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class="mck-o-container--wrapped mck-o-container--mobile-spacing mdc-u-grid mdc-u-grid-gutter-xxl"><section data-layer-region="article-body-header" class="mdc-u-grid mdc-u-grid-col-md-12 mck-u-animation-blur-in-400 byline-share-container"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-8 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-8 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/chandra-gnanasambandam" class="mdc-c-link-inline___7DRrt_8032924 mdc-c-link-inline--secondary___YKoOK_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Chandra Gnanasambandam</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/martin-harrysson" class="mdc-c-link-inline___7DRrt_8032924 mdc-c-link-inline--secondary___YKoOK_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Martin Harrysson</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/rikki-singh" class="mdc-c-link-inline___7DRrt_8032924 mdc-c-link-inline--secondary___YKoOK_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Rikki Singh</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">Chaitra Yarlagadda</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">A new study offers early lessons on how generative AI can improve product managers’ productivity, quality, and experience.</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%2Findustries%2Ftechnology%20media%20and%20telecommunications%2Fhigh%20tech%2Four%20insights%2Fhow%20generative%20ai%20could%20accelerate%20software%20product%20time%20to%20market%2Fhow-generative-ai-could-accelerate-software-product-time-to-market.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="how-generative-ai-could-accelerate-software-product-time-to-market" data-layer-report-name="how-generative-ai-could-accelerate-software-product-time-to-market>"><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"> (7 pages)</span></a></div></div></div></div> <p><strong>In today’s fast-paced and dynamic business landscape,</strong> software product managers (PMs) are crucial to product innovation and success, acting as the “mini CEOs” of products. In the quest to address customers’ needs, PMs are increasingly turning to generative AI (gen AI), with adoption of the technology growing exponentially in a matter of months.<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="045506ce-1c33-49de-b12f-96a655af9d79"><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="045506ce-1c33-49de-b12f-96a655af9d79" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">See “<a href="/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai">Unleashing developer productivity with generative AI</a>,” McKinsey, June 27, 2023.</span></span></span></a></span></p> <p>Over the past two decades, several trends have shaped the evolution of the PM role into a mini CEO: the shift to cloud, the growing importance of data in decision making, an increased focus on customer-led design, and growing expectations for responsible stewardship regarding privacy, sustainability, and inclusion. Now, gen AI is expected to reshape the role of PMs in a much more substantive way. This technology has the potential to not only augment and expand PMs’ roles and responsibilities but also fundamentally rewire the product development life cycle (PDLC) to achieve better customer outcomes on a shorter timeline.</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 the research</div></h2><div class="SideBar_mck-c-sidebar__content-description__4p9iI mdc-u-ts-7"><div class="mdc-o-content-body"><p>To understand the impact of generative AI (gen AI) on product management quality of output, experience, product time to market, and productivity, McKinsey recruited 40 product managers (PMs) with varying levels of product management experience from the United States, Canada, Europe, and Latin America to participate in a study. Data collected about each PM included years of experience working as a PM, familiarity with various technology stacks, adoption level of generative AI tools, and time typically spent on various tasks.</p> <p>Before participating in the study, the PMs attended a brief training workshop to learn about the case context and familiarize themselves with the tools. Participants were then asked to play the PM role for a fictional company and to work individually through five activities at their own pace. The activities mirrored the real-life work of PMs across the three phases of the product development life cycle (PDLC)—discovery, viability, and build—and required the PMs to generate deliverables including a market research document, a press release with frequently asked questions, a product one-pager, a product requirements document, and a product backlog. Tasks were arranged in chronological order along the PDLC to emulate their real-life execution flow to the extent possible.</p> <p>McKinsey divided participants into three groups and asked them to complete activities in the three stages of the PDLC using varying levels of access to gen AI tools. PMs either had access to task-specific gen AI tools (such as Copy.ai), ChatGPT only, or no gen AI tools. Each group rotated, and start and end times were recorded for all groups to measure the time spent on each task.