A Novel Enterprise AI Classification Framework for Business Transformation: A Structured Literature Review and Integration of AI Types and Autonomy Levels

Enterprise investment in artificial intelligence has reached an unprecedented scale across national, regional, and organisational levels of the economy, yet transformation outcomes remain highly variable. Recent global research indicates that the majority of enterprise AI investments produce no measurable profit-and-loss impact, with only a minority of organisations extracting material enterprise-level value. Without an integrated classification framework that allows enterprises to deploy, govern, and create value from the full range of AI technologies and autonomy levels across enterprise functions, the risks of misallocated investment, fragmented deployment, and unrealised return remain difficult to mitigate. A structured literature review on AI classification reveals that existing frameworks do not integrate AI technology types, operational autonomy levels, and concrete enterprise applications in a single classification structure that can support enterprise AI-driven transformation decisions. To address this gap, this paper proposes the Enterprise AI Classification Framework, a novel classification model that integrates six AI technology types with six autonomy levels in a 6 × 6 matrix of 36 combinations, each corresponding to concrete business applications across enterprise functions. A computational pilot study (n = 20 cases for case-based coding validation and 12 LLM-simulated personas × 30 vignettes for role-specific hypothesis pre-specification) provides preliminary evidence of substantially higher classification coverage than four baseline frameworks and pre-specifies role-divergence hypotheses for a planned empirical validation, with all design choices locked in an OSF pre-registration; reliability point estimates exceed the pre-specified α≥0.70 threshold at the pilot scale, with bootstrapped confidence intervals expected to tighten under the full-corpus run in the follow-up study. An interview-based empirical validation with approximately 250 decision-makers and practitioners across 20 industries is in preparation.