New large-scale research indicates that treating AI agents as if they were employees creates several unintended side effects inside organizations:

• Reduced individual accountability: When people see AI as a “colleague,” they are more likely to assume the system is sharing responsibility for outcomes. This can blur who is ultimately on the hook for decisions.
• More unnecessary escalation: Anthropomorphizing AI led participants to escalate issues more often than needed, instead of resolving them at their level. That slows work down and adds management overhead.
• Lower review quality: People reviewed AI-generated outputs less rigorously when they thought of the AI as an employee, which weakened quality control.
• Higher role uncertainty: Employees became less clear about their own roles and boundaries when AI was framed as a peer on the team.

Notably, these effects showed up without any improvement in AI adoption. In other words, calling AI an “employee” didn’t make people use it more, but it did introduce confusion and governance risks.

The research suggests a better path: treat AI as a powerful system that humans supervise, not as a co-worker that shares accountability.

The findings point to a shift in focus: the core challenge is not whether to deploy agentic AI, but how to integrate it into work so that humans stay clearly accountable.

According to the research, leaders should concentrate on:

• Redesigning workflows: Map where AI agents create value, where humans must stay in the loop, and how handoffs work. The goal is to reimagine processes so AI augments people rather than replaces their judgment.
• Clarifying roles: Define explicitly what the AI does (e.g., draft, recommend, monitor) and what humans own (e.g., final decisions, exceptions, ethical calls). This reduces the role uncertainty observed in the study.
• Strengthening governance: Put in place policies, controls, and review steps that make it clear who is accountable for outcomes, even when AI is heavily involved.

In short, the challenge is to reshape work, roles, and governance so that increasingly capable AI systems are supervised effectively, while humans remain the ultimate decision-makers.

Based on the research, a more effective approach is to position AI agents as advanced tools and systems that people manage, not as peers on the org chart.

Practically, that means:

• Language and framing: Refer to AI as a “system,” “agent,” or “capability,” not as a “team member” or “digital employee.” This helps maintain clear accountability.
• Clear ownership: Assign human owners for each AI use case. A named person or role is responsible for outcomes, oversight, and escalation paths.
• Defined supervision steps: Build in checkpoints where humans must review, approve, or override AI outputs. The study found that when AI is anthropomorphized, review quality drops—so make review an explicit part of the workflow.
• Role clarity for employees: Communicate how AI will support their work (e.g., drafting, analysis, monitoring) and what remains firmly in their remit. This reduces the uncertainty about roles that the research documented.

This framing allows organizations to rethink and reimagine how work gets done with AI, while keeping human accountability and oversight front and center.