</p> <p>After the study, PMs were asked about their experiences performing the activities and how helpful they found gen AI tools to be for completing each one. McKinsey team members then reviewed deliverables to assess their quality (accuracy and completeness). Finally, a post-study survey gathered participants’ feedback on the overall experience and the likelihood that they will adopt the tools in the future.</p></div></div></div></div></div></div> <p>Given the technology’s newness and rapid pace of evolution, many product leaders are unsure which tools to select, which use cases to start with, and how to help PMs make the most of gen AI opportunities while adapting ways of working. McKinsey recently conducted empirical research on PMs in Europe and the Americas to understand the potential impact of using gen AI–based tools in their roles (see sidebar, “About the research”). The results were promising. Gen AI accelerated product time to market by 5 percent, improved PM productivity by 40 percent, and even uplifted employee experience by 100 percent. This article details findings from the study to offer an early look at the quantifiable impact of gen AI on PM roles and offers insights on how product leaders can translate these findings into impact.</p> <h2>The impact of gen AI on product management tasks</h2> <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>Participants in the PM study were divided into three groups. Each group rotated through the three phases of the PDLC to individually complete the activities using varying levels of access to gen AI tools. The PMs’ experience and performance were then assessed, and several insights emerged.</p> <h3>Accelerated time to market</h3> <p>PMs who used gen AI tools—either generic tools such as ChatGPT or task-specific tools—took less time, on average, to complete activities than PMs who did not use them, resulting in an acceleration of time to market by about 5 percent across a six-month PDLC. The time savings were driven by the use of gen AI to synthesize user research and write press releases in the discovery phase; develop product one-pagers and write product requirement documents in the viability phase; and create product backlogs in the build phase. The acceleration in time to market assumes that current PDLC activities are completed more quickly and the roles and activities involved beyond PMs are rewired to support the acceleration. The adoption of gen AI tools may allow PMs to shift time and resources to more-strategic activities such as defining the product vision, creating a long-term road map, and conducting customer-facing activities.</p> <h3>Significant uplift in PM experience</h3> <p>Strikingly, 100 percent of the participants reported an improvement in their activity experience when using gen AI tools (Exhibit 1). All but one of the PMs reported the tools were helpful with the tasks and that they would be highly or somewhat likely to use these tools in their work after the study ended. And three out of four believed the quality of their deliverables was either largely improved or somewhat improved compared to what they achieved without them. PMs perceived the tools as automating their “high toil” tasks and enabling them to focus on what mattered.</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/industries/technology%20media%20and%20telecommunications/high%20tech/our%20insights/how%20generative%20ai%20could%20accelerate%20software%20product%20time%20to%20market/svgz_mck239031-product-time-to-market-genai_exhibits_exh1.svgz?cq=50&cpy=Center"/><img alt="In our small-scale study, product managers reported a significant uplift in their experience when using generative AI tools." src="/~/media/mckinsey/industries/technology%20media%20and%20telecommunications/high%20tech/our%20insights/how%20generative%20ai%20could%20accelerate%20software%20product%20time%20to%20market/svgz_mck239031-product-time-to-market-genai_exhibits_exh1.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <h3>Increased productivity</h3> <p>Gen AI tools had almost twice as much positive impact on content-heavy tasks—gathering and synthesizing information, creating and polishing content, and brainstorming—as on content-light tasks such as data gathering and visualization (Exhibit 2). In part, this is because content-heavy tasks have more potential for automation and because gen AI excels in extracting insights from unstructured data and outpacing human content generation. In other words, content-heavy tasks inherently possess greater opportunities to harness the full potential of gen AI.</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/industries/technology%20media%20and%20telecommunications/high%20tech/our%20insights/how%20generative%20ai%20could%20accelerate%20software%20product%20time%20to%20market/svgz_mck239031-product-time-to-market-genai_exhibits_exh2.svgz?cq=50&cpy=Center"/><img alt="Generative AI tools are especially useful in increasing product managers' productivity with content-heavy tasks." src="/~/media/mckinsey/industries/technology%20media%20and%20telecommunications/high%20tech/our%20insights/how%20generative%20ai%20could%20accelerate%20software%20product%20time%20to%20market/svgz_mck239031-product-time-to-market-genai_exhibits_exh2.