It was Sprint Planning, and the team’s AI-powered estimation tool had just suggested that a feature would take “3 story points” to complete. One of the senior developers noticed a hidden complexity in the integration work that the AI hadn’t accounted for. But instead of speaking up, she hesitated.
The team had been relying on the tool’s accuracy for weeks, and questioning it now felt like rocking the boat. The moment passed, the estimate went unchallenged, and by mid-sprint, the team was scrambling to manage the unexpected work.
What.
Psychological safety, the belief that you can take interpersonal risks without fear of embarrassment or retribution, is a cornerstone of high-performing Agile teams. When AI enters the workflow, safety becomes even more critical. AI outputs often carry a veneer of authority, especially when wrapped in confidence scores or polished visuals. Without the freedom to question those outputs, teams risk letting flawed assumptions drive their plans and priorities.
So What.
Without psychological safety, two patterns emerge:
Blind following – Teams accept AI recommendations without discussion, trusting the algorithm over their own expertise.
Silent dissent – Team members see flaws but choose not to speak up, fearing conflict or appearing resistant to change.
Both erode Agile’s adaptive nature. As Amy Edmondson’s The Fearless Organization reminds us, silence doesn’t mean agreement, it means learning opportunities are being lost. In the context of AI, those missed opportunities can compound quickly, introducing bias, risk, and waste.
Now What.
Leaders can strengthen psychological safety in AI-driven Agile environments by:
Modeling curiosity – Treat AI outputs as prompts for inquiry, not verdicts.
Inviting dissent – Ask, “Who sees it differently?” or “What might the AI be missing?” in every key decision.
Reinforcing human judgment – Make it explicit that final decisions rest with the team, not the tool.
Sharing counterexamples – Highlight times when challenging the AI improved outcomes, to normalize healthy pushback.
These practices keep humans in the loop, ensuring AI enhances, rather than undermines, collaborative problem-solving.
Closing
AI in Agile works best when it’s a collaborator, not an overlord. If people don’t feel safe to speak up when something seems off, the team loses its most powerful adaptive advantage. Create the space where questioning AI is a sign of strength, not defiance, and your team will get the best of both worlds: the precision of data and the insight of human judgment. Don't forget the humans!