- Eric D. Brown, D.Sc.
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- Creating Real Value with AI
Creating Real Value with AI
Moving Beyond Experimentation to Organizational Transformation. New research reveals how companies are rewiring their structures and processes to capture real value from AI.
Eric Brown
March 18, 2025
In partnership with
A simple red arrow points upward against a backdrop of weathered yellow wooden planks—representing the structured, deliberate organizational changes companies must make to realize genuine value from AI implementation.
The latest McKinsey Global Survey on AI reveals something I've seen repeatedly in my career. There's a massive difference between companies discussing <insert new tech here> and those capturing value from it. This survey shows the same to be true with AI.
Organizations are beginning to make the structural changes needed to generate real results from generative AI, but we're still in the early days.
The Current State of AI Adoption
According to the survey, 78% of organizations now use AI in at least one business function, up from 72% in early 2024 and 55% a year earlier. For generative AI specifically, 71% of respondents report regular use in at least one function, an increase from 65% in early 2024.
What Drives Value From AI?
The survey identified several key factors that correlate with bottom-line impact. Let's examine the most important ones.
CEO Oversight of AI Governance
A compelling finding is the link between CEO involvement and AI success. Companies see a more significant financial impact when CEOs take direct responsibility for the policies, processes, and technology necessary for responsible AI development.
The survey shows that 28% of respondents whose organizations use AI report that their CEO oversees AI governance. This oversight particularly matters at larger companies,
and is
most strongly correlated with Earnings Before Interest and Taxes (EBIT) attributed to generative AI.
Fundamental Workflow Redesign
Of all organizational attributes tested, redesigning workflows showed the biggest effect on an organization's ability to see the EBIT impact of generative AI. Yet only 21% of respondents say their organizations have fundamentally redesigned workflows as they deploy AI.
This finding confirms what I've seen in practice: bolting AI onto existing processes yields limited results. The real value comes from rethinking how work gets done with AI as an integral component.
Strategic Centralization
Organizations are selectively centralizing elements of their AI deployment. The report shows that risk, compliance and data governance tend to be managed through fully centralized models like centers of excellence. Meanwhile, tech talent and AI solution adoption more often use hybrid approaches.
This pattern allows organizations to maintain consistent standards for critical aspects like risk management while allowing flexibility in implementation across business units.
Tracking Meaningful KPIs
Among 12 adoption and scaling practices examined, tracking well-defined KPIs for generative AI solutions significantly impacted the bottom line. Yet fewer than one in five respondents say their organizations are doing this.
Establishing a clear roadmap to drive adoption for larger organizations showed a substantial impact. This involves planning phased rollouts across teams and business units and creating a structured approach to implementation.
How Organizations Monitor AI Outputs
The survey reveals wide variation in how companies monitor what their AI systems produce. Twenty-seven percent of respondents say employees review all content created by generative AI before use. A similar percentage says that 20% or less of AI-produced content is checked.
Respondents in professional services are much more likely than those in other industries to review all outputs - reflecting the higher stakes and regulatory scrutiny in these sectors.
Managing AI Risks
Risk management efforts are increasing across organizations. Compared to early 2024, more respondents say their organizations are actively managing risks related to:
Inaccuracy of AI outputs
Cybersecurity vulnerabilities
Intellectual property infringement
These happen to be three of the AI-related risks that most commonly cause negative consequences for organizations according to the survey.
Larger organizations are significantly more proactive in risk management, particularly regarding cybersecurity and privacy risks, though they're not more likely to address accuracy or explainability issues.
AI's Impact on the Workforce
The report provides valuable insights on how AI is reshaping workplace skills and responsibilities.
Hiring for New Roles
Organizations continue hiring for AI-related positions at similar rates to early 2024. Thirteen percent of respondents say their organizations have hired AI compliance specialists, and 6% report hiring AI ethics specialists. Larger companies are more likely to hire across a range of AI-related roles, particularly data scientists, machine learning engineers, and data engineers.
