Probabilistic Requirement Modeling For Enterprise AI Workflow Orchestration: An Outcome-Centric Framework For Non-Deterministic Systems

Abstract

Enterprise artificial intelligence (AI) platforms — spanning large language model (LLM)-powered copilots, agentic workflow systems, and intelligent decision engines — expose a structural incompatibility between their probabilistic operational behavior and the deterministic requirement frameworks that product and engineering organizations conventionally rely on. Constructs such as Epics, User Stories, and Tasks assume stable, binary-verifiable outputs, an assumption that breaks systematically in production AI environments where model behavior evolves continuously, validation is statistical in nature, and user intent shifts with context and session. This paper formalizes Probabilistic Requirement Modeling (PRM), an outcome-centric framework that redefines enterprise AI system requirements in terms of use cases, user intent, and measurable outcome criteria rather than fixed feature behavior. The PRM model introduces a formal four-layer decomposition — Use Case → User Intent → Outcome Metrics → Validation Criteria — that decouples requirement specifications from specific model implementations, enabling adaptive workflow orchestration and continuous, statistically-grounded validation. The framework is evaluated through a prototype enterprise codebase intelligence system deployed over a 12-week production period, demonstrating a 38% reduction in requirement drift rate, a 44% improvement in outcome achievement rate, and a 31% reduction in execution friction index relative to deterministic pipeline baselines. Contributions include a formal PRM specification language, a four-category outcome metric taxonomy, a feedback-driven orchestration architecture, and documented alignment with the National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework (AI RMF) and the European Union (EU) AI Act, establishing PRM as an operationalizable foundation for compliant enterprise AI governance.