Can AI Agents Solve the Dysfunctions Teams Face?
With the rise of the agentic world, AI agents have quickly entered our working lives.
Human intelligence, emotional intelligence and empathy have made people even more valuable. AI agents now take on the repetitive, error-prone operational processes that people used to drown in, while humans increasingly steer strategy rather than run operations. In teams, everyone is now a leader, because each of us is likely to lead at least one AI agent in the near future. Whatever our role, we will guide the AI agent’s direction and help build its capabilities in the operational side of the work. So in such a world, has the balance of being a team changed as well?
Let’s examine these new dynamics through Patrick Lencioni’s Five Dysfunctions of a Team model:
Absence of Trust
Team members avoid revealing their weaknesses to one another so they won’t be left vulnerable. It therefore helps to create environments where we can openly discuss our gaps and weaknesses and don’t hesitate to ask for help. Creating this environment isn’t always easy. If we say “we’re safe here” but don’t create the experience to back it up, we end up with a false sense of trust.
Agents, on the other hand, are more transparent, more objective and less emotional. So are they perfect? No. They still need human guidance on empathy and ethics. In fact, a toxic environment is possible, one in which each employee develops their own AI agent to serve their personal interests while ignoring what’s best for the team.
The new balance of trust: Trust is no longer built only from person to person, but also on honesty about the outcomes of the collaboration people build with AI.
Fear of Conflict
Fear of conflict is a natural consequence of an environment lacking trust. Healthy conflict is sometimes needed to discuss ideas openly and seize opportunities. Conflict allows different and opposing views to feed each other productively. When agents join the team and take over operations, technical debates between people decrease. But this can lead to a dangerous silence. If teams hesitate to question the data an agent provides or the path it follows, “artificial harmony” sets in.
The new balance of conflict: Healthy conflict should center on debating ethical errors or strategic blind spots in the agent’s outputs.
Lack of Commitment
Where there is no healthy conflict, lack of commitment follows naturally. In a state of artificial harmony, team members can slip into a “there’s a task in front of me, let me just get it done” mode. Team members who don’t voice their ideas honestly often appear to agree, yet they don’t truly buy in. In such situations, clearly stated goals, respect for expertise, autonomy and recognition can help. Now consider a team where agents are members: when everyone leads their own agent, shared understanding risks falling apart. If team members optimize their agents’ outputs only for their own job descriptions rather than the common goal, collective success becomes impossible.
The new balance of commitment: Commitment is now measured by how well each “leader” (employee) aligns the prompts they give their agent with the team’s vision.
Avoidance of Accountability
If a team member doesn’t feel committed to the work or the team, they won’t hold others accountable when something conflicts with the interests of the work, no matter how focused they are. Without accountability, work gets delayed, quality drops, and eventually the elephant in the room is never discussed and transparency is lost. AI agents, by contrast, can regularly put the facts in front of the team. They can set up processes and prepare objective reports that get teams talking. The key point is making sure that when there is a delay or an agent makes a mistake, the team still takes responsibility.
The new balance of accountability: Team members should hold each other accountable not only for their own work, but also for the capability and oversight of the agents they manage.
Inattention to Results
When accountability doesn’t work properly, results get ignored too. Yet the team’s goals, and the effort team members put into reaching them, should be tracked by the team as a whole. Agents working 24/7 can deliver results faster, and they can accelerate the team’s output. Agents can also track results at regular intervals, share them with the team, and even suggest improvement actions. The risk here is that teams get buried in the volume of agent output and lose sight of whether that output creates real value.
The new balance of results: Success isn’t how many tasks an agent completes or how efficiently it works; it’s which strategic problem the person solves together with that agent and what value they bring to the team.
AI agents make our lives and work much easier. But as we can see, sustainable success is still only possible through the balance that humans create and manage.
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