What I Tell MBA Students Who Want to Build in the Age of AI

When BIMPreneur — the entrepreneurship club of Bharathidasan Institute of Management in Trichy — invited me to speak as part of their PRAYAANA series, I didn't prepare a polished deck full of frameworks and models. I prepared to have an honest conversation.

MBA students get a lot of frameworks. What they often don't get is a practitioner's unvarnished account of what it actually feels like to build something — especially in a field as fast-moving and misunderstood as AI.

So that's what I gave them. Here's the core of it.

The Question That Changes Everything

Before you decide to build an AI company — or build anything with AI — there's one question you need to answer honestly: are you solving a real problem, or are you excited about the technology?

This sounds obvious. It isn't. In 2015, when I started WiselyWise, the honest answer was that I was excited about both — but I didn't yet know how to separate them. It took years of iteration, failed product directions, and conversations with hundreds of frustrated customers to learn the difference.

The technology being genuinely impressive is not sufficient justification for building a business around it. Your customers don't care that it's impressive. They care whether it saves them time, makes them money, or removes something they hate doing.

The AI-Native Advantage — and the Trap

Here's what's genuinely different about building in 2022 compared to 2015: you can build an AI-native business from day one. When I started WiselyWise, AI infrastructure was expensive, slow, and required deep technical expertise. Today, you can use large language models, computer vision APIs, and no-code AI tools to build an MVP in weeks.

That's the advantage. The trap is the same one that catches every generation of technology entrepreneurs: assuming that because the technology is new, the business fundamentals don't apply.

They do. Distribution still matters. Unit economics still matter. Customer retention still matters. AI doesn't make a bad business model good — it makes a good business model more efficient.

What I Wish I'd Known

If I were starting WiselyWise today with everything I know now, three things would be different:

• I'd pick a narrower problem earlier. We tried to be an AI platform for everything before we were excellent at anything. The discipline to resist scope creep when the technology tempts you in every direction is one of the hardest skills to build — and one of the most valuable.
• I'd find my first ten customers before writing a line of code. The conversations you have with potential customers before you build anything are worth more than six months of product development. They tell you what they will actually pay for, not what they say they want.
• I'd stop worrying about the technology being "good enough." Every product I've shipped has been imperfect. Every product I've ever used has been imperfect. Waiting for perfection is a form of procrastination with a credible alibi.

The MBA Advantage You're Not Using

Here's something I told the BIM students that seemed to surprise them: the MBA is genuinely useful for building AI companies — just not for the reasons most people think.

It's not the finance models or the strategy frameworks that matter most. It's the network, the credibility, and — if you use the programme correctly — the time to think deeply and experiment cheaply, before you have a mortgage and three people depending on your monthly salary transfer.

Most MBA students treat the degree as a credential to get a better job. The rare ones treat it as a two-year protected period to test ideas, build relationships, and develop the conviction to go after something worth building.

The AI era doesn't change this. It amplifies it. Because the people who will build the most interesting AI companies aren't necessarily the ones who understand the transformer architecture. They're the ones who deeply understand a domain — education, healthcare, logistics, finance — and know exactly which problems are worth solving.

One Practical Place to Start

If you're sitting in an MBA programme right now wondering where to begin: pick one thing in your programme, your internship, or your previous job that you found genuinely painful and repetitive. Not mildly annoying — genuinely painful. The kind of thing you avoided doing until you absolutely had to.

Now go find out if twenty other people in similar roles feel the same way. If they do, you have a problem worth solving. If AI can meaningfully help solve it, you have a business worth exploring. Everything else — the tech stack, the funding, the go-to-market — comes after you've answered those two questions.

The tools have never been more accessible. The window has never been more open. The only question is whether you're willing to start with the problem rather than the solution.

Want me to speak at your MBA programme or entrepreneurship club?
I deliver keynotes and interactive sessions on AI strategy, startup building, and the practical realities of entrepreneurship in the AI era — for business schools and entrepreneur communities across India, Singapore, and Asia.
Invite me to speak →  ·  Or start with the practical AI side: AI Essentials course at WiselyWise →