Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new multi-agent benchmark, \textsc{CEO-Bench}, evaluates large language models' (LLMs) strategic resource reallocation capabilities, a critical aspect of executive decision-making. Unlike existing benchmarks focused on isolated cognitive tasks, \textsc{CEO-Bench} simulates a multi-round, constraint-rich organizational environment where LLM agents must integrate conflicting recommendations from four role-conditioned C-suite advisors (CFO, CTO, COO, CMO), each with private signals and distinct priorities. The benchmark assesses LLM performance across 13 scenarios based on role integration, conditional boldness, history-sensitive judgment, and plan validity. Experiments with five frontier models revealed high structural validity but significant divergence in strategic calibration. Identified systematic failure modes include single-advisor capture, conservative default under ambiguity, and historical amnesia, highlighting an integration-boldness tradeoff where deeper engagement with conflicting perspectives often leads to less decisive action.

Key takeaway

For AI Architects evaluating LLMs for executive support systems, recognize that current models achieve high structural validity but struggle with strategic calibration. You should prioritize designing AI-assisted executive systems that explicitly address systematic failure modes like single-advisor capture and historical amnesia. Be aware that deeper integration of conflicting perspectives in LLMs might lead to less decisive action, requiring careful human oversight or specific architectural interventions to ensure bold, timely decisions.

Key insights

LLMs struggle with integrating conflicting advice for strategic resource reallocation, showing an integration-boldness tradeoff.

Principles

• Executive decisions demand integrating conflicting advice.
• LLMs show an integration-boldness tradeoff.
• Systematic failure modes limit LLM executive roles.

Method

\textsc{CEO-Bench} evaluates LLMs by having agents synthesize conflicting advice from four C-suite advisors (CFO, CTO, COO, CMO) into a resource allocation plan across 13 scenarios, assessed on four dimensions.

In practice

• Benchmark LLMs on multi-agent strategic tasks.
• Design AI systems to mitigate single-advisor capture.

Topics

• Large Language Models
• Strategic Resource Reallocation
• Multi-Agent Simulation
• CEO-Bench Benchmark
• Executive Decision-Making
• AI-assisted Systems

Best for: Research Scientist, AI Scientist, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.