A Decision-Making Framework for Prioritizing AI Adoption Across Enterprises

Abstract

This paper addresses the increasing challenge of how to select and prioritize generative AI initiatives
when technologies develop faster than governance, compliance, and funding processes. We propose the
GAIQ framework: a design-science-based, gate-driven model for qualifying GenAI use cases along
three dimensions, namely, PVI, TFR, and ERC. The model structures decision-making through a SEA
sequence of scanning, evaluation, and activation and applies weighted thresholds so that use cases that
are strategically attractive but weak in ethics or technology cannot advance. Two complementary
instruments, NEXA and NOVA, extend the framework to investment decisions. Validation on simulated
enterprise scenarios shows that GAIQ produces more consistent, auditable, and business-aligned
recommendations than generic AI maturity models, thereby closing the gap between high-level AI
strategy and operational implementation.