology\"\u003eadoption for its own sake has never created value\u003c/a\u003e, 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 \u0026ldquo;\u0026lsquo;People led, tech powered\u0026rsquo;: Walmart\u0026rsquo;s vision for gen AI\u0026rdquo;). Our survey findings suggest that early adopters are on track: 63 percent of early-adopter respondents say their organizations\u0026rsquo; AI and gen AI strategies align greatly with their business strategies, compared with only 17 percent of respondents at \u0026ldquo;experimenter\u0026rdquo; companies.[[footnote 6]]\u003c/p\u003e\n[[sidebar 1]]\n\u003cp\u003eTo capture gen AI\u0026rsquo;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\u0026rsquo;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.\u003c/p\u003e\n\u003ch2\u003eReinvent domains by translating vision into value\u003c/h2\u003e\n[[MostPopularArticles 5]]\n\u003cp\u003eCompanies can only reap gen AI\u0026rsquo;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.\u003c/p\u003e\n\u003cp\u003eTo start, companies should prioritize the right unit of transformation by focusing on specific domains, such as product development, marketing, and customer service. \u003ca href=\"/capabilities/mckinsey-digital/our-insights/choose-the-right-transformation-bite-size\"\u003eThis domain-based approach\u003c/a\u003e\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003eHere are examples of what\u0026rsquo;s possible with a domain-based transformation, and the implications for roles and day-to-day work:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIn software development, gen AI can revolutionize work by delivering higher-quality, resilient products much faster; think days instead of months. This will require changes across the product life cycle and closer collaboration between product and engineering teams. Comprehensive product data, prompt-based proofs of concept, and automated requirements can shorten ideation-to-prototyping timelines, allowing for a greater number of iterations. The use of self-writing code, autogenerated user guidance, and continuous code testing would also transform engineers from task completers to systems designers.\u003c/li\u003e\n \u003cli\u003eIn marketing, gen AI could (finally) enable the vision of true personalization at scale. Companies such as Netflix and Spotify have started on this path with hyperpersonalized video previews and personalized user playlists. These types of practices can enhance engagement and loyalty, allow brands to integrate seamlessly into customers\u0026rsquo; lives, boost productivity of content creation, and improve ROI across the sales and marketing funnel. By doing so, the marketing organization\u0026rsquo;s silos could break down, especially between the creative and analytics teams.\u003c/li\u003e\n \u003cli\u003eIn customer service, gen AI can transform teams into centers of customer delight by proactively addressing issues and offering new products, all at reduced costs. AI-empowered humans will work with \u003ca href=\"/industries/technology-media-and-telecommunications/our-insights/the-promise-and-the-reality-of-gen-ai-agents-in-the-enterprise\"\u003egen AI agents\u003c/a\u003e, using real-time trends and customer insights to become empathetic problem solvers and supervisors of customer experience. In the process, customer service agents and supervisors will make more use of technology, apply systems thinking, balance empathetic and commercial mindsets, and work more closely with the customer experience and product teams.\u003c/li\u003e\n \u003cli\u003eGen AI is also set to revolutionize cross-cutting domains, such as performance management and team management. For the latter, gen AI can put coaching prompts at managers\u0026rsquo; fingertips and make it easier to access employee resources. This can meaningfully shift the time managers spend on certain tasks: less on administrative to-dos, more on checking in with team members and developing soft skills.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003eReimagine talent and skilling by putting people at the center\u003c/h2\u003e\n\u003cp\u003eAs the examples above highlight, gen AI\u0026rsquo;s implications for talent and skill needs are massive. \u003ca href=\"https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#work-and-productivity\"\u003eThe technology\u0026rsquo;s potential to accelerate automation\u003c/a\u003e and transform operating models will significantly affect the roles and skills that organizations need. According to \u003ca href=\"/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier\"\u003eother McKinsey research\u003c/a\u003e, half of today\u0026rsquo;s work activities could be automated between 2030 and 2060, accelerating \u003ca href=\"/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works\"\u003eprevious, pre-gen-AI projections\u003c/a\u003e\u0026nbsp;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\u0026rsquo;t something companies can simply hire their way out of, as it affects the entire organization and its ways of working.