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <p>General-purpose tools such as ChatGPT were more readily adopted by PMs than task-specific tools, and they resulted in about twice the productivity gains. This result is likely attributable to the fact that general tools are more familiar to PMs and thus easier to use than specialized tools. Furthermore, some specific gen AI tools are designed to address more tailored use cases and therefore require specific input and prompts that PMs are not accustomed to. In contrast, general-purpose tools allow PMs to iterate with flexibility and use the tools as partners in solving problems.</p> <h3>Improved quality of deliverables</h3> <p>Observation of the PMs and review of their deliverables suggest that on average, gen AI tools enabled them to iterate more rapidly and produce more accurate and complete output. However, the impact of gen AI generally varied based on the experience levels of the PMs using them. PMs with more years of experience maintained a high quality of output, whereas more junior PMs gained productivity but at the expense of quality.</p> <p>Our hypothesis is that senior PMs, given their familiarity with the deliverables and their stronger product sense, can better review the gen AI tools’ output and ensure high accuracy and writing fluency. More junior PMs, conversely, are still learning how to produce high-quality deliverables and cannot yet effectively review gen AI outputs. Clearly, gen AI cannot replace the foundational skills needed to be a PM, but it can help PMs develop those skills.</p> <p>An increase in the quality of deliverables has broader strategic implications. Better insights and synthesis of customer needs, potential impact, and solution overview can lead to a virtuous cycle of higher-quality products and better adoption. To meet these goals, some organizations are turning to gen AI tools to accelerate their journey toward product-led growth.</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>Subscribe to the <em>McKinsey on Building Products</em> podcast</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="https://podcasts.apple.com/us/podcast/mckinsey-on-building-product/id1735607317" class="mdc-c-button___U4iY2_8032924 mdc-c-button--primary___Ed-lT_8032924 mdc-c-button--size-large___jwpUy_8032924" target="_blank"><span class="mdc-c-link__label___Pfqtd_8032924">Apple Podcasts</span></a><a data-component="mdc-c-link" href="https://open.spotify.com/show/79pncGJtBtzz7pFoR6OiH8" class="mdc-c-button___U4iY2_8032924 mdc-c-button--primary___Ed-lT_8032924 mdc-c-button--size-large___jwpUy_8032924" target="_blank"><span class="mdc-c-link__label___Pfqtd_8032924">Spotify</span></a><a data-component="mdc-c-link" href="/capabilities/people-and-organizational-performance/how-we-help-clients/mckinsey-academy/product-academy/mckinsey-on-building-product" class="mdc-c-link-cta___NBQVi_8032924"><span class="mdc-c-link__label___Pfqtd_8032924">Listen to previous episodes</span><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_8032924 mck-link-arrow-right-icon"></span></a></div></div></div></div> <h2>Implications for product leaders</h2> <p>Technology leaders can use these findings to translate higher productivity, improved experience, and quality improvements into greater customer and product impact. As with most fundamental shifts in operating model, adopting gen AI in product management requires a thoughtful, phased approach. Leaders can keep five steps in mind as they embark on this journey to minimize the risks of using gen AI in product management.</p> <h3>Develop a road map linked to customer outcomes</h3> <p>Creating a road map for gen AI adoption is essential to build momentum and conviction within the organization. Leaders can start by establishing goals for the adoption. For software engineers, the goal of adopting gen AI tools is typically to meet productivity goals. For PMs, however, goals should be linked to customer outcomes, such as better customer satisfaction scores driven by product quality.</p> <p>Once leaders have established targets for customer outcomes, they should carefully assess and prioritize key use cases in the product development process based on holistic customer impact and the maturity of tools. One common pitfall is picking use cases ad hoc based on the most readily available tool, rather than strategically selecting use cases that align with the organization’s goals and have the potential to deliver the most significant customer impact.</p> <p>Once use cases are identified, product leaders can take a phased approach to gen AI adoption, starting with simpler use cases, such as document writing and idea generation. Introducing use cases to a small group of PMs before rolling them out to everyone allows for easy testing and refinement of the approach, which is critical to understand where PMs need coaching or oversight to mitigate risks and ensure that quality does not decline. In parallel, product leaders can set a baseline for organization-wide PM capability as well as PDLC maturity. Our research shows that output quality is highly contingent on PM experience. Baselining helps leaders understand which PM skills may need augmentation as they begin planning to adopt at scale. To get the most out of this adoption, leaders will need to rewire the PDLC to be aligned with the maturity baseline and inform the pace and approach for rollout.