Integrating Generative AI into Digital Transformation: A Unified Pilot-to-Production Framework for Key Economic Sectors

Abstract

Generative AI (GenAI) is set to become the next big investment area for enterprises globally, but in most cases, when the proof of concept is promising, organizations are finding it difficult to get the proof of concept into production and into a system that delivers actual business value. In this paper presents a unified framework for scaling GenAI across healthcare, finance, retail, and manufacturing and validate the framework by exploring nontrivial scaling applications with industry clients. Eight 12 month longitudinal case studies plus a survey of 212 executives are employed for triangulation via technical telemetry, organizational-readiness metrics, and 10 year cost-benefit models. Our results show that (i) data platform maturity is the strongest accelerator of time until production (β=0.42,p<0.001), (ii) the adoption of ISO 42001's governance reduces 100 years ¼ severity incident risk and preserves as much as nine percent of net present value, and (iii) internal rate of return of median deployment is 41%, and payback takes a year or less for all deployments. There is a five level maturity ladder that ranges from ideation sandboxes to autonomous GenAI agents that links incremental capability building to gating KPIs and risk controls. Drawing from dynamic-capabilities theory, this study extends the theory by providing a specification of how they must co-evolve in order to convert transient pilots into durable advantage in the form of LLMOps, trust layers and "fusion teams." Guided by evidence based playbook, executives are equipped with concrete steps to make strategy, data fabric, culture and governance work hand in hand to take pilots out of the lab to production and get the cross-industry potential of GenAI unlocked.

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