Capability-Aware Digital Transformation: Adaptive Deployment Strategies for Enterprise AI

A Falsifiable Multidimensional Policy for Sequencing, Capacity-Building, and Reversible Rollout / Enterprise AI implementation increasingly depends on organizational capabilities, governance, and behavior, but these requirements are already recognized in organizational readiness and phased-rollout research. This paper proposes the Capability-Aware Deployment Policy (CADP) as a bounded, testable method rather than a demonstrated technology. CADP represents each enterprise unit through task-specific capability, process, governance, support, benefit, risk, and observed-outcome variables, and uses a transparent policy to choose among capacity-building, pilot, expansion, maintenance, suspension, and rollback. It distinguishes mandatory noncompensatory constraints from soft objectives, explicitly protects safe low-readiness units from automatic exclusion, and accounts for measurement error, intervention overhead, irreversible consequences, and resource contention. A four-policy comparison contrasts simultaneous deployment, fixed staged rollout, technology-readiness ordering, and CADP, with an expert-governed fixed strategy as the strongest practical challenger. The proposed research program specifies adversarial synthetic environments, ablation tests, outcomes for sustained effective adoption, and a conditional prospective cluster evaluation. No simulation, organizational intervention, empirical uplift, algorithmic superiority, or global novelty is reported. The candidate contribution is an independently reproducible policy and falsification contract that will be valuable only if it adds measurable decision quality beyond existing readiness assessments, dynamic treatment methods, and well-designed staged deployment.