</p> <h3>Integrate tooling into PM flow</h3> <p>Another foundational aspect of effective gen AI adoption is establishing the right AI tool stack for PMs—which can be challenging given how quickly the landscape is evolving. Each new tool brings a unique mix of strengths and capabilities, and leaders must consider how new tools integrate into existing PM workflows and their potential in distinct use cases as well as across categories. Taking these steps smooths the adoption process by minimizing switching between tools and enables structured and unstructured data (for example, product instrumentation data, latest details on product ideas or epics, customer feedback, and market trends) to flow seamlessly across the PM tool stack to ensure a single source of truth.</p> <h3>Provide PMs with gen AI coaching</h3> <p>As with much of the workforce, most PMs today will require a combination of upskilling and coaching to use the technology effectively to augment their daily work. Organizations should not expect people to adopt new tools organically and derive benefit from them. However, gen AI tools are only as effective as the PMs using them. Every PM has a distinct set of core skills, resulting in a wide variance in individuals’ effectiveness and outcomes when adopting gen AI tools. To ensure PMs can benefit from the use of these tools, organizations should invest in capability building to upskill PMs.</p> <h3>Provide risk controls</h3> <p>Although it may be tempting to implement gen AI tools quickly across the PDLC, measures to mitigate risks associated with ethics, privacy, intellectual property (IP), legal considerations, and interpretability<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="14091b69-55f7-4d72-9d2f-dbda4fe7e1e2"><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="14091b69-55f7-4d72-9d2f-dbda4fe7e1e2" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline">Gen AI tools are considered “interpretable” when PMs can readily discern how they arrived at the output they created.</span></span></span></a></span> are crucial. PMs can be trained to identify potential gen AI–related risks (for example, privacy and IP infringement) and concrete actions they can take to mitigate them (for example, review guidelines or consult with the legal function).</p> <p>As leaders update governance, they should consider three potential risks: first, data privacy is always a factor when tools can access confidential information. Proper training, strict access controls, and robust privacy safeguards are crucial. Second, companies must ensure their gen AI usage complies with all applicable (and, again, rapidly evolving) legal and regulatory changes, including changes to the European Union’s General Data Protection Regulation (GDPR). And third, organizations must set clear guidelines on the ethical use of AI for PMs, including respect for IP and proper attribution of generated ideas.</p> <h3>Reimagine the product development life cycle</h3> <p>While augmenting PM tasks provides some benefits, the ability to harness the power of gen AI in product development comes from reimagining and rewiring the PDLC. Some fundamental shifts in the PDLC could arise, including compression of the PDLC driven by gen AI–enabled PM and engineering execution; blurred boundaries between the discovery and viability phases as gen AI empowers PMs to rapidly prototype and test new customer-backed product ideas; and a shift in PMs’ responsibilities as they become more proficient in building marketing and sales collateral to accelerate commercial launch and adoption. Leaders should be aware of how the PDLC will change and prepare PMs to adapt accordingly.</p> <hr/> <p>In a rapidly changing business landscape, the transformative potential of gen AI for product management cannot be overstated. By embracing this cutting-edge technology, PMs can gain a decisive edge as they discover untapped opportunities and streamline product development processes. But research shows that simply adopting gen AI is not enough to realize the technology’s full potential. Product leaders should formulate a structured, phased approach anchored in priority customer outcomes to take advantage of the technology’s potential to deliver extraordinary benefits. In addition, they should consider adopting the gen AI functionality in tools they may already have. The pace of innovation in this domain is high, and delays in figuring out adoption approaches will have long-term consequences for talent and ways of working.</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/chandra-gnanasambandam">Chandra Gnanasambandam</a></strong> and <strong><a href="/our-people/martin-harrysson">Martin Harrysson</a></strong> are senior partners in McKinsey’s Bay Area office, where <strong><a href="/our-people/rikki-singh">Rikki Singh</a></strong> is a partner and <strong>Chaitra Yarlagadda</strong> is a consultant.