Bringing AI into a team changes how the team works together. It reduces interpersonal communication, trust and empathy, because it removes the friction teams need in order to innovate. Without deliberate rules, shared mental models, the ability to coordinate and social learning all wear away. High-performing teams therefore treat AI like a new team member, with explicit agreements on roles, decision-making authority and communication channels.
Key Takeaways
- Trust in a team grows when people experience each other as fallible. Someone who keeps consulting AI as an all-knowing source comes across to colleagues as smoother and more infallible, which actively wears down trust and likability.
- AI use shifts team communication from the social level to a purely task-focused one. Because the brain needs emotional processing for every decision, the result is not objectivity but communication that only looks objective.
- Young people who use a lot of AI early on get less practice in social skills and empathy, because they skip the implicit social learning at work that earlier career starters could not avoid.
- Integrating AI into a team takes the same deliberate effort as onboarding a new team member: explicit rules, clear roles and defined decision-making authority for people and machines alike.
Do You Still Need a Team When AI Can Do Almost Everything?
Yes, but a different kind of team. That answer is the starting point for any serious look at how AI changes teamwork, and at what happens to trust in teams along the way. The idea that one person will soon steer a swarm of agents and won’t need colleagues anymore sounds efficient. It ignores what people need in order to stay human.
Early signals from research point in an uncomfortable direction. Heavy AI use can make people lonelier and less empathetic. Talking to an AI feels pleasant, but the body no longer produces the hormones that create social bonds. Oxytocin, for example, is released when people look each other in the eye. With an AI, that doesn’t happen.
So the more honest question is what role AI takes on inside the team, not whether it will replace teams. Once you’ve decided that you still need a team, the next task follows: working out where the machine does the thinking and where the people do.
How AI Reduces Friction in Teams
AI reduces friction in a team, and friction isn’t bad in itself: innovation is made of it. When friction disappears, part of what makes a team productive disappears with it.
At first, heavy AI use doesn’t change how much people communicate. It changes the quality. Conversations between people slide down to a purely task- and goal-oriented level and lose their social and emotional side. Many teams welcome that at first. Finally objective. Finally down to business.
In neurobiological terms, that objectivity is an illusion. For the prefrontal cortex to work at all, the signal passes through the limbic system, which handles emotions. So emotions are always involved, whether you feel them or not. What gets sold as objective is really pseudo-objective. And that layer wears away the foundations a team runs on.
Why Trust in Teams Can’t Grow Without Fallibility
Trust needs two things that AI systematically reduces in a team: favors done for each other and fallibility you have seen with your own eyes. Both become rarer when everyone would rather put their questions to the machine than to a colleague.
The first mechanism is simple. Trust grows when you ask someone for something and they help you. If you get all your answers from AI, you ask less often and give others fewer chances to do you a favor. Trust never starts to build.
The second mechanism is subtler. You have to see the other person as vulnerable before you can trust them. In a business setting, very few people like to show their flaws. Someone who always carries an all-knowing tool in their pocket looks even smoother from the outside. The other person’s brain can no longer file them under vulnerable.
“There is a high correlation between vulnerability and likeability.”
(Jasmine Simons)
A friendly AI makes this worse. It praises you, confirms what you say, tells you you’re right. A pleasant conversation partner, on the surface. But there is no risk and no pain in it, and you need both to build real relationships.
People Who Already Struggle Withdraw Even Further
AI hits hardest the people who already find communication difficult. That is the central social finding, and it applies to individual team members as well as to entire careers.
In settings where AI served as a companion for isolated or anxious people, they formed a strong emotional bond with the machine. In the short term, that helped. In the long term, it led to even more social isolation. The same pattern shows up in teams. People who used to struggle to stand up for their ideas and claim their space turn with relief to the agreeable AI. With the AI, there is no pain and no risk.
The second effect has been studied more closely in young people. Those who use a lot of AI get less practice in empathy and understand social rules less well. That hits exactly the phase in which you often know a lot about your field but can’t yet apply it.
Starting a career is largely social learning. How does work actually work? How do I read what my colleagues need so we can coordinate? What feedback do you need from me, and when and where do I ask which questions? When young people hand these questions to an AI, that learning no longer happens.
How AI Changes Feedback Culture in Teams
Social learning isn’t only falling away for the young. It also breaks down from the other end. When experienced colleagues rely heavily on AI too, the feedback culture of the whole team erodes into purely factual feedback.
