LLM agents tested for decision-making in complex business scenarios
EnterpriseBench: Benchmarking LLM Agents on Enterprise-Level Strategic Reasoning and Decision-Making
Artificial Intelligence
Summary
Large language models (LLMs) are being tested to see how well they help with complex business tasks that involve uncertain information and long-term planning. The authors created EnterpriseBench, a set of tests that cover simple questions and interactive business problems like consulting, supply chain management, and project planning. They found that current models still struggle to reliably handle these complex, multi-step tasks. This means more work is needed before LLMs can fully support enterprise decision-making.
What this means in practice
- •For enterprise software developers: Evaluate and improve AI agents for business decision-making and strategy under real-world conditions using EnterpriseBench.
- •For business process analysts: Use the benchmark to assess AI tools for complex consulting and supply chain simulations reflecting actual business challenges.
Authors
Min Yang, Yichen Pan, Jinghua Piao, Dandan Song, Yongshun Gong, Yong Li
Abstract
LLM agents are increasingly expected to support enterprise workflows, where tasks often involve missing information, uncertainty, feedback, and long-term trade-offs. However, existing enterprise and financial benchmarks mainly test static capabilities such as information extraction, numerical calculation, domain knowledge, and financial QA, leaving interactive and long-horizon decision-making underexplored. To bridge this gap, we introduce EnterpriseBench, a benchmark that evaluates LLM agents across this spectrum, from static question answering to dynamic decision-making. Specifically, EnterpriseBench reorganizes existing enterprise and financial QA datasets into a unified foundational suite annotated by capability and difficulty, and introduces three professional interactive settings: Consulting, based on management-consulting-style business cases for client problem diagnosis through multi-turn information seeking; the Beer Game, adapted from a classic supply-chain management simulation for inventory control under delayed feedback; and Enterprise Digital Twin, a project-based business simulator for workforce, risk, and project planning. Experiments with nine agent methods under four backbone models show that current agents have not yet achieved stable, comprehensive, and cross-task reliability in enterprise scenarios. These results show that EnterpriseBench provides a practical benchmark for evaluating LLM agents in realistic enterprise strategic reasoning and decision-making.