</p> <p>The authors wish to thank Alejo Navarro Goldaraz, An Yan, Andrey Suprun, Annie Fang, Anuj Kohli, Ashay Makim, Assata Quinichett, Camilla Zanon di Valgiurata, Dev Seth, Dmitry Gafarov, Doria Du, Gustavo Torres da Silva, Lucy Xia, and Ryan Skinner for their contributions to this article.</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="/featured-insights/mckinsey-explainers/what-is-generative-ai" class="mdc-c-link___lBbY1_8032924"><picture data-component="mdc-c-picture" class="Card_wrapper-image__8b4P6"><style>.picture-uniqueKey-what-0 { aspect-ratio: 16/9 }</style><img alt="A green apple split into 3 parts on a gray background. 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" class="picture-uniqueKey-what-0" src="/~/media/mckinsey/featured%20insights/mckinsey%20explainers/what%20is%20generative%20ai/generative-ai-1219474321-thumb-1536x1536-v3.jpg?cq=50&mw=767&car=16:9&cpy=Center" loading="lazy"/></picture></a></div><div class="Card_wrapper-text__U6Y3k"><div data-component="mdc-c-content-block" class="mdc-c-content-block___7p6Lu_8032924 Card_content-block__pF6Z1"><span>Article</span><h6 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-u-ts-6"><a data-component="mdc-c-link" href="/featured-insights/mckinsey-explainers/what-is-generative-ai" class="mdc-c-link-heading___Zggl8_8032924 mdc-c-link___lBbY1_8032924"><div>What is generative AI?</div></a></h6></div></div></div><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/mckinsey-digital/our-insights/technologys-generational-moment-with-generative-ai-a-cio-and-cto-guide" class="mdc-c-link___lBbY1_8032924"><picture data-component="mdc-c-picture" class="Card_wrapper-image__8b4P6"><style>.picture-uniqueKey-techn-0 { aspect-ratio: 16/9 }</style><img alt="Digital generated image of abstract blue wave swirl flowing turbulent against grey background." class="picture-uniqueKey-techn-0" src="/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/technologys%20generational%20moment%20with%20generative%20ai%20a%20cio%20and%20cto%20guide/thumb-gettyimages-1470777844.jpg?cq=50&mw=767&car=16:9&cpy=Center" loading="lazy"/></picture></a></div><div class="Card_wrapper-text__U6Y3k"><div data-component="mdc-c-content-block" class="mdc-c-content-block___7p6Lu_8032924 Card_content-block__pF6Z1"><span>Article</span><h6 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-u-ts-6"><a data-component="mdc-c-link" href="/capabilities/mckinsey-digital/our-insights/technologys-generational-moment-with-generative-ai-a-cio-and-cto-guide" class="mdc-c-link-heading___Zggl8_8032924 mdc-c-link___lBbY1_8032924"><div>Technology’s generational moment with generative AI: A CIO and CTO guide</div></a></h6></div></div></div><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/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier" class="mdc-c-link___lBbY1_8032924"><picture data-component="mdc-c-picture" class="Card_wrapper-image__8b4P6"><style>.picture-uniqueKey-thee-0 { aspect-ratio: 16/9 }</style><img alt="Light dots and lines evolve into a pattern of a human face and continue to stream off the the side in a moving grid pattern." class="picture-uniqueKey-thee-0" src="/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20economic%20potential%20of%20generative%20ai%20the%20next%20productivity%20frontier/the-economic-potential-of-generative-ai-1324915617-thumb-1536x1536.jpg?cq=50&mw=767&car=16:9&cpy=Center" loading="lazy"/></picture></a></div><div class="Card_wrapper-text__U6Y3k"><div data-component="mdc-c-content-block" class="mdc-c-content-block___7p6Lu_8032924 Card_content-block__pF6Z1"><span>Report</span><h6 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_8032924 mdc-u-ts-6"><a data-component="mdc-c-link" href="/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier" class="mdc-c-link-heading___Zggl8_8032924 mdc-c-link___lBbY1_8032924"><div>The economic potential of generative AI: The next productivity frontier</div></a></h6></div></div></div></div></div></div></div></main></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"locale":"en","dictionary":{},"sitecoreContext":{"route":{"name":"How generative AI could accelerate software product time to market","displayName":"How generative AI could accelerate software product time to market","fields":null,"databaseName":"web","deviceId":"fe5d7fdf-89c0-4d99-9aa3-b5fbd009c9f3","itemId":"cc619949-faa6-4a90-a688-703ed7b7b74b","itemLanguage":"en","itemVersion":1,"layoutId":"ae753eb4-a035-40b4-83bf-4b4438df6742","templateId":"683910db-02ba-40ba-92e7-726c880160a9","templateName":"ArticleJSS","placeholders":{"jss-main":[{"uid":"232bb7e9-289f-492d-a916-2b6185e44a84","componentName":"ArticleTemplate","dataSource":"","fields":{"data":{"articleTemplate":{"title":{"jsonValue":{"value":"How generative AI could accelerate software product time to market"}},"sEOTitle":{"value":""},"description":{"jsonValue":{"value":"A new study offers early lessons on how generative AI can improve product managers\u0026rsquo; productivity, quality, and experience."}},"sEODescription":{"value":"The latest generation of AI-enabled project management tools can improve product managers’ productivity, quality, and experience. Here's how."