When teams add AI into their workflows, many leaders assume the biggest challenges will be about tools, getting the right models, access, or accuracy. What research is starting to show is more fundamental: AI changes how teams think and work together, and unless that shift is understood and managed, performance can suffer.
This matters most in decision-making, innovation, and improvement work—contexts where information is incomplete, interpretations conflict, and responsibility cannot be delegated.
Here’s what the research says about the core patterns you’re likely to see.
Communication and Team Cognition Breakdowns
Researchers find that when you introduce an AI teammate, coordination and communication within the team can decline, not improve. According to a comprehensive review of human–AI teams, adding an AI agent often reduces team coordination, communication, and trust, precisely because shared understanding and mutual awareness are weaker in mixed human–AI teams than in all-human teams.
AI models can produce output quickly and confidently. That can mask gaps in meaning or assumptions, leading teams to move ahead without shared clarity.
Shared Mental Models Are Essential
A central concept from team science is the shared mental model: a common, internal understanding of the task, roles, goals, and each partner’s capabilities.
In human teams, strong shared mental models correlate with better coordination and performance. Research adapting this idea to human AI settings argues that without compatible mental models between humans and AI, teams struggle to benefit from collaboration.
Other work shows that AI’s technical accuracy alone does not guarantee good team performance. What matters is whether humans understand AI strengths and limitations and know when to rely on its output.
Here are mental model than can be shared
AI as Input, Not Authority
In strong teams, AI is treated as an advisor rather than a decision-maker. It surfaces options, risks, and trade-offs, while humans remain accountable for choices—especially when consequences are uneven or political. Problems arise when some team members treat AI output as a recommendation and others as a decision, without ever making that difference explicit.
AI as a Starting Point for Thinking
Many teams succeed when they share the expectation that AI provides a first draft, not a final answer. The value comes from reacting to the output together—questioning assumptions, refining interpretations, and adding missing perspectives. When this shared model is absent, conversation ends too early and apparent alignment replaces real understanding.
AI Detects Patterns; Humans Own Context
AI is effective at identifying patterns and anomalies across large volumes of information. Humans contribute what AI lacks: knowledge of local constraints, incentives, history, and impact on people. Collaboration breaks down when AI-generated patterns are treated as neutral truth instead of inputs that require interpretation.
Trust Builds Through Visible Learning
Teams that work well with AI expect mistakes and use them as learning moments. When failures are discussed openly and the collaboration is adjusted, trust grows over time. Where errors are silently corrected or ignored, trust erodes and the AI becomes either underused or overtrusted.
Alignment Is Ongoing, Not One-Time
Shared mental models are not defined once and enforced. They are reinforced through repeated use, shared review, and ongoing dialogue about how the AI is meant to support the work. In complex environments, this alignment is what allows AI to accelerate thinking without weakening responsibility.
Trust is Dynamic and Must Be Managed
Trust isn’t a static “on/off” switch; it’s something teams build through experience. Research frameworks on human–AI trust highlight that trust depends on people’s perceptions of both the AI system and the collaboration context.
When AI makes mistakes—or when its competence is misunderstood—trust can drop quickly. Without explicit processes to analyze those failures together, people either disengage from the AI or over-rely on it without real understanding.
High Skill + Good Alignment = Better Outcomes
Large empirical syntheses show a nuanced truth: Human–AI combinations can help people perform better than humans alone, but they do not automatically outperform the best of humans or AI alone unless the collaboration is structured well.
This nuance matters. The research does not say AI is inherently bad; it says that synergy depends on how the team makes sense of the task together, not just on tool quality.
What This Means in Practice
The science converges on three fundamentals:
1. You cannot assume shared understanding.
AI doesn’t come with a built-in model of your team’s goals or priorities. People must create a shared mental model with the AI through explicit alignment work.
2. Trust must be observable, not assumed.
Trust rises and falls with experience. Teams need shared processes to inspect failures and adapt, instead of silently fixing errors alone.
