Generative Artificial Intelligence in Enterprise Information Systems: Transforming Business Intelligence and Strategic Decision Support Processes

Abstract

Background: General artificial intelligence (GenAI) is a paradigm shift of deterministic computing to introduction of reasoning based decision support in Enterprise Information Systems (EIS). This change in the United States can be characterized by rapidly moving experimental pilots to the mainstream business processes of having autonomous and agentic functions implemented in the business environment in the financial planning, supply chain management, and procurement. With a high of 33.9 billion in international investments in GenAI in 2024, it has now become more than a productivity tool with the executive leadership.

Research Objective: This systematic review serves the goal of thoroughly examining how and the effects of generative artificial intelligence (GenAI) on enterprise information systems (EIS) in the United States.

Research Methods: The major academic and industry databases were searched and focused on the research published within the period of 2020-2025. The quality of methods and the risk of bias were identified using the approved critic appraisal instruments, that is, AMSTAR-2 and JBI checklists. The performance quantitative measurements that were incorporated in the review are technical frameworks, such as the Gen-Optimizer, and qualitative data.

Conclusion: espite the fact that the productivity enhancement of high-performing organizations is up by as much as 40 percent and cost reduction up by 95 percent when agentic orchestration happens, the failure rate of initial enterprise AI initiatives, standing at 95 percent, reveals that few success stories exist, and there is no ability to correct the underlying problem, namely, the data quality and infrastructure bottlenecks.