},"displayDate":{"jsonValue":{"value":"2024-05-31T00:00:00Z"}},"body":{"value":"[[audio 1]]\n[[DownloadsSidebar]]\n\u003cp\u003e\u003cstrong\u003eIn today\u0026rsquo;s fast-paced and dynamic business landscape,\u003c/strong\u003e software product managers (PMs) are crucial to product innovation and success, acting as the \u0026ldquo;mini CEOs\u0026rdquo; of products. In the quest to address customers\u0026rsquo; needs, PMs are increasingly turning to generative AI (gen AI), with adoption of the technology growing exponentially in a matter of months.[[footnote 1]]\u003c/p\u003e\n\u003cp\u003eOver the past two decades, several trends have shaped the evolution of the PM role into a mini CEO: the shift to cloud, the growing importance of data in decision making, an increased focus on customer-led design, and growing expectations for responsible stewardship regarding privacy, sustainability, and inclusion. Now, gen AI is expected to reshape the role of PMs in a much more substantive way. This technology has the potential to not only augment and expand PMs\u0026rsquo; roles and responsibilities but also fundamentally rewire the product development life cycle (PDLC) to achieve better customer outcomes on a shorter timeline.\u003c/p\u003e\n[[sidebar 1]]\n\u003cp\u003eGiven the technology\u0026rsquo;s newness and rapid pace of evolution, many product leaders are unsure which tools to select, which use cases to start with, and how to help PMs make the most of gen AI opportunities while adapting ways of working. McKinsey recently conducted empirical research on PMs in Europe and the Americas to understand the potential impact of using gen AI\u0026ndash;based tools in their roles (see sidebar, \u0026ldquo;About the research\u0026rdquo;). The results were promising. Gen AI accelerated product time to market by 5 percent, improved PM productivity by 40 percent, and even uplifted employee experience by 100 percent. This article details findings from the study to offer an early look at the quantifiable impact of gen AI on PM roles and offers insights on how product leaders can translate these findings into impact.\u003c/p\u003e\n\u003ch2\u003eThe impact of gen AI on product management tasks\u003c/h2\u003e\n[[globalsidebar QB]]\n\u003cp\u003eParticipants in the PM study were divided into three groups. Each group rotated through the three phases of the PDLC to individually complete the activities using varying levels of access to gen AI tools. The PMs\u0026rsquo; experience and performance were then assessed, and several insights emerged.\u003c/p\u003e\n\u003ch3\u003eAccelerated time to market\u003c/h3\u003e\n\u003cp\u003ePMs who used gen AI tools\u0026mdash;either generic tools such as ChatGPT or task-specific tools\u0026mdash;took less time, on average, to complete activities than PMs who did not use them, resulting in an acceleration of time to market by about 5 percent across a six-month PDLC. The time savings were driven by the use of gen AI to synthesize user research and write press releases in the discovery phase; develop product one-pagers and write product requirement documents in the viability phase; and create product backlogs in the build phase. The acceleration in time to market assumes that current PDLC activities are completed more quickly and the roles and activities involved beyond PMs are rewired to support the acceleration. The adoption of gen AI tools may allow PMs to shift time and resources to more-strategic activities such as defining the product vision, creating a long-term road map, and conducting customer-facing activities.\u003c/p\u003e\n\u003ch3\u003eSignificant uplift in PM experience\u003c/h3\u003e\n\u003cp\u003eStrikingly, 100 percent of the participants reported an improvement in their activity experience when using gen AI tools (Exhibit 1). All but one of the PMs reported the tools were helpful with the tasks and that they would be highly or somewhat likely to use these tools in their work after the study ended. And three out of four believed the quality of their deliverables was either largely improved or somewhat improved compared to what they achieved without them. PMs perceived the tools as automating their \u0026ldquo;high toil\u0026rdquo; tasks and enabling them to focus on what mattered.\u003c/p\u003e\n[[Exhibit 1]]\n\u003ch3\u003eIncreased productivity\u003c/h3\u003e\n\u003cp\u003eGen AI tools had almost twice as much positive impact on content-heavy tasks\u0026mdash;gathering and synthesizing information, creating and polishing content, and brainstorming\u0026mdash;as on content-light tasks such as data gathering and visualization (Exhibit 2). In part, this is because content-heavy tasks have more potential for automation and because gen AI excels in extracting insights from unstructured data and outpacing human content generation. In other words, content-heavy tasks inherently possess greater opportunities to harness the full potential of gen AI.\u003c/p\u003e\n[[Exhibit 2]]\n\u003cp\u003eGeneral-purpose tools such as ChatGPT were more readily adopted by PMs than task-specific tools, and they resulted in about twice the productivity gains. This result is likely attributable to the fact that general tools are more familiar to PMs and thus easier to use than specialized tools. Furthermore, some specific gen AI tools are designed to address more tailored use cases and therefore require specific input and prompts that PMs are not accustomed to. In contrast, general-purpose tools allow PMs to iterate with flexibility and use the tools as partners in solving problems.