Process Intelligence Maturity as a Leading Indicator of Enterprise AI Return on Investment

Abstract

Enterprise artificial intelligence investment decisions are frequently made under uncertainty about which initia-tives will realize a measurable return. Existing AI readiness frameworks address data, infrastructure, talent, and governance, yet they tend to treat process intelligence as one factor among many rather than as a distinct leading indicator. This paper proposes that process intelligence maturity, measured across four specific dimensions, may serve as a leading indicator of enterprise AI return-on-investment realization. The four dimensions are catalog completeness and hygiene, process model conformance, repository organization and discoverability, and process telemetry quality. Drawing on practitioner observation from large-scale process-architecture migrations, the pa-per develops five testable propositions and proposes a retrospective empirical methodology spanning 30 to 50 enterprises across at least four sectors. Measurement approaches for each dimension are specified, along with a regression-based analysis plan that controls for sector, organizational size, and use-case category. The paper discusses anticipated findings, validity threats inherent to retrospective designs, and implications for AI invest-ment decisions, readiness-framework development, and vendor platform strategy. The contribution is a testable reframing that positions process intelligence as measurable groundwork preceding AI value.