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. Work design research in I-O psychology, running back through the sociotechnical systems tradition, keeps arriving at the same conclusion: 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. The applications deployed at any given moment are just the current output of that capacity.
That distinction changes what you fund. Consider what happens when a new model release makes a previously marginal application suddenly viable, as releases regularly do. The organization with the catalog reconvenes its prioritization committee, rescores the spreadsheet, and slots the change into the next planning cycle. The organization with adaptive capacity has a team testing the application by Friday. Neither organization predicted the release. Only one was built to absorb it. Sensing, seizing, and reconfiguring sound abstract, but in practice they are the difference between a strategy that absorbs surprise and one that schedules it.
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 bottleneck is also a signal-loss problem. The analyst who notices that a tedious reconciliation task is mostly pattern matching holds exactly the tacit knowledge a good AI application needs, and usually has no sanctioned way to act on it. When the strategy team owns the catalog, that observation has to travel upward through intake forms and steering committees, losing fidelity at every hop, and most observations never make the trip. The organizations that handle this well invert the flow. The center sets guardrails, provides tools, and clears obstacles. The edges generate the applications. That is Nonaka and Takeuchi's dynamic in operational terms: tacit knowledge stays close to where it lives, and the organization builds channels for it to become explicit.
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 succeeded by building a system in which every worker was expected and equipped to identify and test improvements continuously, rather than relying on a central team to spot every opportunity. 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 keep watching a version of the same story play out. A large, regulated enterprise (financial services is the usual setting, though I have seen the insurance and pharma variants) spends the better part of a year building the library: a spreadsheet of a couple hundred candidate applications, each scored for feasibility and impact, color-coded, and prioritized into waves. By the time wave one is funded, the model capabilities the scoring assumed are two generations old, the executive sponsor for the top-ranked item has changed roles, and a team three levels down has quietly built something better with the tools it already had. The library was obsolete before anyone deployed from it. That is a field observation, not a measurement, but the pattern repeats often enough that I have stopped expecting exceptions. 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.