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What happens when the AI starts getting it right — and the human starts getting it wrong?
In this episode, I go somewhere my recent article on AI governance in HUB operations couldn't quite reach. The article laid out the handoff problem: most HUB programs claim to have human-in-the-loop oversight, but without workflow-level specificity, you don't have governance — you have the appearance of it.
But there's a third failure mode I saved for the podcast. It's what happens when governance is working exactly as designed — and the human is still getting it wrong. Not because the system failed. Because the human over-trusted it.
What we cover in this episode
The research on automation bias in clinical settings is consistent: trained professionals who work alongside AI tools over time develop a tendency to accept AI outputs without critically evaluating them — not because they're careless, but because the AI is usually right. The research calls this moral deskilling: the progressive erosion of judgment in environments that reward speed and penalize the friction of independent thought.
I walk through what this looks like inside specialty HUB programs twelve months post-deployment, why the signals are invisible to standard compliance frameworks, and three things governance leaders can do to keep the human sharp enough to matter when the system gets it wrong.
Key questions this episode sits with
What decisions still require your full, unassisted judgment — and is that list getting shorter?
If the pushback rate on AI outputs in your program has dropped, is it because the AI got better — or because the reviewers stopped trusting their own instincts?
Read the companion article
📄 The Handoff Problem: Why HUBs Get Human-in-the-Loop Wrong — Article 2 in the AI Governance in HUB Operations series at Artha Consulting Lab
🎙️ Subscribe to HUB Brief at thehubbrief.substack.com
🔗 Follow Ankur Jain on LinkedIn: linkedin.com/in/ankurjaincons/
By Ankur Jain Esq.What happens when the AI starts getting it right — and the human starts getting it wrong?
In this episode, I go somewhere my recent article on AI governance in HUB operations couldn't quite reach. The article laid out the handoff problem: most HUB programs claim to have human-in-the-loop oversight, but without workflow-level specificity, you don't have governance — you have the appearance of it.
But there's a third failure mode I saved for the podcast. It's what happens when governance is working exactly as designed — and the human is still getting it wrong. Not because the system failed. Because the human over-trusted it.
What we cover in this episode
The research on automation bias in clinical settings is consistent: trained professionals who work alongside AI tools over time develop a tendency to accept AI outputs without critically evaluating them — not because they're careless, but because the AI is usually right. The research calls this moral deskilling: the progressive erosion of judgment in environments that reward speed and penalize the friction of independent thought.
I walk through what this looks like inside specialty HUB programs twelve months post-deployment, why the signals are invisible to standard compliance frameworks, and three things governance leaders can do to keep the human sharp enough to matter when the system gets it wrong.
Key questions this episode sits with
What decisions still require your full, unassisted judgment — and is that list getting shorter?
If the pushback rate on AI outputs in your program has dropped, is it because the AI got better — or because the reviewers stopped trusting their own instincts?
Read the companion article
📄 The Handoff Problem: Why HUBs Get Human-in-the-Loop Wrong — Article 2 in the AI Governance in HUB Operations series at Artha Consulting Lab
🎙️ Subscribe to HUB Brief at thehubbrief.substack.com
🔗 Follow Ankur Jain on LinkedIn: linkedin.com/in/ankurjaincons/