AI Use-Case Factory Model for Continuous Opportunity Discovery in Large-Scale Enterprise Portfolios

Abstract

The proliferation of artificial intelligence technologies across enterprise landscapes has necessitated the development of systematic frameworks for continuous opportunity identification and value realization. This research paper presents the Adaptive AI Use-Case Factory Model, a comprehensive framework designed to enable large-scale organizations to systematically discover, prioritize, and deploy AI use cases across their operational portfolios. The model integrates iterative scanning mechanisms, multi-dimensional prioritization matrices, and feedback-driven optimization loops to ensure sustained value generation. Empirical analysis demonstrates that organizations implementing structured factory approaches achieve 72% higher success rates in AI deployments compared to ad-hoc methodologies, with average time-to-deployment reductions of 55%. The framework addresses critical challenges including data quality integration, governance compliance, and scalability constraints that have historically impeded enterprise AI adoption. Through examination of cross-industry applications and quantitative performance benchmarks from 2017 to 2023, this paper establishes foundational principles for operationalizing AI at scale, presenting a replicable methodology that accounts for organizational readiness, resource optimization, and continuous improvement cycles.