Most business schools are adding AI tools just to say they have them. That’s pointless. AI should change what students can actually do. Here’s what that means in practice. Before AI, we taught everyone the same way at the same speed. You got twelve weeks of finance whether you needed three weeks or twenty. Now students learn at their own pace. Our AI tutor teaches the technical content pre-class. When students come to class, they already know the basics. So we don’t waste time explaining concepts. We use class time for complex problems, live debates, decisions with no clear answer—the things that actually need a professor. This means every student can reach the same capability, even if their path looks completely different. AI removes the constraint that forced everyone to move together.

In what ways can AI-powered tutors or learning companions help bridge gaps between classroom teaching and real-world business applications?

In business, problems don’t arrive sequentially by subject. You’re not working on “marketing week” followed by “finance week.” You’re handling a pricing decision that needs marketing instincts, financial modelling, and supply chain understanding all at once, by tomorrow morning. Traditional education can rarely match that. A student hits a financial question while working on strategy, but the finance professor’s office hours are Thursday. By then, the moment’s gone.

AI tutors change the timing. Our students work on live business projects and when they hit a gap, the AI tutor is there. In the moment when the question actually matters. And, AI tutors don’t teach in isolation. A student asks about pricing strategy, and it can immediately connect that to relevant financial metrics, competitive positioning, and operational constraints, the way problems actually exist in business, not in separate course syllabi.

The bridge isn’t about better explanations. It’s about having expertise available when the problem demands it, not when the academic calendar or faculty schedule permit it.

As education becomes increasingly personalised through technology, how can institutions ensure that the “human touch” in mentorship and critical thinking isn’t lost?

When AI can answer technical questions instantly, human time becomes more valuable. The mistake most schools will make is trying to keep everything the same. Same office hours, same faculty availability for everything. That just spreads professors too thin. Instead, use faculty time only for what needs human judgment. At SP Jain Global, students learn technical content through AI. Then they face sessions where they must defend their thinking out loud. Faculty challenge them: “Why did you ignore this risk? What assumption haven’t you questioned?”

The human touch isn’t lost because of technology. It’s lost when faculty time gets wasted on routine explanations instead of the hard conversations that actually make students grow.

What role will AI-driven learning tools play in enhancing employability, from academic performance to interview readiness and professional confidence?

Traditionally, students practiced for interviews, went to campus recruitment, and got rejected with zero insight into why. Was it their answer structure? Their hesitation on financial questions? Their body language? They’d never know. They’d just hear “not a fit” and repeat the same mistakes at the next company.

AI breaks that black box. We have a Job Preparation Tutor (JPT) created by students for students. It predicts actual interview questions, allows students to practice doing mock interviews and gives them detailed feedback on how they can improve. And you can practice any number of times, unlike a human tutor.

A student practices, fails, understands exactly why, adjusts, and tries again within minutes. By the time they face a real recruiter, they’ve compressed what used to take twenty interviews across two years into three weeks of AI practice. The AI plays isn’t teaching them interview techniques. It’s giving them what the market used to provide painfully: clear signals on what works and what doesn’t, fast enough to actually improve before opportunities run out.

How can the integration of AI into education help make learning more inclusive and equitable, especially for students with different learning speeds and backgrounds?

For centuries, we’ve been teaching at one speed and calling the survivors “talented.” Think about a student from a tier-three engineering college who’s brilliant analytically but has never written a business case before. In a traditional classroom, he either fakes his way through or falls behind while others move on. With AI, he practices case analysis fifteen times before anyone sees his work, building the muscle memory at his own pace, without the embarrassment of asking “basic” questions in front of peers.

That’s not just about speed. It’s about background. Students from non-business families don’t intuitively understand corporate jargon or industry context. AI doesn’t judge them for asking what “EBITDA” means for the third time.

also challenges our best students differently. The student who cruises through can be pushed harder, given more complex scenarios, forced into deeper analysis without holding back the rest of the class.

The equity issue isn’t that some students are slower. It’s that we built a system where everyone had to move together. AI breaks that constraint.