People don’t learn because they enjoy learning. The brain doesn’t like it. Learning happens through relationships, through pain you feel or through pleasure you get from working with others. Reduce feedback to the facts alone, and you remove the lever that drives learning in the first place.
The pandemic laid the groundwork here. Many teams never fully returned to the office, and communication had already dropped before that. Add AI, and communication between people may fall even further.
The Shared Mental Model Quietly Unravels
Teams that rely heavily on AI lose their shared mental model, and with it their ability to coordinate. That model doesn’t come from a decision. It comes from people talking to each other.
Through conversation, a team automatically builds a shared picture: of the work in front of it, of the social relationships and of the wider context. When that model falls away, the team loses several dimensions at once. The diversity of ideas drops, conflicts increase, trust collapses.
The information loss runs in two directions. You stop passing on to the team what you have sorted out with the AI. And because the others are talking to their AI as well, they don’t come to you for that information either. Everyone is saturated with information, each on their own.
In the end, the team lacks not only a shared picture of the information but also of the goal and of where it stands. Where are we, actually? When a team can no longer answer that question together, its coordination suffers, and trust takes another hit.
How Teams Erode Without Anyone Noticing
Dysfunction rarely comes from a single event. It’s a slow decline over time. The story dysfunctional teams tell about themselves almost always sounds the same: everything used to be fine, then this one team member or this one boss arrived, and after that things eroded piece by piece.
In most cases, that one person isn’t the real problem but more of a dumping ground for a lot of things. What matters is the dynamic. No new boss ruins a team in one stroke. The decline creeps in.
That is exactly where the danger of quiet AI use lies. It creeps in the same way. Nobody consciously decides to weaken the team. It happens slowly, because the friction gets a little smaller every day.
What Role AI Should Play: Onboard It Like a New Team Member
Treat AI like a new colleague you onboard, not like a tool you hand out. The effort is necessary, and right now very few teams make it.
In most teams, AI use is implicit, sometimes even hidden: officially not allowed, so people just use their private account. The alternative is to make AI use explicit and decide as a team which role the AI plays. There are three roles to choose from, and each has different consequences.
| Role of the AI | Guiding question for the team | Decision-making authority of the AI |
|---|---|---|
| Coordinator | When does the human think, and when the machine? How do its decisions become transparent? | Only with clear transparency, otherwise trust suffers |
| Source of creativity | Where does the AI expose blind spots? | None, it provides input, not decisions |
| Quality assurance | Which review decisions may it make, and which stay with people? | Deliberately limited and agreed on |
Whatever the role, make it transparent where which kind of thinking happens, and put that into a clear agreement within the team. That way you invest in trust, the ability to handle conflict and communication instead of losing them without noticing.
The method isn’t new. High-performing teams have always made their rules explicit. When AI joins, you are simply a high-performing team that also settles how to integrate this AI.
Team Resilience Is a Decision, Not a Side Effect
The first step is a conscious decision that the team cares about its own resilience. Without that decision, no method will work.
The comparison with diet holds up well here. If you want to live healthily, you decide that first and then pay for it in discipline and routine. You don’t eat Sachertorte every day, even though you love it. In the same way, a team can decide that resilience doesn’t matter to it and accept the cost of erosion. That’s legitimate, as long as it’s a conscious choice.
If you decide otherwise, you accept the effort: discipline, time and new agreements. In a world that moves fast and where plans change constantly, team resilience is what lets you act quickly. That’s not a feel-good argument. It’s an economic one.
How AI Shifts Leadership from Expertise to Relationships
AI changes the leadership role because it takes over more and more of the subject-matter work. Promoting the best specialist to manager rarely made sense anyway. The usual result is a bad manager and a lost specialist.
Still, people need leaders who understand their field. An agile coach can support teams but wouldn’t be a good manager for a developer or tester, because the professional connection and the mentoring are missing. That closeness still matters.
The role is shifting all the same. As AI takes over more of the subject-matter work, the leader becomes more of the person who teaches critical thinking, reminds people of discipline and stands beside them as a coach. Like the soccer coach on the sidelines who says: you’re still running your laps. Many people need exactly that, because in a world full of options they have to narrow their choices and build habits that last.
This shift hits managers hard whose sense of self-efficacy rests entirely on technical excellence. Many corporate reward systems are built on exactly that. Anyone rethinking leadership in the age of AI also has to answer where managers will draw their effectiveness from.
