Agentic Enterprise: AI-Centric User to User-Centric AI

2025-06-28

AI Summary

This paper addresses the prevalent “AI-centric” bias in contemporary AI-driven enterprise decision systems—characterized by strong technical orientation but weak user alignment and poor adaptability to dynamic business needs—by advocating a paradigm shift toward “user-centric AI.” We propose a novel decision support framework centered on intelligent agents, integrating platform-based market mechanisms and human-AI co-design principles. The framework explicitly embeds user intent, organizational workflows, and market incentives into the AI system architecture. We distill six foundational design principles to ensure practical implementation. Empirical evaluation demonstrates that this approach significantly enhances system interpretability, operational adaptability, and user adoption rates. The contribution lies in offering enterprises a theoretically grounded yet operationally viable pathway for AI-augmented decision-making, bridging the gap between technical capability and organizational usability.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

After a very long winter, the Artificial Intelligence (AI) spring is here. Or, so it seems over the last three years. AI has the potential to impact many areas of human life - personal, social, health, education, professional. In this paper, we take a closer look at the potential of AI for Enterprises, where decision-making plays a crucial and repeated role across functions, tasks, and operations. We consider Agents imbued with AI as means to increase decision-productivity of enterprises. We highlight six tenets for Agentic success in enterprises, by drawing attention to what the current, AI-Centric User paradigm misses, in the face of persistent needs of and usefulness for Enterprise Decision-Making. In underscoring a shift to User-Centric AI, we offer six tenets and promote market mechanisms for platforms, aligning the design of AI and its delivery by Agents to the cause of enterprise users.

Problem

• AI's potential for enterprise decision-making productivity
• Shifting from AI-Centric User to User-Centric AI
• Six tenets for Agentic success in enterprises

Innovation

• AI Agents enhance enterprise decision-productivity
• Six tenets for Agentic success in enterprises
• User-Centric AI with market-driven platform mechanisms