I keep hearing the same request: "We need more use cases." But the use-case mentality treats AI like a vending machine. Insert problem, receive solution. The organizations I see pulling ahead are not building use case libraries. They are building adaptive capacity. Job design research tells us that when work is dynamic, prescription fails. What if the goal is not to identify the right 50 applications, but to build a system that surfaces the next 500 on its own?
The hypothesis: organizations that invest in building adaptive capacity for AI experimentation are better positioned to capture value than those that invest in curating predetermined use cases, because use case catalogs assume stable conditions that most knowledge work environments do not provide.
Three Takeaways
First, the use case approach assumes stable task environments. But most knowledge work environments are characterized by what Weick (1995) called equivocality: the raw materials of work are ambiguous, interpretable in multiple ways, and constantly shifting. In equivocal environments, the "right" use case today may be irrelevant in six months. Organizations that lock into a fixed set of applications are optimizing for a snapshot of conditions that are already changing.
Second, adaptive capacity is an organizational capability, not a project plan. Teece, Pisano, and Shuen's (1997) dynamic capabilities framework argues that sustained competitive advantage comes from an organization's ability to sense opportunities, seize them, and reconfigure resources accordingly. Applied to AI, this means the strategic asset is the organization's capacity to identify, test, and integrate AI applications continuously, not the specific applications it has deployed at any given moment.
Third, use case catalogs create a subtle dependency on centralized expertise. When a strategy team curates and distributes use cases, they become a bottleneck for organizational learning. The people closest to the work, the ones who understand the nuances of specific tasks and workflows, are positioned as recipients rather than generators of innovation. This is the opposite of what Nonaka and Takeuchi (1995) described as the knowledge-creating company, where innovation emerges from the dynamic interaction between tacit and explicit knowledge at every level.
The Longer View
In ecology, the concept of adaptive management (Holling, 1978) provides a useful parallel. Adaptive management treats environmental policy as a series of experiments rather than fixed prescriptions, acknowledging that complex systems are inherently unpredictable. The approach emphasizes monitoring, learning, and adjusting. Applied to AI strategy, adaptive management suggests that the goal is to create the conditions for continuous experimentation rather than to specify all possible experiments in advance.
From the history of manufacturing, the Toyota Production System's concept of kaizen, continuous improvement driven by the people doing the work (Imai, 1986), offers a structural analog. Toyota did not succeed by having a central team identify all possible process improvements. It succeeded by building a system where every worker was empowered and expected to identify and test improvements continuously. The AI equivalent is an organization where every team can experiment with AI applications within a governed framework.
My Two Cents
The use case library is comforting because it creates the appearance of strategic clarity. "We have identified 200 use cases." That looks good in a board deck. But I have seen too many organizations spend twelve months curating use cases, only to discover that the technology moved, the business context shifted, or the people closest to the work had already found better applications on their own. The energy spent cataloging would have been better spent building the infrastructure for safe, fast experimentation.
Instead of asking "What are our AI use cases?" try asking "How fast can a team go from identifying a potential AI application to testing it?" Measure that cycle time. Then work on reducing it. Build lightweight governance that enables experimentation rather than requiring exhaustive justification. Create channels for teams to share what they are learning. The use cases will emerge, and they will be better than the ones you would have prescribed.
Read to Learn More
Academic: Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.
Industry: Reeves, M., & Deimler, M. (2011). Adaptability: The new competitive advantage. Harvard Business Review, 89(7/8), 134-141.
References
Holling, C. S. (1978). Adaptive environmental assessment and management. Wiley.
Imai, M. (1986). Kaizen: The key to Japan's competitive success. McGraw-Hill.
Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. Oxford University Press.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.
Weick, K. E. (1995). Sensemaking in organizations. Sage.