\u003c/p\u003e\n\u003cp\u003eOur research shows that early adopters prioritize talent and \u003ca href=\"/capabilities/people-and-organizational-performance/our-insights/the-human-side-of-generative-ai-creating-a-path-to-productivity\"\u003ethe human side of gen AI\u003c/a\u003e\u0026nbsp;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\u0026rsquo;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.\u003c/p\u003e\n[[exhibit 3]]\n\u003cp\u003eCompanies can capitalize on employees\u0026rsquo; enthusiasm for gen AI by investing in both technology adoption \u003cem\u003eand\u003c/em\u003e skills (for more, see sidebar \u0026ldquo;Taking the granular view on gen AI\u0026rsquo;s workforce implications\u0026rdquo;). As \u003ca href=\"/mgi/our-research/a-new-future-of-work-the-race-to-deploy-ai-and-raise-skills-in-europe-and-beyond\"\u003eprevious McKinsey research\u003c/a\u003e\u0026nbsp;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.\u003c/p\u003e\n[[sidebar 2]]\n\u003cp\u003eWith gen AI, building capabilities across the entire enterprise is crucial. As it\u0026rsquo;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.\u003c/p\u003e\n\u003cp\u003eWithin 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.\u003c/p\u003e\n[[Disruptor1UP dis1]]\n\u003cp\u003eFor 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.\u003c/p\u003e\n\u003cp\u003eAnd for all employees, including leaders and managers, it\u0026rsquo;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.\u003c/p\u003e\n[[globalsidebar QB]]\n\u003cp\u003eA 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\u0026rsquo; 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.\u003c/p\u003e\n\u003ch2\u003eReinforce the changes to continue transforming\u003c/h2\u003e\n\u003cp\u003eHow, 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.\u003c/p\u003e\n[[Disruptor1UP surveys]]\n\u003cp\u003eThe first step is establishing the right governance for gen AI (for more, see sidebar \u0026ldquo;Good gen AI governance at work\u0026rdquo;). In our experience, this means creating a centralized structure that oversees the organization\u0026rsquo;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\u0026mdash;specifically, which experiments to scale or to stop, based on priorities and risks.\u003c/p\u003e\n[[sidebar 3]]\n\u003cp\u003eThe second step is treating these changes like \u003ca href=\"/capabilities/mckinsey-digital/our-insights/rewired-to-outcompete\"\u003ea true transformation\u003c/a\u003e. This means defining the transformation\u0026rsquo;s infrastructure, roles, and measurement criteria; ensuring accountability within business units; and implementing a regular cadence to monitor progress\u0026mdash;and adjusting as needed.\u003c/p\u003e\n\u003cp\u003eThird is addressing employee mindsets and behaviors across the organization. We know from \u003ca href=\"/quarterly/the-five-fifty/five-fifty-what-it-takes-to-create-a-winning-transformation\"\u003eextensive transformation research\u003c/a\u003e\u0026nbsp;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 \u003ca href=\"/capabilities/people-and-organizational-performance/our-insights/the-four-building-blocks--of-change\"\u003ethe influence model\u003c/a\u003e\u0026nbsp;that enables such changes: role modeling, fostering understanding and conviction, building capabilities, and reinforcing new ways of working (Exhibit 4).\u003c/p\u003e\n[[exhibit 4]]\n\u003cp\u003eIn the gen AI context, this means:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eRole modeling.\u003c/em\u003e Leaders should visibly adopt generative AI in their own ways of working. For instance, using AI tools to generate insights and make data-driven decisions showcases the technology\u0026rsquo;s benefits. It sets a strong example when a CEO uses AI to streamline workflows or a senior executive uses AI-driven analytics for business reviews, encouraging others to follow suit.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eFostering understanding and conviction.\u003c/em\u003e Organizations should communicate the reasons behind implementing gen-AI-related changes through internal communications, town hall meetings, and training sessions. Highlighting AI\u0026rsquo;s potential to improve efficiency, accuracy, and decision making aligns the team with the new direction. Informative content such as video tutorials and success stories can build collective conviction in AI\u0026rsquo;s advantages.