\u003c/p\u003e\n\u003ch3\u003eImproved quality of deliverables\u003c/h3\u003e\n\u003cp\u003eObservation of the PMs and review of their deliverables suggest that on average, gen AI tools enabled them to iterate more rapidly and produce more accurate and complete output. However, the impact of gen AI generally varied based on the experience levels of the PMs using them. PMs with more years of experience maintained a high quality of output, whereas more junior PMs gained productivity but at the expense of quality.\u003c/p\u003e\n\u003cp\u003eOur hypothesis is that senior PMs, given their familiarity with the deliverables and their stronger product sense, can better review the gen AI tools\u0026rsquo; output and ensure high accuracy and writing fluency. More junior PMs, conversely, are still learning how to produce high-quality deliverables and cannot yet effectively review gen AI outputs. Clearly, gen AI cannot replace the foundational skills needed to be a PM, but it can help PMs develop those skills.\u003c/p\u003e\n\u003cp\u003eAn increase in the quality of deliverables has broader strategic implications. Better insights and synthesis of customer needs, potential impact, and solution overview can lead to a virtuous cycle of higher-quality products and better adoption. To meet these goals, some organizations are turning to gen AI tools to accelerate their journey toward product-led growth.\u003c/p\u003e\n[[Disruptor1UP buildingproductspodcast]]\n\u003ch2\u003eImplications for product leaders\u003c/h2\u003e\n\u003cp\u003eTechnology leaders can use these findings to translate higher productivity, improved experience, and quality improvements into greater customer and product impact. As with most fundamental shifts in operating model, adopting gen AI in product management requires a thoughtful, phased approach. Leaders can keep five steps in mind as they embark on this journey to minimize the risks of using gen AI in product management.\u003c/p\u003e\n\u003ch3\u003eDevelop a road map linked to customer outcomes\u003c/h3\u003e\n\u003cp\u003eCreating a road map for gen AI adoption is essential to build momentum and conviction within the organization. Leaders can start by establishing goals for the adoption. For software engineers, the goal of adopting gen AI tools is typically to meet productivity goals. For PMs, however, goals should be linked to customer outcomes, such as better customer satisfaction scores driven by product quality.\u003c/p\u003e\n\u003cp\u003eOnce leaders have established targets for customer outcomes, they should carefully assess and prioritize key use cases in the product development process based on holistic customer impact and the maturity of tools. One common pitfall is picking use cases ad hoc based on the most readily available tool, rather than strategically selecting use cases that align with the organization\u0026rsquo;s goals and have the potential to deliver the most significant customer impact.\u003c/p\u003e\n\u003cp\u003eOnce use cases are identified, product leaders can take a phased approach to gen AI adoption, starting with simpler use cases, such as document writing and idea generation. Introducing use cases to a small group of PMs before rolling them out to everyone allows for easy testing and refinement of the approach, which is critical to understand where PMs need coaching or oversight to mitigate risks and ensure that quality does not decline. In parallel, product leaders can set a baseline for organization-wide PM capability as well as PDLC maturity. Our research shows that output quality is highly contingent on PM experience. Baselining helps leaders understand which PM skills may need augmentation as they begin planning to adopt at scale. To get the most out of this adoption, leaders will need to rewire the PDLC to be aligned with the maturity baseline and inform the pace and approach for rollout.\u003c/p\u003e\n\u003ch3\u003eIntegrate tooling into PM flow\u003c/h3\u003e\n\u003cp\u003eAnother foundational aspect of effective gen AI adoption is establishing the right AI tool stack for PMs\u0026mdash;which can be challenging given how quickly the landscape is evolving. Each new tool brings a unique mix of strengths and capabilities, and leaders must consider how new tools integrate into existing PM workflows and their potential in distinct use cases as well as across categories. Taking these steps smooths the adoption process by minimizing switching between tools and enables structured and unstructured data (for example, product instrumentation data, latest details on product ideas or epics, customer feedback, and market trends) to flow seamlessly across the PM tool stack to ensure a single source of truth.