McKinsey State of AI

The state of AI March 2025 Alex Singla Alexander Sukharevsky Lareina Yee Michael Chui Bryce Hall How organizations are rewiring to capture value Organizations are beginning to create the structures and processes that lead to meaningful value from gen AI. While still in early days, companies are redesigning workflows, elevating governance, and mitigating more risks. Organizations are starting to make organizational changes designed to generate future value from gen AI, and large companies are leading the way. The latest McKinsey Global Survey on AI finds that organizations are beginning to take steps that drive bottom-line impact—for example, redesigning workflows as they deploy gen AI and putting senior leaders in critical roles, such as overseeing AI governance. The findings also show that organizations are working to mitigate a growing set of gen-AI-related risks and are hiring for new AI-related roles while they retrain employees to participate in AI deployment. Companies with at least $500 million in annual revenue are changing more quickly than smaller organizations. Overall, the use of AI—that is, gen AI as well as analytical AI—continues to build momentum: More than three-quarters of respondents now say that their organizations use AI in at least one business function. The use of gen AI in particular is rapidly increasing. 1The state of AI: How organizations are rewiring to capture value How companies are organizing their gen AI deployment— and who’s in charge Our survey analyses show that a CEO’s oversight of AI governance—that is, the policies, processes, and technology necessary to develop and deploy AI systems responsibly—is one element most correlated with higher self-reported bottom-line impact from an organization’s gen AI use.1 That’s particularly true at larger companies, where CEO oversight is the element with the most impact on EBIT attributable to gen AI. Twenty-eight percent of respondents whose organizations use AI report that their CEO is responsible for overseeing AI governance, though the share is smaller at larger organizations with $500 million or more in annual revenues, and 17 percent say AI governance is overseen by their board of directors. In many cases, AI governance is jointly owned: On average, respondents report that two leaders are in charge. The value of AI comes from rewiring how companies run, and the latest survey shows that, out of 25 attributes tested for organizations of all sizes, the redesign of workflows has the biggest effect on an organization’s ability to see EBIT impact from its use of gen AI. Organizations are beginning to reshape their workflows as they deploy gen AI. Twenty-one percent of respondents reporting gen AI use by their organizations say their organizations have fundamentally redesigned at least some workflows. 1 The correlation analyses considered 25 attributes and the reported effect of gen AI use on organizations’ EBIT, and using the Johnson’s Relative Weights regression analysis yielded an R-squared of 0.20. The attributes included which leaders oversee AI governance at organizations, how organizations are managing the time saved by gen AI deployment (for example, assigning completely new activities and fewer hours to employees, reducing head count), whether organizations have fundamentally redesigned at least some of their workflows as a result of gen AI deployment, and whether they have adopted each of 12 gen AI adoption and scaling best practices: 1) establishing a dedicated team to drive gen AI adoption (for example, a project management office, transformation office, or dedicated adoption and scaling team); 2) having regular internal communications about the value created by their gen AI solutions to build awareness and momentum; 3) having senior leaders who are actively engaged in driving gen AI adoption, including role modeling the use of gen AI; 4) embedding gen AI solutions into business processes effectively (for example, changing frontline employees’ processes, creating user interfaces to incorporate gen AI solutions); 5) establishing role-based capability training courses to make sure employees at each level know how to use gen AI capabilities appropriately; 6) creating a comprehensive approach to foster trust among employees in our use of gen AI (for example, understanding primary sources, mitigating inaccuracies); 7) having a mechanism to incorporate feedback on the performance of gen AI solutions and improve them over time; 8) establishing a clearly defined road map to drive adoption of gen AI solutions (for example, with phased rollouts across teams and business units); 9) establishing a compelling change story about the need for gen AI adoption; 10) tracking well-defined KPIs for gen AI solutions, enabling insights into their adoption and ROI; 11) establishing employee incentives that reinforce gen AI adoption; and 12) creating a comprehensive approach to foster trust among customers in our use of gen AI (for example, transparency on regulatory compliance, use of customer data). Twenty-eight percent of respondents whose organizations use AI report that their CEO is responsible for overseeing AI governance. 2The state of AI: How organizations are rewiring to capture value McKinsey commentary Alexander Sukharevsky Senior partner and global coleader of QuantumBlack, AI by McKinsey The more we see organizations using AI, the more we recognize that it takes a top- down process to really move the needle. Effective AI implementation starts with a fully committed C-suite and, ideally, an engaged board. Many companies’ instinct is to delegate implementation to the IT or digital department, but over and over again, this turns out to be a recipe for failure. There are several reasons for this. The first is that getting real value out of AI requires transformation, not just new technology. It’s a question of successful change management and mobilization, which is why C-suite leadership is essential. It’s also a potentially expensive transformation, requiring intensive use of sometimes scarce resources and talent. A lot rides on how those resources are made available, and that’s an executive-level call requiring nuanced decision-making that reflects the balance organizations must strike between efficient resource use and broad empowerment—a balance that must be constantly reevaluated as the technology and organization evolve. As organizations become more fluent with AI, it will essentially become embedded in all functions, leaving leadership to focus on higher-level tasks like impact monitoring and talent development rather than on implementation. Twenty-one percent of respondents reporting gen AI use by their organizations say their organizations have fundamentally redesigned at least some workflows. 3The state of AI: How organizations are rewiring to capture value Organizations are selectively centralizing elements of their AI deployment The survey findings also shed light on how organizations are structuring their AI deployment efforts. Some essential elements for deploying AI tend to be fully or partially centralized (Exhibit 1). For risk and compliance, as well as data governance, organizations often use a fully centralized model such as a center of excellence. For tech talent and adoption of AI solutions, on the other hand, respondents most often report using a hybrid or partially centralized model, with some resources handled centrally and others distributed across functions or business units—though respondents at organizations with less than $500 million in annual revenues are more likely than others to report fully centralizing these elements. Exhibit 1 Web <2024> <GenAI2024-2> Exhibit <1> of <14> Degree of centralization of AI deployment,¹ % of respondents McKinsey & Company ¹Question was asked only of respondents whose organizations use AI in at least 1 function, n = 1,229. Figures were calculated after removing the share who said “don’t know/not applicable.” Source: McKinsey Global Survey on the state of AI, 1,491 participants at all levels of the organization, July 16–31, 2024 Risk and data governance are two of the most centralized elements of deploying AI solutions, whereas tech talent is often hybrid. 57 46 36 35 29 23 30 39 48 44 49 54 13 15 16 21 22 23 Fully centralized (eg, a hub or center of excellence is responsible across the organization) Hybrid (eg, some resources are primarily centralized and some are distributed across function) Fully distributed (eg, all resources live within the business functions) Risk and compliance Data governance for AI AI strategy Road map for AI-enhanced or AI-focused products Tech talent (eg, data engineers and machine learning engineers) Adoption of AI solutions (including changing pro- cesses, change management) CENTRALIZED “HUB” MODEL DECENTRALIZED “SPOKE” MODEL 4The state of AI: How organizations are rewiring to capture value Exhibit 2 ¹Question was asked only of respondents whose organizations use AI in at least 1 function, n = 1,229. Figures were calculated after removing the share who said “don’t know/not applicable.” Source: McKinsey Global Survey on the state of AI, 1,491 participants at all levels of the organization, July 16–31, 2024 1 Only asked of respondents whose organizations regularly use gen Al in at least 1 function. Figures were calculated after removing the share who said “don’t know”; n = 830. Source: McKinsey Global Survey on the state of AI, 1,491 participants at all levels of the organization, July 16–31, 2024