Reskilling Employees
Many organizations are reskilling portions of their workforce as part of AI deployment. The survey indicates this trend will accelerate, with respondents expecting increased reskilling over the next three years.
Reallocating Time Saved by AI
When AI automates tasks, how do companies use the time saved? Respondents often reported that employees spend the freed-up time on entirely new activities or focus more on existing responsibilities that haven't been automated.
Interestingly, respondents from larger organizations are more likely to report reducing headcount due to time saved, which correlates with higher bottom-line value from generative AI.
Future Workforce Impact
Looking forward, 38% of respondents predict that generative AI will have little effect on their workforce size in the next three years. The expectations vary by industry - financial services respondents are the only group much more likely to expect workforce reductions than no change.
Respondents often predict decreasing headcount in service operations and supply chain management by function. In contrast, IT and product development are more likely to see increasing staff numbers.
Value Creation From AI
AI implementation is beginning to translate into tangible business value, though mostly at the business unit level rather than enterprise-wide:
Revenue increases: More respondents than in early 2024 report that generative AI use cases have increased revenue within business units deploying them.
Cost reductions: In early 2024, a minority of respondents saw cost reductions from generative AI use in specific business functions. In the latest survey, a majority report cost reductions for most business functions.
Enterprise-level impact: Despite these positive trends, over 80% of respondents say their organizations aren't yet seeing tangible impact on enterprise-level EBIT from generative AI.
Where and How AI Is Being Used
Organizations across industries are most likely to use generative AI in marketing and sales, though deployment in other functions varies by sector. For example:
Service operations for media and telecommunication companies
Software engineering for technology companies
Knowledge management for professional services organizations
Most respondents (63%) say their organizations use generative AI to create text outputs, but many are also experimenting with other formats:
36% are generating images
27% are creating computer code
13% are producing video
13% are creating voice and music
Key Takeaways for Business Leaders
Based on the McKinsey report, here are some critical actions for senior leaders:
Take personal ownership of AI governance: The CEO's direct involvement correlates strongly with AI success, particularly in larger organizations.
Redesign workflows, don’t just add AI: Organizations that see the most value are not simply applying AI to existing processes but rethinking how work gets done.
Develop a clear AI adoption roadmap: Establish a structured plan for AI implementation across your organization, with phased rollouts and clear expectations.
Implement meaningful KPIs: Track well-defined metrics for your AI initiatives to measure adoption and ROI.
Adopt a balanced approach to centralization: Centralize governance, risk management, and data strategies while maintaining flexibility in implementation.
Proactively manage emerging risks: As your AI use expands, systematically address risks related to accuracy, cybersecurity, and intellectual property.
Invest in hiring and reskilling: Build your AI capabilities through strategic hiring for specialized roles while reskilling existing employees to work effectively with AI.
Use time saved strategically: Make deliberate choices about using AI's efficiency gains, whether for new initiatives, a deeper focus on core activities, or workforce adjustments.
The Path Forward
The survey findings show that we're still in the early stages of realizing AI's full potential. Organizations have been experimenting with generative AI tools, and while usage continues to surge, these remain early days from a value capture standpoint.
Larger companies are taking more systematic approaches to realizing AI's value. They are investing more heavily in AI talent, mitigating risks, and adopting best practices. As AI technology continues to evolve toward agentic AI as the next frontier, organizations that follow the roadmap for successful implementation will be best positioned to capture value in 2025 and beyond.
The truth is more nuanced for all the talk about AI transformation: real value comes from thoughtful implementation, structural changes, and leadership commitment. The companies seeing the most impact aren't just adding AI to their technology stack; they are rewiring their organizations to leverage their capabilities fully.
If you found this post helpful, consider sharing it with another executive grappling with AI, technology, and data. If you want to explore AI and other Technology strategies, grab some time on my calendar, and let's chat.
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