Can LLM Agents Be CFOs? A Benchmark for Resource Allocation in Dynamic Enterprise Environments
Summary: arXiv:2603.23638v1 Announce Type: new
Abstract
Large language models (LLMs) have enabled agentic systems that can reason, plan, and act across complex tasks, but it remains unclear whether they can allocate resources effectively under uncertainty. Unlike short-horizon reactive decisions, allocation requires committing scarce resources over time while balancing competing objectives and preserving flexibility for future needs.
Introduction
In the rapidly evolving landscape of enterprise management, the role of Chief Financial Officers (CFOs) is becoming increasingly complex. With the advent of artificial intelligence, specifically large language models (LLMs), there is a growing interest in whether these systems can effectively take on such critical roles. A recent study introduces EnterpriseArena, a benchmark designed to evaluate the capabilities of LLMs in long-horizon resource allocation within dynamic enterprise environments.
About EnterpriseArena
EnterpriseArena is the first benchmark specifically crafted to assess agents on long-horizon enterprise resource allocation. This innovative platform simulates CFO-style decision-making over a span of 132 months. It integrates a rich array of elements, including:
- Firm-level financial data
- Anonymized business documents
- Macroeconomic and industry signals
- Expert-validated operating rules
Challenges in Resource Allocation
The environment in which these agents operate is partially observable, meaning that they can only deduce the state of the enterprise through available budgeted organizational tools. This design forces LLM agents to make critical trade-offs between:
- Information acquisition
- Conserving scarce resources
Such decisions are not straightforward, as they must navigate uncertainty while also committing to resource allocations that will impact future operational flexibility.
Experimental Findings
In a series of experiments conducted with eleven advanced LLMs, it was revealed that the task of long-horizon resource allocation remains highly challenging. Key findings from the research include:
- Only 16% of the simulation runs were able to survive the full 132-month horizon.
- Larger models did not consistently outperform their smaller counterparts, indicating a potential capability gap.
Conclusion
The results of this study highlight a significant challenge for current LLM agents: the ability to effectively manage long-horizon resource allocation under uncertainty. As organizations increasingly rely on AI for decision-making, understanding these limitations will be crucial for integrating LLMs into high-stakes roles such as that of a CFO. The EnterpriseArena benchmark serves as a critical tool for further research and development in this area, paving the way for future advancements in AI-driven enterprise management.