Managing AI Teams Like Human Teams

As AI agents become more capable and more embedded in government workflows, we need to rethink how we manage them. The best analogy? Treat AI teams like human teams.

In traditional teams, we assign tasks based on skill, monitor performance, and apply quality assurance. The same principles apply to AI. Where outputs are high-stakes – like policy advice, legal interpretation, or public communications – a human expert should always review the AI’s work. But for lower-risk, mechanical tasks – like research, drafting reports, or even coding – AI agents can check each other’s work (up to a point).

This layered approach is already emerging in practice. For example, one AI agent might conduct research, another draft the report, while another checks for factual consistency or formatting errors. Depending on the use case, perhaps another agent might flag anomalies for human review. It’s a scalable model that balances efficiency with accountability.

To make this work, agencies should define “roles” for AI agents:

• Task scope: What the agent is allowed to do
• Quality thresholds: When human review is required
• Escalation paths: What happens when something goes wrong
• Performance metrics: How success is measured

This isn’t science fiction – it’s operational design. And it’s especially relevant in the Government context, where transparency, auditability, and public trust are non-negotiable.

Managing AI like people doesn’t mean pretending they’re human. It means applying the same discipline, structure, and oversight that we use for any team. The result? Smarter systems, safer outcomes, and a hybrid workforce that works in harmony.

If you want to talk about how to better manage your AI teams (or your people teams!), reach out to our team.