Can LLM Agents Be CFOs? A Benchmark for Resource Allocation in Dynamic Enterprise Environments

Researchers introduced EnterpriseArena, the first benchmark testing whether AI agents can function as CFOs by allocating resources in complex enterprise environments over 132 months. Testing on eleven advanced LLMs revealed poor performance, with only 16% of runs surviving the full simulation period, highlighting significant capability gaps in long-term resource allocation under uncertainty.

• →Only 16% of LLM agent runs successfully completed the full 132-month enterprise simulation, indicating poor long-term decision-making capabilities.
• →Larger language models did not reliably outperform smaller ones in resource allocation tasks, challenging assumptions about model scaling benefits.
• →The benchmark combines real financial data, business documents, and macroeconomic signals to test CFO-level decision-making abilities.
• →Current LLM agents struggle with long-horizon planning that requires balancing competing objectives while preserving flexibility for future needs.
• →The research identifies resource allocation under uncertainty as a distinct capability gap that remains unsolved by current AI systems.