3. Human judgment stays central in uncertainty.
In leadership, improvement, and innovation work, responsibility cannot be delegated—even when AI delivers strong signals. What AI cannot do well without support is interpret ambiguous information and negotiate conflicting interpretations.
Bottom Line
Better tools help, but they don’t solve the coordination, communication, and trust problems that emerge when AI enters human systems. The science suggests that collaborative practice and explicit alignment—not just better prompting—are essential if teams want to benefit from AI in the messy, ambiguous work that matters most.
HAdding AI to a team is often described as a productivity upgrade.
In practice, it behaves more like adding a new team member—one that changes how people think, interact, and decide.
That change is subtle at first. Conversations feel smoother. Outputs arrive faster. Fewer disagreements surface.
And yet, over time, something degrades: clarity, ownership, trust, and sometimes performance itself.
This is not a tooling problem. It is a collaboration problem.
AI Changes Team Dynamics—Whether You Plan for It or Not
When a person joins or leaves a team, dynamics shift. Roles are renegotiated. Assumptions are tested.
AI has a similar effect, but with one critical difference: teams rarely treat it as a social or cognitive actor.
Research refers to these setups as Human–AI Teams. What is striking is that many of them underperform compared to well-functioning all-human teams—not because AI is weak, but because collaboration fundamentals are missing.
Three patterns show up repeatedly.
1. Communication Becomes Thinner, Not Clearer
Humans adapt their communication when interacting with AI.
Instructions get simplified. Context is reduced. Nuance is dropped.
This works for bounded tasks. It fails in complex work.
AI produces output that appears structured and coherent, but it often reflects only the narrow slice of intent it was given. Teams then compensate downstream—through rework, clarification, or silent corrections that never make it back into shared understanding.
The issue is not prompting quality alone. It is the absence of mutual sense-making.
2. Shared Understanding Erodes Quietly
In effective teams, people align continuously on:
- what problem they are solving,
- who is responsible for what,
- where judgment is required.
With AI, those alignments are often assumed rather than discussed.
Teams disagree—implicitly—on whether AI is advising, deciding, exploring, or validating.
Because AI outputs sound confident, these differences stay hidden until consequences appear.
Disagreement is not the problem.
Unexamined disagreement is.
3. Trust Becomes Fragile—and Hard to Repair
When AI fails, trust drops fast.
People either stop using it altogether or keep using it while discounting its output informally. Both responses are risky. One wastes capability; the other creates hidden workarounds and false confidence.
Trust in Human–AI collaboration does not come from accuracy alone.
It comes from understanding why the system behaves as it does and having a way to respond when it does not.
Without that, sociability declines, motivation drops, and collaboration narrows to mechanical use.
What Actually Makes Human–AI Collaboration Work
Strong Human–AI collaboration rests on fundamentals that resemble good human teamwork more than advanced technology.
First: Shared mental models.
Teams need explicit agreement on the AI’s role, limits, and decision authority. Not once—but continuously. This includes practicing together, comparing interpretations, and updating expectations based on real use.
Second: Visible learning loops.
Errors should trigger joint analysis, not quiet fixes. Trust grows when teams can explain failures, adapt behavior, and see improvement over time.
Third: Preserved human judgment.
In complex environments—leadership, improvement, innovation—responsibility cannot be delegated. AI can inform and accelerate, but sense-making remains a human, social act.
Interestingly, research shows that highly skilled humans working with capable AI can outperform other configurations.
But only when collaboration is designed deliberately—not assumed.
The Core Misconception
Many organizations believe Human–AI collaboration will improve naturally as tools get better.
In reality, collaboration breaks precisely where information is incomplete, interpretations conflict, and accountability matters most.
Better models help.
Better conversations matter more.
Human–AI collaboration succeeds not when AI replaces thinking—but when it makes human thinking visible, discussable, and testable again.
That is the real work.uman–AI Collaboration: Why It Feels Harder Than Expected