AI rarely says, “I’m not sure.” And that alone should make us pause.
Most AI tools sound confident, decisive, and data backed. Which is exactly why people tend to trust them. Research in behavioral science calls this automation bias. Studies show that humans are more likely to follow automated recommendations than question them, even when something feels slightly off.
This plays out quietly at work every day:
- A manager forwards an AI generated summary without reading the full report.
- A team follows a recommendation because it looks logical and well structured.
- A decision gets made faster than usual, with less discussion than usual.
And no one questions it. Because questioning AI somehow feels harder than questioning a colleague.
Later, when things do not land as expected, a familiar question appears: “Who approved this decision?”
AI, of course, does not raise its hand.
This is the gap organizations are starting to feel. It is not a lack of AI tools or technical training. Most employees already know how to use the tools. The real gap is knowing how to decide when AI is in the room.
Research from Harvard Business School shows that teams perform significantly better with AI when they are trained to challenge outputs, apply context, and exercise human judgment, rather than treating AI as an authority. Without this, confidence drops and errors scale faster.
We hear this reflected in conversations with teams.
“It looked right, so we went with it.”
“I felt unsure, but the system recommended it.”
Decision making with AI is different. It requires context, ethical judgment, accountability, and the confidence to slow down when speed is tempting.
This is where learning needs to evolve.
The most forward looking organizations are shifting away from only teaching tools. Instead, they are building decision capability through real scenarios, guided reflection, and practice conversations around risk and responsibility. Because when AI gets it wrong, it does not correct the course. People do.
Athiya works with organizations to build this exact capability helping teams learn how to use AI appropriately, question it confidently, and make better decisions when it matters most.
In 2026, this may be the skill no one can afford to leave untrained.
