Briefing Document: The State of AI - How Organizations Are Rewiring to Capture Value (McKinsey, March 2025)

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Source: Excerpts from : The state of AI: How organizations are rewiring to capture value
 | Survey

Date of Report: March 2025 (Survey data collected July 16-31, 2024)

Key Authors: Alex Singla, Alexander Sukharevsky, Lareina Yee, Michael Chui, Bryce Hall

Executive Summary:

This McKinsey report analyzes the evolving landscape of AI adoption, particularly focusing on generative AI (gen AI), within organizations. The findings reveal a significant increase in both the use of AI and gen AI across various business functions. While still in the early stages of deployment, organizations are beginning to implement structural and process changes, including redesigning workflows, elevating governance, and mitigating risks, to capture meaningful value from gen AI. Larger companies are leading the way in these organizational shifts. The report highlights the correlation between top-down commitment (especially CEO oversight of AI governance) and bottom-line impact, the importance of workflow redesign, and the nascent adoption of key scaling best practices. While gen AI is generating revenue increases and cost reductions within specific business units, its impact on enterprise-wide EBIT remains limited for most organizations. The report also explores the evolving skills landscape, with a continued demand for AI talent and increasing focus on employee reskilling.


Main Themes and Important Ideas/Facts:


1. Rapid Increase in AI and Gen AI Adoption:

  • The use of AI in at least one business function has continued to climb, reaching 78% of surveyed organizations in July 2024, up from 72% in early 2024 and 55% the year prior.
  • Gen AI adoption is also accelerating, with 71% of respondents reporting regular use in at least one business function, compared to 65% in early 2024.
  • AI is being used across an increasing number of business functions, with most organizations now using it in more than one.
  • Gen AI is most frequently used in marketing and sales, product and service development, service operations, and software engineering.

Quote: "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."


2. Organizational Rewiring for Value Capture:

  • Organizations are beginning to create structures and processes to generate value from gen AI, including redesigning workflows, elevating governance, and mitigating risks.
  • Workflow redesign has the biggest effect on an organization's ability to see EBIT impact from gen AI use. 21% of respondents using gen AI report fundamentally redesigning at least some workflows.
  • CEO oversight of AI governance is strongly correlated with higher self-reported bottom-line impact from gen AI, especially in larger companies.
  • AI governance is often jointly owned by multiple leaders. 28% of respondents report CEO oversight, while 17% say the board is responsible.

Quote: "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."

Quote (McKinsey Commentary): "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."


3. Centralized vs. Decentralized AI Deployment:

  • Organizations are selectively centralizing elements of their AI deployment.
  • Risk and compliance, as well as data governance for AI, are often fully centralized (e.g., through a center of excellence).
  • Tech talent and adoption of AI solutions are more frequently managed through a hybrid or partially centralized model.

Quote: "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..."


4. Variable Monitoring and Mitigation of Gen AI Risks:

  • Organizations have employees overseeing the quality of gen AI outputs, but the extent of oversight varies widely. 27% review all content, while a similar share reviews 20% or less.
  • Organizations are increasingly working to mitigate gen-AI-related risks, particularly inaccuracy, cybersecurity, and intellectual property infringement.
  • Larger organizations report mitigating more risks than smaller ones, especially concerning cybersecurity and privacy.

Quote: "Twenty-seven percent of respondents whose organizations use gen AI say that employees review all content created by gen AI before it is used... A similar share says that 20 percent or less of gen-AI-produced content is checked before use."

Quote: "Respondents report increasing mitigation of inaccuracy, intellectual property infringement, and privacy risks related to use of gen AI."


5. Early Stages of Adoption and Scaling Best Practices:

  • Most respondents have yet to see organization-wide, bottom-line impact from gen AI.
  • Less than one-third of respondents report following most of the 12 adoption and scaling best practices identified as contributing to value creation.
  • The practice with the most impact on the bottom line is tracking well-defined KPIs for gen AI solutions. For larger organizations, having a clearly defined road map is also crucial.
  • Larger organizations are more likely to implement these best practices than smaller ones.

Quote: "Most respondents have yet to see organization-wide, bottom-line impact from gen AI use—and most aren’t yet implementing the adoption and scaling practices that we know from earlier research help create value when deploying new technologies."

