The Use Case Illusion: Why the Public Sector’s Approach to AI Is Undermining Transformation

Abstract

The public sector’s prevailing approach to artificial intelligence (AI) emphasizes use cases and pilot projects as indicators of progress. While well-intentioned, this mindset is deeply flawed. Measuring AI maturity through the number of projects undertaken leads to fragmented, siloed automation efforts that lack systemic coherence and fail to deliver strategic transformation. This article argues that the “use case mindset” stems from legacy business process reengineering paradigms and remains fundamentally ill-suited to generative AI and other advanced systems. The goal of AI is not merely task-level automation but the reconfiguration of work itself – both cognitive and physical – across the organizational graph. Public institutions should move beyond linear workflows and embrace models that treat agencies, economies, and even governments as complex adaptive systems. Only through this systems-based lens can GenAI fulfill its potential to increase institutional productivity, responsiveness, and strategic capability. The article concludes with a call to redefine AI strategy away from pilot counting and toward full-system optimization, offering a framework for agencies to escape the use case trap.