Can LLMs Be CEOs? What Multi-Role Agent Simulation Teaches Us About Strategic AI

Hi, I’m Keito Inoshita from Affectosphere Group.

If you’ve sat in enough executive meetings, you’ve seen this play out.

The CFO says: “We need to prioritize cost reduction.”

The CTO pushes back: “If we don’t invest in technology now, we’ll be stuck in three years.”

The COO cuts in: “A plan that ignores operational capacity is unexecutable.”

The CMO adds: “Nobody is accounting for brand risk here.”

Each of them is right. But when all four are right simultaneously, a decision that satisfies everyone tends to land nowhere.

This — synthesizing contradictory but legitimate perspectives into a final judgment — is the core work of executive leadership.

A 2026 arXiv preprint by Yuyang Dai, Xueqing Peng, Lingfei Qian, and Zhuohan Xie runs that exact task through an LLM. The paper asks: “Can LLMs Be CEOs?”

CFO, CTO, COO, and CMO agents generate conflicting advice. A coordinating layer synthesizes that advice and makes a strategic resource reallocation decision. What actually happens in that process?

Today I’ll break down the findings for strategy, business development, and corporate planning teams.

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The Experimental Setup

Let me lay out the framework.

CEO-bench — the benchmark the paper constructs — centers on strategic resource reallocation: when a company faces a new strategic situation, how should it redistribute personnel, budget, and technical resources?

The key structural choice is the multi-role agent simulation.

Rather than telling a single LLM “act as CEO and decide,” the researchers assign distinct roles (CFO, CTO, COO, CMO) to separate LLM agents, have each generate advice from its own perspective, and then let a separate “coordination layer” agent integrate those perspectives into a final decision.

This architecture attempts to model the actual dynamics of executive team deliberation. Real management teams bring their functional expertise, argue from their own optimization targets, and arrive at a synthesized decision through debate.

The experiment asks: can AI replicate that?

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What the Research Found: Divergence and Conservatism

One of the most significant findings is what the paper calls divergence at the strategic coordination layer.

Each role agent — CFO, CTO, and so on — produces advice that is internally consistent with its assigned role.

But when the coordination agent synthesizes those inputs into a final decision, the resulting judgment diverges from what the individual role agents actually recommended.

The paper also reports a conservatism tendency.

When the synthesis layer tries to reconcile conflicting inputs, the distinctive and assertive positions tend to get dampened. The output converges toward a moderate, cautious middle ground.

This will feel familiar to anyone who has worked inside large organizations.

When the goal is “everyone needs to accept this,” whatever was most strongly argued by any one voice tends to get trimmed. The resulting decision carries lower risk — but also lower boldness.

The AI management simulation appears to reproduce this exact organizational pathology. That observation has real implications.

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Implications for Corporate Planning: Use AI for First Drafts

Now let me turn to practical applications.

The most actionable takeaway from this research is this: LLM-based CFO/CTO/COO/CMO simulation cannot replace executive judgment on its own — but it can be genuinely useful for generating first-pass proposals and structuring debate.

Consider a concrete use case.

Imagine a corporate strategy team building a resource allocation proposal for a new business initiative. The usual process involves sequential meetings with Finance, Technology, Operations, and Marketing — collecting each team’s concerns and requirements, then synthesizing them into a proposal. That process is expensive in coordination time.

A multi-role AI agent simulation could generate a first-draft proposal in hours.

CFO-perspective financial concerns. CTO-perspective technical risks. COO-perspective execution feasibility. CMO-perspective market and brand risks. If AI surfaces these as structured debate points in advance, the human team arrives at the table knowing what questions to argue about.

The AI-generated first draft becomes the object to critique, not the blank page to fill.

Human reviewers then ask: “What did the CFO angle miss? What if the CTO’s assumptions are wrong?” That division of labor is realistic and efficient.

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KPI Framework: Measuring the Right Thing

For corporate planning teams implementing this kind of AI support, KPI design is a practical challenge.

Here’s one framing.

Measuring “quality of decisions” directly is hard. What you can measure is issue coverage.

Of the considerations that ultimately shaped the human decision, what fraction were surfaced in advance by the AI multi-role first draft?

If that coverage ratio is high, the AI is demonstrably reducing the information-preparation costs of decision-making.

A second metric: coordination hours saved.

How many person-hours of cross-functional alignment did the old process require to produce a resource allocation proposal? How did that change after introducing an AI first-draft step?

Both metrics are grounded in exactly the capability this research demonstrates: AI’s ability to generate multi-perspective, multi-role issue maps quickly.

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Governance Design: AI First Draft, Human Final Decision

The most important governance implication of this research is this: design AI as a pre-processing layer for strategic decisions, not a replacement for them.

The divergence and conservatism found in the coordination layer is a warning against full-delegation AI governance.

When the AI coordination layer integrates and synthesizes inputs, the resulting “final decision” may have drifted from the strongest, most substantive positions that each functional agent held.

In other words: “the decision the AI made” may not actually reflect the specialized expertise each role agent contributed.

This maps directly onto the risk of over-delegating to AI in real organizational decisions.

The governance structure I’d propose from this research:

AI generates issue points from each functional perspective (Finance, Technology, Operations, Marketing). Humans review, evaluate, and select among those issue points. Humans make the final allocation decision.

AI handles the “surfacing” step. Humans handle the “deciding” step. That division is the most honest design for current capabilities.

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What CEO-Bench Is Really Asking

Back to the title. Can LLMs be CEOs?

Based on this paper’s findings, the answer trends toward: not yet, but they can be useful assistants.

The core of what a CEO does is bet under uncertainty while integrating contradictory legitimate views.

Surfacing contradictory perspectives? AI can do that.

Maintaining logical coherence within each perspective? AI can help with that.

But the final call — which bet to make — needs to be made by a human who owns the consequences.

This is not a question of AI capability. It is a question about the intrinsic structure of decisions.

Strategic decisions require not just logic, but interpretation (“how do I read this market?”), intention (“what are we actually trying to build?”), and ethical framing (“who is accountable when this goes wrong?”).

AI should be used as a tool that improves the quality of that deliberation — not as the deliberating party itself.

CEO-bench is a paper that experimentally draws that boundary. For practitioners thinking about where AI fits in management, it provides valuable empirical grounding.

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Summary

Three points to close.

1. Multi-role agent design shows promise: CFO/CTO/COO/CMO role agents demonstrably generate multi-perspective issue maps that human teams would otherwise need expensive coordination to produce.

2. Divergence and conservatism are real limits: the integration layer’s tendency to drift from individual agents’ positions and trend conservative is a structural warning against full AI delegation in strategic decisions.

3. AI first draft, human final judgment: design AI to surface and structure the deliberation, not to conclude it. That is the honest governance architecture for current systems.

The question for corporate planning and business development is not “should we use AI or not?” It is: “at which stage, and how?”

CEO-bench offers an empirically grounded place to start answering that question.

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Reference

Yuyang Dai, Xueqing Peng, Lingfei Qian, Zhuohan Xie. “Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation.” arXiv preprint arXiv:2606.17459 (2026). https://arxiv.org/abs/2606.17459

* This article was written in part with AI assistance and may contain inaccuracies.