AI agents need managers, not just users—companies lag in training

The business world has spent two years teaching employees the basics of AI: how to write good prompts, how to use ChatGPT and similar tools to summarize meetings or reformat data. That training has worked in its limited way—employees are now comfortable with AI and see its value. But a new generation of AI systems is upending that model. These agents do not wait for instructions. They pursue goals, call external tools, make recommendations, trigger actions, and hand work from one system to another. The human role changes fundamentally: the employee becomes not a user but a manager, responsible for directing and overseeing the agent.

Gianpaolo Barozzi, Cisco's 3P CTO, captures the shift: "Agentic AI changes the relationship between people and technology. It requires new ways of setting boundaries, calibrating trust, and maintaining human accountability." The problem is that most companies are still teaching people how to get more out of AI, not how to lead it. This capability gap is becoming one of the most critical challenges in enterprise AI adoption. Without proper management discipline, employees may grant agents more trust and autonomy than the work or the technology warrants.

The risk is subtle but serious. Agents are becoming useful, fast, fluent, and confident enough that people begin to relax their judgment at exactly the moment they need to sharpen it. A 2025 MIT Sloan Management Review and BCG study found that 76% of executives already view agentic AI more as a coworker than as a tool. That language points in the right direction—agents will increasingly function like teammates, participating in work, shaping decisions, coordinating tasks, and taking on pieces of execution that used to belong only to people. But "teammate" is not a single relationship. Once an agent begins participating in the work, people need to understand what role it is playing and manage it accordingly.

The article identifies five distinct mental models. When an agent is treated as a tool, the human expects it to perform a bounded task on command—for example, summarizing a meeting transcript or reformatting data into a report. The person operates the system, evaluates the output, and remains responsible. This works for narrow, repeatable tasks but becomes insufficient as the agent gains autonomy. When treated as an intern, the human provides context, inspects work closely, corrects mistakes, and gradually expands responsibilities. A manager might ask an agent to draft a client briefing, then review and coach it on organizational standards. Wharton professor Ethan Mollick has popularized the "AI intern" analogy: like a new employee, the AI needs a defined role, sufficient context, clear assignments, and ongoing evaluation of reliability.

When an agent is treated as a service provider, the human defines desired outcomes, scope, deliverables, performance standards, decision rights, constraints, and escalation requirements. The agent is given discretion over execution within those parameters while the organization maintains visibility and control. Hala Jalwan, co-founder and CEO of Rivio.ai, which builds procurement agents designed to operate as service providers, explains: "When an agent takes responsibility for a body of work, the human role becomes more managerial, not less important. Someone still has to set the objective, define the boundaries, determine when the agent should escalate, and remain accountable for the outcome." A teammate relationship is collaborative and ongoing: the agent helps develop ideas, coordinates work, responds to feedback, and participates in shared problem-solving. An expert relationship is consultative: the human turns to the agent for specialized knowledge that may exceed their own capabilities. The primary risk is deference—because the agent appears authoritative, people may fail to question its conclusions.

BNY offers a real-world example of managing agents as colleagues. BNY CIO and Global Head of Engineering Leigh-Ann Russell explains: "We launched our first digital employee in 2025 with the same approach as our enterprise-wide systems—governance was built in from the start. We understood that, over time, digital agents and people would work side by side. Digital employees are onboarded, governed, and continuously monitored with the same rigor we expect across our technology estate, while accountability always remains with our people." The example shows that treating an agent as a colleague does not mean treating it as an equal bearer of responsibility. It requires clear oversight, performance expectations, and human ownership.

Research at Procter & Gamble illustrates how agents can play an expert role within a broader collaborative relationship. In a field experiment with professionals working on product innovation challenges, AI helped employees generate solutions beyond their own areas of specialization. Commercial professionals incorporated more technical thinking, and R&D professionals incorporated more commercial thinking. Less experienced participants were able to perform more like teammates with deeper expertise. The AI acted as a source of specialized knowledge that broadened human perspective, but employees still had to assess whether its recommendations fit the business context.

Each model can be useful, and each creates different risks when applied carelessly. A person who sees an agent as an expert may fail to question it. Someone who views it as a teammate may assume it shares organizational context or responsibility. Someone who treats it as a service provider may delegate an outcome without maintaining sufficient visibility. Someone who treats it like an intern may waste time micromanaging work it can already perform reliably. Nor should an agent be placed permanently into one category: the same system may function like an expert when analyzing a large dataset, a service provider when executing a defined workflow, a teammate when helping a group solve a problem, and an intern when navigating a new or ambiguous situation.

This is why companies need to build "human-agent fluency": the ability to recognize which relationship the work requires and manage the agent accordingly. That is very different from prompt engineering. Prompt engineering asks how to get a better answer from the machine. Human-agent fluency asks what role the agent should play and what that role requires of the human. Tom Lamberty, Senior Consultant in Cisco's 3P Tech Office, puts it this way: "How we define an agent's role shapes how we work with it: how much authority we give it, how closely we question it, and where we retain judgment. We shape our agents, and then our agents shape us."

Research by Boston University's Emma Wiles found that people caught 18% fewer errors when work was described as coming from an agentic "AI employee" rather than a chatbot. The finding suggests that framing matters deeply. When agents function like teammates, they require clear standards, challenge, escalation, and ownership. A teammate relationship without appropriate oversight is not teaming—it is overtrust. Organizations should make the expectations associated with each model explicit: what the agent may decide, when it may act independently, how its work will be reviewed, and what would justify changing the relationship. Employees must also learn to recognize when they are granting an agent too much, or too little, trust. That is the difference between AI usage and agentic readiness. A workforce is not ready simply because employees use AI frequently. It is ready when people can identify the relationship the work requires, manage it appropriately, and recalibrate it as the task, risk, and agent capability evolve. Across every model, human accountability for the outcome remains constant.