AI is no longer constrained by interest. The harder question is why so few organizations convert AI activity into durable business value.
McKinsey’s 2025 Global Survey on AI found that 88% of organizations now use AI in at least one business function. Yet more than 80% say they are still not seeing a tangible enterprise-level EBIT impact from generative AI. IBM’s 2025 CEO study points to the same gap: only 25% of AI initiatives have delivered expected ROI, and only 16% have scaled enterprise-wide.
The issue is not whether organizations are experimenting with AI. The issue is whether they are targeting the right value pools, building the required capabilities, and redesigning work in ways that turn isolated progress into measurable enterprise outcomes.
3 Challenges to Overcome
Executive teams need clarity on three questions:
1) Where is AI value actually available in our business?
2) What is preventing us from capturing it today?
3) What has to change to move from experimentation to scaled impact?
That is the purpose of our AI Value Gap Assessment: to make the barriers visible, identify where value creation is getting stuck, and define the next moves required to realize it.
AI Use Is Not AI Value
AI value starts with choosing the right problems, not deploying the latest tools.
Many organizations already use AI. That does not mean they are addressing the problems that matter most. AI activity can create the illusion of progress: more pilots, more tools, more prototypes, more internal momentum. But activity is not value. In many organizations, AI is still being tested broadly without enough clarity on where it can produce measurable business outcomes. That is why the first challenge is not adoption. It is judgment.
Organizations need to distinguish between:
- interesting use cases
- feasible use cases
- scalable use cases
- use cases that can create measurable business value
Without that discipline, AI becomes busy experimentation rather than a reliable path to performance.
To Identify Value Gaps
Assess your Organization on how to drive Value across the board!
The Journey Cannot Be Skipped
The biggest gains usually come after experimentation, when the organization redesigns work rather than simply layering AI onto existing processes.
Early stages create learning. Later stages create value. Organizations begin by testing use cases and validating assumptions. In the process, they usually discover that data, governance, ownership, incentives, and workflows are weaker than expected. Those stages matter. They cannot be skipped.
McKinsey’s 2025 research found that workflow redesign has the strongest effect on whether organizations achieve EBIT impact from generative AI. Yet only 21% of respondents reporting gen AI use said their organizations had fundamentally redesigned at least some workflows.
Real value rarely comes from dropping AI into existing work as-is. It comes from redesigning how work gets done:
- how decisions are made
- how handoffs happen
- how people interact with AI
- how performance is monitored and
- improved over time
That is why later-stage disciplines matter so much: workflow redesign, operating discipline, governance, adoption, and value tracking. discipline, governance, adoption, and value tracking—are where significant value begins to emerge.
To identify your Journey Stage
Assess what are your next steps on the path to AI Value Creation!
Value Breaks in the Gaps
AI value does not depend on optimizing one area in isolation. It depends on aligning the full system required to create results.
AI does not fail only because of weak technology. It fails when the system required to create value is not aligned. That system includes five areas:
- Psychology — trust, behavior, incentives, adoption, and workflow change
- Business — what matters, where value is expected, and who owns outcomes
- Technology — integration, security, reliability, and support
- Algorithms — whether your AI approach is a good fit for the problem
- Data — availability, quality, meaning, and governance
Most organizations do not advance these areas at the same speed. Business priorities may be unclear. Technology may move faster than data readiness. Analytics may generate useful outputs that people do not trust or use. Governance may arrive too late and create friction instead of clarity.That is where AI value gets stuck: in the gaps between teams, decisions, capabilities, and stages of execution.
Frameworks matter because they make those gaps visible. They help leaders see what is missing, what is out ahead of the system, and what must improve before AI can scale into durable value.
To Identify misalignment within your Value Areas
Assess where your weaknesses are to create value with AI!