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eBuilding capabilities. \u003c/em\u003eSuccessful AI adoption requires comprehensive training programs. This includes training on data analysis, machine learning algorithms, and understanding AI-generated outputs. Collaborating with online education platforms to provide courses and setting up internal AI boot camps for hands-on experience ensures proficiency in AI technologies.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eReinforcing new ways of working. \u003c/em\u003eCompanies should integrate AI goals into performance metrics and evaluation processes. They can set targets related to AI adoption, measure AI\u0026rsquo;s impact on key performance indicators, and recognize employees who effectively incorporate AI into their work. For instance, sales teams could set targets that include leveraging AI for customer segmentation and lead generation, with bonuses tied to successful AI-driven strategies. Tracking and celebrating milestones such as efficiency gains or innovative AI applications embeds these practices within the organization\u0026rsquo;s fabric.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr /\u003e\n\u003cp\u003eNo 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\u0026rsquo;s next inflection point, the downside of lagging behind\u0026mdash;and missing out on gen AI\u0026rsquo;s potential benefits\u0026mdash;may be even greater. With employees\u0026rsquo; embrace of gen AI and the technology\u0026rsquo;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\u0026rsquo;s operations and preparing people for continuous change.\u003c/p\u003e"},"isFullScreenInteractive":{"boolValue":false},"hideStickySocialShareBar":{"boolValue":false},"desktopID":{"value":""},"mobileID":{"value":""},"desktopURL":{"value":""},"mobileURL":{"value":""},"desktopPaddingPercentage":{"value":""},"mobilePaddingPercentage":{"value":""},"desktopOverrideHeight":{"value":""},"mobileOverrideHeight":{"value":""},"cerosOembedURL":{"value":""},"cerosRenderMode":{"targetItem":null},"cerosBackgroundColor":{"targetItem":null},"hideByLine":{"boolValue":false},"tableOfContentsTitle":{"value":"TABLE OF CONTENTS"},"accessStatus":{"targetItem":{"key":{"value":"RegisteredUsers"},"value":{"value":"Registered Users"}}},"articleType":{"targetItem":{"displayName":"Survey"}},"hasSpecialReport":{"boolValue":false},"contentType":{"targetItem":{"displayName":"Article"}},"sourcePublication":{"targetItem":null},"externalPublication":{"value":""},"excludeFromClientLink":{"boolValue":false},"originalPublishDate":{"jsonValue":{"value":"2024-08-07T00:00:00Z"}},"footnotes":{"value":"\u003col\u003e\n \u003cli\u003eThe 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.\u003c/li\u003e\n \u003cli\u003eThat is, specific workflows, processes, journeys, and even functions.\u003c/li\u003e\n \u003cli\u003eWe define early adopters as those companies, according to respondents, that have implemented six or more gen AI use cases to date.\u003c/li\u003e\n \u003cli\u003eSurvey respondents were asked to rate the frequency of their use of gen AI tools, both publicly available and internally developed tools, at work\u0026mdash;as well as their use of public tools for nonwork purposes. For all of these questions, potential responses were \u0026ldquo;never,\u0026rdquo; \u0026ldquo;rarely (that is, once per month),\u0026rdquo; \u0026ldquo;sometimes (that is, once per week),\u0026rdquo; \u0026ldquo;often (that is, two to three times per week),\u0026rdquo; and \u0026ldquo;almost always (that is, every day).\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eSuch as OpenAI\u0026rsquo;s ChatGPT and Microsoft\u0026rsquo;s Copilot.\u003c/li\u003e\n \u003cli\u003eWe define experimenters as those companies, according to respondents, that have implemented one to five gen AI use cases.\u003c/li\u003e\n\u003c/ol\u003e"},"contributoryPractice":{"targetItems":[{"displayName":"People \u0026amp; Organizational Performance"},{"displayName":"Talent"}]},"aboutTheAuthors":{"value":"\u003cp\u003e\u003cstrong\u003e\u003ca href=\"/our-people/charlotte-relyea\"\u003eCharlotte Relyea\u003c/a\u003e\u003c/strong\u003e is a senior partner in McKinsey\u0026rsquo;s New York office,\u003cstrong\u003e \u003ca href=\"/our-people/dana-maor\"\u003eDana Maor\u003c/a\u003e\u003c/strong\u003e is a senior partner in the Tel Aviv office, \u003cstrong\u003e\u003ca href=\"/our-people/sandra-durth\"\u003eSandra Durth\u003c/a\u003e\u003c/strong\u003e is a partner in the Cologne office, and \u003cstrong\u003eJan Bouly\u003c/strong\u003e is an associate partner in the Brussels office.\u003c/p\u003e\n\u003cp\u003eThe 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 Calv\u0026atilde;o, and Sanjna Parasrampuria for their contributions to this article.