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Commentarycorporate leadership

Weary managers of the world, get ready to learn a new skill: Leading all the AI agents and bots whose work you’ll be accountable for

By
Colin M. Fisher
Colin M. Fisher
and
Sarah Touzani
Sarah Touzani
Down Arrow Button Icon
By
Colin M. Fisher
Colin M. Fisher
and
Sarah Touzani
Sarah Touzani
Down Arrow Button Icon
September 5, 2025, 11:36 AM ET
Colin M. Fisher is a professor at UCL School of Management and author of The Collective Edge: Unlocking the Secret Power of Groups. Sarah Touzani is Co-founder and CEO of Waggle AI.
Nvidia CEO Jensen Huang predicts organizations will soon have millions of AI agents.
Nvidia CEO Jensen Huang predicts organizations will soon have millions of AI agents. Johannes Neudecker/picture alliance via Getty Images

Managers, get ready: You’re about to lead a new team. But your new team isn’t human – it’s a team of AIs. And you’ll be accountable for their work.

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The era when “team leadership” meant only humans is over. The savviest workers are now using multiple AI models. Managers now need to decide which model is assigned to which task, delegate tasks effectively, and review the output from each. That sounds a lot like leading a team.

AI use is exploding. Microsoft’s 2025 Work Trend Index reports that 36% of leaders expect managing AI systems to be part of their scope within five years. The World Economic Forum hears CEOs predicting future leaders will be measured partly by how many “digital workers” they can effectively manage.

Luckily, not all the skills for leading a team of AI agents are unfamiliar. While there’s some learning to do, you can use some basic principles of teamwork and leadership to help guide your new team of AIs – with a few tweaks.

What stays the same – and what changes?

Using AI well is no longer about picking the right app. It’s about orchestrating a set of agents, each with unique strengths and weaknesses. Like any team, they can underperform when misaligned—or create unity when structured well.

Research from Harvard’s David Deming and colleagues shows a strong link between skill at coordinating AI agents and skill at leading human teams. The same leadership moves matter: asking clarifying questions, setting clear expectations, and learning through trial and error. Microsoft calls this the rise of the “agent boss,” urging leaders to onboard AIs like new hires—compose your “teams” carefully, set clear expectations, monitor early outputs, and establish trust-but-verify routines.

That’s why the basics of leading your AI team are the same as leading a human team: establish clear goals, define roles, and provide performance feedback—themes I talk about in my book The Collective Edge: Unlocking the Secret Power of Groups.

However, leading a team of AIs is not the same as leading people. AI agents don’t negotiate roles, learn from each other, or self-organize unless explicitly programmed to do so. There’s no shared leadership, no emergent norms, no psychological safety—but also no fear or status dynamics holding them back.

Years ago, Harvard professor J. Richard Hackman argued that team leaders should be less like orchestral conductors and more like leaders of small jazz ensembles, with lots of room for creativity, shifting leadership, and improvisation. But, with teams of AIs, leaders are more like orchestral conductors. All coordination is centralized in the human.

AI also demands more technical fluency than human leadership. As Gabi Beyo notes, you can’t rely on charisma to get an AI back on track; you need to understand prompt construction, error patterns, and model capabilities.

From our research, here are the five leadership skills for you’ll need to manage teams of AIs:

  • Team Composition and Role Clarity: Stay up to date on the latest models and their strengths and weaknesses. Match model strengths to task requirements.
  • Delegation and Task Design: Delegation is a core leadership competency, whether you are working with humans or LLMs. You define the task (prompting), choose the right “team member” (model/tool), and provide context with the clarity you’d give a smart junior hire.
  • Performance Monitoring and Feedback: Build iterative refinement into the workflow. A common practice is to have one AI give feedback on the work of another. AI, like a new team member, often needs multiple tries.
  • Trust Calibration: You can’t trust today’s AI models to work unsupervised on unfamiliar tasks. Trust, but verify – especially for new tasks.
  • Ethical Oversight:As Ayumi Moore Aoki emphasizes, always verify that outputs align with values.

The leadership memo you didn’t know you needed

This isn’t theoretical. NVIDIA’s CEO Jensen Huang predicts organizations will soon have tens of thousands of humans and millions of AI agents. Leadership development programs rarely cover this. Yet the stakes are high: AI won’t push back if you set a bad goal. It won’t raise a hand when it hallucinates facts. When those mistakes surface, they’ll have your name on them.

The AI revolution hasn’t reduced the importance of leadership. The leaders who thrive will be those who treat AI agents not as magical black boxes, but as capable yet fallible teammates: giving them clear objectives, the right roles, structured onboarding, iterative coaching, and informed trust. Whether you’re a Coins2Day 500 CEO or solo-preneur, the next leadership skill you need is leading teams of AIs to collaborate better. It’s time to add a new line to your job description: conductor of the non-human workforce.

Coins2Day Brainstorm AI returns to San Francisco Dec. 8–9 to convene the smartest people we know—technologists, entrepreneurs, Coins2Day Global 500 executives, investors, policymakers, and the brilliant minds in between—to explore and interrogate the most pressing questions about AI at another pivotal moment. Register here.
About the Authors
By Colin M. Fisher
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By Sarah Touzani
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