Quote: "We asked respondents about 12 adoption- and scaling-related practices for gen AI and found that there are positive correlations on EBIT impact from each. The one with the most impact on the bottom line is tracking well-defined KPIs for gen AI solutions..."


6. Shifting Skills and Workforce Implications:

  • Hiring for AI-related roles remains prevalent, with new risk-focused roles like AI compliance specialists (13%) and AI ethics specialists (6%) emerging.
  • Difficulty in hiring for many AI roles is easing compared to previous years, except for AI data scientists, who remain in high demand. Half of respondents expect to need more data scientists in the next year.
  • Organizations have begun reskilling portions of their workforces due to AI, and more reskilling is expected in the next three years.
  • Time saved by gen AI is most often being redirected to entirely new activities or existing responsibilities. Larger organizations are more likely to report headcount reductions as a result of time savings.
  • A plurality of respondents (38%) predict little effect on workforce size in the next three years due to gen AI. However, financial services are more likely to expect workforce reductions.
  • Decreased headcount is most often expected in service operations and supply chain/inventory management, while increases are more likely in IT and product development.

Quote: "Half of respondents whose organizations use AI say their employers will need more data scientists over the next year."

Quote: "Many respondents expect to undertake more AI-related reskilling in the next three years than they conducted in the past year."

Quote (McKinsey Commentary): "Although we remain in the early stages of gen AI, we’re beginning to get a glimpse into the ways the technology is affecting the workforce. A common fear about the technology is that it will be a job killer... But our survey suggests that this is not necessarily the case. In fact, a plurality of respondents anticipate no immediate change to the size of their workforces."


7. Increasing Value Creation at the Business Unit Level:

  • An increasing share of respondents report revenue increases within business units using gen AI, comparable to those seen with analytical AI.
  • A majority of respondents now report cost reductions from gen AI use within most business functions.
  • However, over 80% of respondents say their organizations aren't seeing a tangible impact on enterprise-level EBIT from gen AI yet.

Quote: "An increasing share of respondents report value creation within the business units using gen AI. Compared with early 2024, larger shares of respondents say that their organizations’ gen AI use cases have increased revenue within the business units deploying them..."

Quote: "Yet gen AI’s reported effects on bottom-line impact are not yet material at the enterprise-wide level. More than 80 percent of respondents say their organizations aren’t seeing a tangible impact on enterprise-level EBIT from their use of gen AI."


8. Proliferation of Gen AI Content Creation:

  • The most common type of content generated by gen AI is text (63%).
  • Organizations are also experimenting with images (36%) and computer code (27%).

Quote: "Most respondents reporting use of gen AI—63 percent—say that their organizations are using gen AI to create text outputs, but organizations are also experimenting with other modalities."


9. C-Level Executives Leading in Personal Gen AI Use:

  • C-level executives are using gen AI more frequently than other employee levels, with 53% reporting regular use at work.

Quote: "Fifty-three percent of surveyed executives say they are regularly using gen AI at work, compared with 44 percent of midlevel managers."

Implications:

  • Organizations are moving beyond initial experimentation with gen AI towards more strategic implementation focused on value capture.
  • Top leadership engagement and a holistic, transformative vision are crucial for successful AI deployment.
  • Prioritizing workflow redesign and implementing robust governance frameworks are key drivers of bottom-line impact.
  • Adopting and scaling best practices, particularly around KPI tracking and roadmaps, will be essential for realizing the full potential of gen AI.
  • While job displacement is a concern, the immediate impact on overall workforce size appears limited, with a greater emphasis on reskilling and shifting skill demands.
  • Focus on risk mitigation and ensuring the quality of gen AI outputs remains critical.
  • While business units are seeing value, achieving enterprise-wide impact requires a more comprehensive and mature approach to gen AI integration.

Moving Forward:

The report suggests that the AI landscape, especially concerning gen AI, is still rapidly evolving. Organizations that embrace transformative thinking, prioritize strategic implementation, and focus on embedding AI into core workflows while actively managing risks and talent development are more likely to capture lasting value. The coming years will be critical in observing how organizations mature their gen AI strategies and translate early gains into significant enterprise-wide impact.

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