\u003c/p\u003e\n\u003chr /\u003e\n\u003cp\u003eThis article was edited by Daniella Seiler, an executive editor in the Washington, DC, office.\u003c/p\u003e"},"authors":{"targetItems":[{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"FBE0EA9B3EAC495EB809D4E1DB690F89","name":"Charlotte Relyea","authorTitle":{"value":"Charlotte Relyea"},"description":{"value":""},"thumbnailImage":{"alt":"","src":null},"profile":{"targetItems":[{"standardImage":{"src":"/~/media/mckinsey/our people/charlotte relyea/charlotte relyea_std_img.jpg","alt":"Charlotte Relyea"},"emailLinks":{"value":"Charlotte_Relyea@mckinsey.com"},"linkedInUrl":{"value":"https://www.linkedin.com/in/charlotte-relyea-35523a"},"description":{"value":"Works closely with clients to build organizational capabilities to sustain long-term growth."},"locations":{"targetItems":[{"name":"New York","displayName":"New York"}]},"firmTitle":{"value":"Senior Partner"},"thumbnailImage":{"alt":"Charlotte Relyea","src":"/~/media/mckinsey/our people/charlotte relyea/charlotte-relyea_headshot_988x741.jpg"},"url":{"path":"/our-people/charlotte-relyea"}}]}},{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"49390C2F1DC94A3AB8E891F847650A91","name":"Dana Maor","authorTitle":{"value":"Dana Maor"},"description":{"value":"Coheads the People \u0026 Organizational Performance Practice globally and leads it across Europe. Passionate about shaping organizations to compete, driving lasting transformational change, transforming talent, and unlocking leadership within client organizations, McKinsey, and society"},"thumbnailImage":{"alt":"","src":null},"profile":{"targetItems":[{"standardImage":{"src":"/~/media/mckinsey/our people/dana maor/dana-maor_profile_1536x1152.jpg","alt":"Dana Maor"},"emailLinks":{"value":"Dana_Maor@mckinsey.com"},"linkedInUrl":{"value":"https://www.linkedin.com/in/dana-maor-046455212/"},"description":{"value":"Coheads the People \u0026 Organizational Performance Practice globally and leads it across Europe. Passionate about shaping organizations to compete, driving lasting transformational change, transforming talent, and unlocking leadership within client organizations, McKinsey, and society"},"locations":{"targetItems":[{"name":"Tel Aviv","displayName":"Tel Aviv"}]},"firmTitle":{"value":"Senior Partner"},"thumbnailImage":{"alt":"Dana Maor","src":"/~/media/mckinsey/our people/dana maor/dana-maor_headshot_988x741.jpg"},"url":{"path":"/our-people/dana-maor"}}]}},{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"46125577F2BA47449895A5DA900C3CAF","name":"Sandra Durth","authorTitle":{"value":"Sandra Durth"},"description":{"value":""},"thumbnailImage":{"alt":"","src":null},"profile":{"targetItems":[{"standardImage":{"src":"/~/media/mckinsey/our people/sandra durth/sandra _durth_standard_profile_1536x1152.jpg","alt":"Sandra Durth"},"emailLinks":{"value":""},"linkedInUrl":{"value":"https://www.linkedin.com/in/sandrayrs/"},"description":{"value":"Drives stronger business performance by helping develop and guide transformational programs that inspire leaders and employees at all levels to adopt new ways of working, organizing, and leading"},"locations":{"targetItems":[{"name":"Cologne","displayName":"Cologne"}]},"firmTitle":{"value":"Partner"},"thumbnailImage":{"alt":"Sandra Durth","src":"/~/media/mckinsey/our people/sandra durth/sandra _durth_headshot_988x741.jpg"},"url":{"path":"/our-people/sandra-durth"}}]}}]},"nonPartnerAuthors":{"targetItems":[{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"DC5397B48E6747D2A79E671E30E5BA6F","name":"Jan Bouly","authorTitle":{"value":"Jan Bouly"},"description":{"value":"Partners with financial institutions in designing future-proof organizational and workforce strategies"},"thumbnailImage":{"alt":"Photo of Jan Bouly, McKinsey \u0026 Company","src":"/~/media/mckinsey/our people/jan bouly/jan bouly_988x741.png"},"profile":{"targetItems":[]}}]},"interactiveToUse":{"targetItem":null},"enableArticleComponents":{"boolValue":false},"relatedArticles":{"targetItems":[{"sourcePublication":{"targetItem":{"name":"McKinsey Quarterly"}},"publicationSource":null,"externalPublication":{"value":""},"title":{"value":"The human side of generative AI: Creating a path to productivity"},"url":{"path":"/capabilities/people-and-organizational-performance/our-insights/the-human-side-of-generative-ai-creating-a-path-to-productivity"},"eyebrow":{"targetItem":{"name":"Article"}},"articleType":{"targetItem":{"name":"Article"}},"contentType":{"targetItem":{"name":"Article"}},"description":{"value":"The number of employees who use generative AI is expected to soar, freeing many to focus on higher-level cognitive work. 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