\u003c/p\u003e\n\u003ch3\u003eProvide PMs with gen AI coaching\u003c/h3\u003e\n\u003cp\u003eAs with much of the workforce, most PMs today will require a combination of upskilling and coaching to use the technology effectively to augment their daily work. Organizations should not expect people to adopt new tools organically and derive benefit from them. However, gen AI tools are only as effective as the PMs using them. Every PM has a distinct set of core skills, resulting in a wide variance in individuals\u0026rsquo; effectiveness and outcomes when adopting gen AI tools. To ensure PMs can benefit from the use of these tools, organizations should invest in capability building to upskill PMs.\u003c/p\u003e\n\u003ch3\u003eProvide risk controls\u003c/h3\u003e\n\u003cp\u003eAlthough it may be tempting to implement gen AI tools quickly across the PDLC, measures to mitigate risks associated with ethics, privacy, intellectual property (IP), legal considerations, and interpretability[[footnote 2]] are crucial. PMs can be trained to identify potential gen AI\u0026ndash;related risks (for example, privacy and IP infringement) and concrete actions they can take to mitigate them (for example, review guidelines or consult with the legal function).\u003c/p\u003e\n\u003cp\u003eAs leaders update governance, they should consider three potential risks: first, data privacy is always a factor when tools can access confidential information. Proper training, strict access controls, and robust privacy safeguards are crucial. Second, companies must ensure their gen AI usage complies with all applicable (and, again, rapidly evolving) legal and regulatory changes, including changes to the European Union\u0026rsquo;s General Data Protection Regulation (GDPR). And third, organizations must set clear guidelines on the ethical use of AI for PMs, including respect for IP and proper attribution of generated ideas.\u003c/p\u003e\n\u003ch3\u003eReimagine the product development life cycle\u003c/h3\u003e\n\u003cp\u003eWhile augmenting PM tasks provides some benefits, the ability to harness the power of gen AI in product development comes from reimagining and rewiring the PDLC. Some fundamental shifts in the PDLC could arise, including compression of the PDLC driven by gen AI\u0026ndash;enabled PM and engineering execution; blurred boundaries between the discovery and viability phases as gen AI empowers PMs to rapidly prototype and test new customer-backed product ideas; and a shift in PMs\u0026rsquo; responsibilities as they become more proficient in building marketing and sales collateral to accelerate commercial launch and adoption. Leaders should be aware of how the PDLC will change and prepare PMs to adapt accordingly.\u003c/p\u003e\n\u003chr /\u003e\n\u003cp\u003eIn a rapidly changing business landscape, the transformative potential of gen AI for product management cannot be overstated. By embracing this cutting-edge technology, PMs can gain a decisive edge as they discover untapped opportunities and streamline product development processes. But research shows that simply adopting gen AI is not enough to realize the technology\u0026rsquo;s full potential. Product leaders should formulate a structured, phased approach anchored in priority customer outcomes to take advantage of the technology\u0026rsquo;s potential to deliver extraordinary benefits. In addition, they should consider adopting the gen AI functionality in tools they may already have. The pace of innovation in this domain is high, and delays in figuring out adoption approaches will have long-term consequences for talent and ways of working.\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":"Article"}},"hasSpecialReport":{"boolValue":false},"contentType":{"targetItem":{"displayName":"Article"}},"sourcePublication":{"targetItem":null},"externalPublication":{"value":""},"excludeFromClientLink":{"boolValue":false},"originalPublishDate":{"jsonValue":{"value":"2024-05-31T00:00:00Z"}},"footnotes":{"value":"\u003col\u003e\n \u003cli\u003eSee \u0026ldquo;\u003ca href=\"/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai\"\u003eUnleashing developer productivity with generative AI\u003c/a\u003e,\u0026rdquo; McKinsey, June 27, 2023.\u003c/li\u003e\n \u003cli\u003eGen AI tools are considered \u0026ldquo;interpretable\u0026rdquo; when PMs can readily discern how they arrived at the output they created.\u003c/li\u003e\n\u003c/ol\u003e"},"contributoryPractice":{"targetItems":[{"displayName":"Technology, Media \u0026 Telecommunications"},{"displayName":"QuantumBlack, AI by McKinsey"},{"displayName":"McKinsey Digital"}]},"aboutTheAuthors":{"value":"\u003cp\u003e\u003cstrong\u003e\u003ca href=\"/our-people/chandra-gnanasambandam\"\u003eChandra Gnanasambandam\u003c/a\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003ca href=\"/our-people/martin-harrysson\"\u003eMartin Harrysson\u003c/a\u003e\u003c/strong\u003e are senior partners in McKinsey\u0026rsquo;s Bay Area office, where \u003cstrong\u003e\u003ca href=\"/our-people/rikki-singh\"\u003eRikki Singh\u003c/a\u003e\u003c/strong\u003e is a partner and \u003cstrong\u003eChaitra Yarlagadda\u003c/strong\u003e is a consultant.\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank Alejo Navarro Goldaraz, An Yan, Andrey Suprun, Annie Fang, Anuj Kohli, Ashay Makim, Assata Quinichett, Camilla Zanon di Valgiurata, Dev Seth, Dmitry Gafarov, Doria Du, Gustavo Torres da Silva, Lucy Xia, and Ryan Skinner for their contributions to this 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