“Organizations are beginning to create the structures and processes that lead to meaningful value from gen AI,” said “The State of AI — How organizations are rewiring to capture value,” a report published by Quantum Black, McKinsey’s AI unit, in March of 2025. The report noted that organizations are taking steps to drive gen AI’s bottom-line impact, such as redesigning workflows and putting senior leaders in critical roles overseeing AI governance. “While still in early days, companies are redesigning workflows, elevating governance, and mitigating more risks.”
The report is based on a McKinsey Global Survey conducted in July of 2024. The online survey received responses from almost 1,500 participants from over 100 nations from a diverse set of regions, industries, and company sizes. 42% of respondents came from companies with at least $500 million in annual revenues, which are changing faster than smaller organizations. Overall, the use of gen AI and 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.”
Let me summarize the report’s key findings.
Organizations are selectively centralizing elements of their AI deployment.
Risk and data governance are two of the most centralized elements of deploying AI solutions.
Degree of centralization of AI deployment
- Risk and compliance: 57% centralized, 13% distributed, 30% hybrid.
- Data governance: 46% centralized, 15% distributed, 39% hybrid.
- AI strategy: 36% centralized, 16% distributed, 48% hybrid.
- Roadmap for AI products: 35% centralized, 21% distributed, 44% hybrid.
- Technology talent: 29% centralized, 22% distributed, 49% hybrid.
- Adoption of AI solutions: 23% centralized, 23% distributed, 44% hybrid.
“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.”
Organizations vary widely in how they monitor gen AI outputs.
Respondents are about equally likely to say their organizations review all gen AI outputs (27%) as they are to say few are reviewed (30%).
“Respondents working in business, legal, and other professional services are much more likely than those in other industries to say that all outputs are reviewed.”
Best practices for adoption and scaling AI can enable bottom-line impact.
Larger organizations, those with over $500 million in annual revenues, are following more adoption and scaling in each of 12 best practices for gen AI deployment than smaller organizations with under $500 million in annual revenues.
- Established a dedicated team to drive gen AI adoption: 52% larger versus 23% smaller.
- Have regular communications about the value created by gen AI: 42% vs 30%.
- Senior leaders are actively engaged in driving gen AI adoption: 37% vs 34%.
- Established role-based capability get AI training courses so for employees: 31 vs 17%.
- Embedded gen AI solutions into business processes effectively: 28% vs 21%.
- Established a clearly defined road map to drive adoption of gen AI solutions: 25% vs 12%.
- Have a mechanism to incorporate feedback on the performance of gen AI solution: 24% vs 19%.
- Created a comprehensive approach to foster trust among employees in gen AI use: 23% vs 21%.
- Established a compelling change story around the need for gen AI: 19% vs 16%.
- Track well-defined KPIs for gen AI solutions: 18% vs 16%.
- Created a comprehensive approach to foster trust among customers in gen AI’s use: 14% vs 8%.
- Established employee incentives that reinforce gen AI adoption: 11% larger vs 12% smaller.
However, “Less than one-third of respondents report that their organizations are following most of the 12 adoption and scaling practices for gen AI.”
AI is shifting the skills that organizations need.
Many respondents also say that their organizations have reskilled portions of their workforces as part of their AI deployment over the past year and they expect to undertake more reskilling in the years ahead.
Share of employees reskilled in the past year due to AI use: up to 10% (62%), 11% to 30% (22%), 31% to 50% (6%), more that 50% (9%).
Share of employees expected to be reskilled over the next 3 years due to AI use: up to 10% (28%), 11% to 30% (36%), 31% to 50% (17%), more than 50% (19%).
Expected change in employees’ use of each of these business functions as a result of gen AI use over the next 3 years
- Service operations: decrease (48%), increase (21%), no change (19%).
- Supply chain/inventory management: decrease (47%), increase (29%), no change (15%).
- HR: decrease (46%), increase (15%), no change (26%).
- Manufacturing: decrease (41%), increase (21%), no change (20%).
- Legal & compliance: decrease (37%), increase (23%), no change (21%).
- Strategy & finance: decrease (31%), increase (30%), no change (29%).
- Marketing & sales: decrease (30%), increase (21%), no change (38%).
- Software engineering: decrease (28%), increase (32%), no change (25%).
- Knowledge mgmt:. decrease (28%), increase (25%), no change (31%).
- IT: decrease (25%), increase (41%), no change (21%).
- Product/service development: decrease (23%), increase (34%), no change (33%).
Overall, a plurality of respondents (38%) predict that the use of AI will have little effect in their organizations workforce over the next three years, 31% predict that it will reduce the size of the workforce, and 19% predict that it will increase it.
AI use continues to climb.
Organizations’ use of AI has accelerated markedly in the past couple of years driven by the rapid growth in the use of gen AI.
Percentage of respondents that use AI in at least one business function: 2017 (20%), 2018 (47%), 2019 (58%), 2020 (50%), 2021 (56%), 2022 (50%), 2023 (55%), 1H2024 (72%), 2H2024 (78%).
Percentage of respondents using gen AI in at least one business function: 2023 (33%), 1H2024 (65%), 2H2024 (71%).
Industries using gen AI in at least one function: technology (88%), professional services (80%), advanced industries (79%), media & telecom (79%), consumer goods & retail (68%), financial services (65%), healthcare & pharma (63%), energy & materials (59%).
“Survey responses show that organizations are most often using gen AI in marketing and sales, product and service development, service operations, and software engineering.”
C-level executives are using gen AI more than others
Personal experience with gen AI tools in 2H2024: C-level executives (53%), senior managers 48%), mid-level managers (44%).
Finally, an increasing share of respondents report value creation, both increased revenues and cost reductions, in 2H204 in the business units using gen AI, but gen AI’s bottom-line impact are not yet material at the enterprise-wide level. “This emphasizes the need for companies to have a comprehensive approach across both AI and gen AI solutions to capture the full potential value.”