The EARBr Framework: A Decision-Theoretic Model for AI-Driven Process Optimization and Scalable System Design in Enterprise Product Management

Abstract

Enterprise product managers face a sequencing problem that most AI governance frameworks ignore. It is not that organizations lack AI capability  -  it is that they apply it in the wrong order, to the wrong processes, at the wrong level of investment. Teams are rewarded for shipping new AI systems; they are not rewarded for eliminating the processes those systems would have automated, or for reusing the AI infrastructure already sitting in another team's codebase. The result is an enterprise AI landscape that is sophisticated in individual components and redundant, fragmented, and expensive to maintain in aggregate. The EARBr framework  -  Eliminate, Automate, Reuse, Build-to-Reuse  -  provides a four-layer decision model for correcting this. Its central governance rule is precedence: advance to the next layer only after genuinely exhausting the prior one. The framework's contribution is not the four categories themselves, which individually are familiar to most practitioners, but the enforced precedence between them: a team may not automate a process it has not first tested for elimination, and may not build new infrastructure for a capability it has not first tried to reuse. This ordering constraint is what existing AI governance guidance omits, and its absence is what allows redundant automation to accumulate at enterprise scale. Three practitioner cases from large-scale retail demonstrate the framework in action, with documented outcomes of approximately 90% efficiency gain (Layer 1), 80% onboarding time reduction across 250,000 annual users (Layer 2), and a modular platform serving four operational domains from a single Build-to-Reuse investment (Layers 3-4). The paper also outlines the organizational adoption conditions - incentive redesign, cross-team visibility, and governance checkpoints - required for the precedence rule to hold under delivery pressure. The intended audience is enterprise product managers, AI governance leads, and platform engineering leaders allocating finite capacity across competing automation demands.