Generative AI in Enterprise Decision Intelligence: Opportunities and Challenges
TL;DR
It is argued that organizations capturing durable value are not necessarily those with the most advanced models, but those that have redesigned decision workflows, built human-in-the-loop verification into high-stakes processes, and treated GenAI as a collaborator rather than an oracle.
Abstract
Generative artificial intelligence (GenAI) is moving from a novelty confined to chatbots and content drafting into something enterprises are beginning to fold into how they actually decide things: pricing, hiring, supply chain routing, capital allocation. This paper examines that shift through the lens of decision intelligence, the discipline concerned with engineering better organizational decisions by combining data, models, and human judgment. Using a PRISMA-informed narrative review of academic and industry literature published mainly between 2019 and 2026, the paper traces how large language models and related generative systems are being embedded into enterprise decision workflows, what measurable value they are producing, and where they fall short. The review finds genuine opportunities: compressed analysis cycles, wider access to sophisticated reasoning for non-specialist decision-makers, and new forms of scenario generation once reserved for expert analysts. At the same time, the literature converges on a stubborn set of challenges, including hallucinated or unreliable outputs, algorithmic bias, unclear governance accountability, and a persistent gap between pilot-stage enthusiasm and enterprise-level financial return. The paper argues that organizations capturing durable value are not necessarily those with the most advanced models, but those that have redesigned decision workflows, built human-in-the-loop verification into high-stakes processes, and treated GenAI as a collaborator rather than an oracle. It closes with practical implications and a short research agenda.