Time’s up for those who won’t change
This isn’t cruelty, it’s realism. If AI explains ideas better than you and explanation has been your core skill for decades, what’s left? For many, the answer may be ‘very little’. Denial won’t change that; it only delays it.
Let me ask a hard question: when was the last time a tenured business school professor actually failed at something that mattered? I do not mean published a paper that got rejected or received a bad teaching evaluation. I mean, actually failed. Lost money, blew a negotiation, hired the wrong person, made a call that kept him/her up at night. For most members of the faculty, the answer is ‘never’. And that’s the real problem with preparing them for an AI world.
Once AI assumes responsibility for knowledge, what remains is experience and judgement – the insights born from failure and pressure. Yet, one cannot teach what one has never lived.
In fact, most B-School professors have never worked in business. Undergraduate to masters to PhD to faculty – that’s the usual path. They’ve studied strategy but never executed it. They’ve analysed leadership but never led teams through a crisis.
When a student asks, “How do you know when to pivot?” What can they say? They’ve analysed pivots in case studies. They’ve never made one at 2 am, with payroll due.
AI exposed this gap brutally. If you’re transmitting knowledge from textbooks, AI does it better. The only defence is lived experience, and most faculty don’t have it.
How good teaching looks: I know a strategy professor who spent years in consulting before teaching. He teaches through live simulations, where things go wrong. Incomplete data, contradictory advice, time pressure, and team conflicts. In one session, students had to pitch to a hostile board of executives. The AI had built a beautiful deck. But when an executive said, “Your numbers don’t add up, and now I don’t trust you”, the pitch collapsed. No algorithm could save them.
That’s what remains when AI handles knowledge. Teaching students what to do when perfect analysis meets imperfect reality. But you need to have lived that reality to teach it effectively.
How big is the challenge?: Enormous. Not because transformation is impossible, but because what’s needed collides with how academia operates. Real training means sending faculty into companies for six months, as embedded observers. Creating peer learning communities, where those who’ve made the shift train others. Using AI itself to help faculty practise new facilitation techniques.
Professors spending six months in industry aren’t publishing papers; they’re falling behind in tenure reviews. Academic incentives actively punish exactly the work that would make them most impactful. The hardest part is that lived experience can’t be simulated. Professors who have never negotiated a deal can run exercises, but they can’t teach the instinct to walk away.
Yet, some make it work: I know professors who’ve taken consulting projects to stay connected to real problems. Members of a faculty who’ve joined advisory boards, partnered with start-ups, and spent sabbaticals embedded in companies. They recognised the gap and filled it. Others have embraced AI to create realistic simulations, provide feedback at scale, and identify struggling students early. They’ve stopped competing with AI and started leveraging it.
These members aren’t waiting for institutional permission. They’re proving that transformation is possible when you’re willing to do the work. Some will change. Most won’t. For universities, that makes the next move obvious. Redesign the faculty model. Bring in practitioners on shorter contracts: a CFO who led a turnaround teaches for two years, then returns to industry. Make faculty smaller, more dynamic, and more connected to actual business.
In the end, some faculty will transition out – not in defeat, but in recognition that the profession has evolved. This transformation will be uncomfortable, but a world where AI teaches better, while students pay premium prices, is far worse.
The challenge of training faculty for the AI era isn’t technical. It’s existential. It requires rethinking what faculty are for when AI handles knowledge delivery. The ones who survive won’t be the most credentialed. There’ll be those who’ve done the work they teach or who went out and got that experience, when they realised they needed it.
And, the ones waiting for the world to return to how it was? Their time has already run out.