Inside The Classroom: How AI Is Reshaping The MBA At Harvard Business School

Harvard Business School has unlimited AI tools at its disposal. What it’s betting on, Faculty Chair of Technology and Transformation Mitch Weiss says, is that the real product is still human judgment – the school aims to “forge the tool on the top of your neck,” as Weiss put it in a recent Poets&Quants interview, even while everyone else races to train AI models. 

That’s the view from the faculty chair’s office. Down in the study groups and case rooms, a second-year MBA named Abhi Gunasekar is putting that philosophy into practice – and, in some cases, building the tools to test it. 

A joint MS/MBA student splitting time between HBS and Harvard’s School of Engineering and Applied Sciences (SEAS), Gunasekar reached out to P&Q after reading the Weiss interview to make the case that the more interesting story isn’t what the administration is planning, but what students are already doing with the AI they’ve been handed.

FROM COMPLIANCE ENGINEERING TO THE FIRST GENAI CHAMPION

Gunasekar was born in Chennai, India, and grew up in Oregon before arriving at HBS. He spent three years at Amazon, first on the compliance side, where he says he re-architected a regulation modeling system handling billions of compliance decisions a second – work that, he says, “had less to do with AI” directly.

His pivot toward AI came after he moved to Amazon’s advertising organization, joining an entrepreneurial team that built a zero-to-one ad product – the kind of impression-serving technology behind ads viewers see during Prime Video’s Thursday Night Football broadcasts. There, he says, “my involvement with the advertising org had a tremendous amount to do with AI. I was nominated by my director to lead the AI adoption efforts within my organization and became the first GenAI champion.” In that role, he ran A/B tests to bring AI into everyday workflows – “starting with writing better decision-making documents all the way to automating code reviews,” he says.

That hands-on experience with rolling out AI at scale, he says, shaped a philosophy he has carried into business school: automation has its place, but judgment does not. “I realized early on that judgment cannot be delegated and must be honed through manual practice,” he says.

BUILDING AN AI STUDY BOT – WITHOUT GIVING AWAY THE ANSWERS

Inside the classroom, Gunasekar is working with Professor Malcolm Baker, former head of HBS’s finance faculty, on a project to rethink how students prepare for finance cases. “We’re currently trying to revamp how students prep for cases in finance,” he says. “I built an AI-native study bot that helps students leverage AI to ask questions about core corporate finance topics without giving away the answer.”

The bot’s real value, he argues, is on the instructor side. 

“Once the student is done with the preparation, the instructors get a feedback report for all of the 100 students or so in the section, and they will know which topics to focus on to strengthen the understanding of the entire section,” he says. “Think of this as your Socratic companion to build a solid financial foundation as an aspiring executive.” It’s a granular, section-by-section version of the same instinct Weiss described at the faculty level: using AI to sharpen judgment rather than replace it.

Gunasekar says he never feeds his own case write-ups to AI models, “because I believe the case method is still really powerful.” He compares the discipline to Mr. Miyagi’s instruction in “The Karate Kid”: “Wax on, wax off,” he says. “That’s my philosophy of learning, when it comes to AI.” It’s a student-level echo of Weiss’s own language about training “the human model” alongside the AI models – for Gunasekar, the case method is where that training actually happens.

A 3-PART FRAMEWORK: INTENTIONALITY, INITIATIVE, INQUISITIVENESS

Gunasekar says he has three principles for AI use that he calls the “3 I’s”: intentionality, initiative, and inquisitiveness: “the fundamental vectors,” he says, “which I believe I should not hand over to AI.” Before prompting a tool, he sets the intention first, “making sure what the end objective is when I’m prompting AI to do some background research,” then drives the direction of his own research and stays curious about what the tool surfaces rather than accepting it at face value.

He points to a research tangent from over the summer as an example. Curious how industries have evolved across different eras, he prompted an AI tool with questions about the Gilded Age: “How exactly did Andrew Carnegie build his fortune? How exactly did John D. Rockefeller build Standard Oil? Could you give me tangible examples, and are there mental models that we can apply?” One answer stuck with him – a mental model borrowed from ecology. 

“Some companies thrive, some companies don’t thrive,” he says. “Why is that? It comes from ecology” – natural selection, applied to markets instead of species.

FROM MANAGEMENT TO LEADERSHIP TO ENTREPRENEURSHIP

Gunasekar argues that business education has moved through distinct eras since HBS was founded in 1908. 

“If you look at the arc of business school education, it starts with management, moves to leadership, now we’re heading towards entrepreneurship,” he says, as AI erodes the traditional boundaries of what a company even looks like. “What we have today is the traditional notion of a company slowly disappearing. We have one-person companies being run, one-person unicorns being run.”

Even so, he says the core mission hasn’t changed. He cites HBS Dean Srikant Datar’s framing that the school’s focus isn’t judgment or AI, but “AI and judgment” together. 

“That is where the school’s focus is,” he says, “and where I think future up-and-coming students should focus, is how do we have command of the technology, and how do we empower others through our leadership?” 

It’s a sentiment that lines up closely with Weiss’s own summary of where the school stands: as Weiss put it, HBS and its students are “all navigating these waters together.”

Asked what he sees from classmates that the administration’s own messaging isn’t capturing, Gunasekar points to where he thinks change actually originates. “Innovation rarely comes from top-down leadership,” he says. “I fundamentally believe that. Innovation comes bottom up, which in this case are going to be students.” He describes the shift as seismic – “10X, if not more, impactful” than prior industrial revolutions, in his view – which is why he argues the curriculum question isn’t how to read a balance sheet, but “how do you cope in a world that’s going to be unprecedented in change.”

Asked about the mood on campus, Gunasekar describes something close to a split. “On one hand, you’ve got founders who are extremely excited about AI, helping them build companies, and a lot of venture capital going into the space,” he says. “On the other hand, you’ve got people who are not as entrepreneurial, who want to stay closer to traditional roles, feeling a little bit scared – and rightfully so, on how these roles are going to evolve. There’s unfortunately no middle ground.”

‘TRANSFORM INSTITUTIONS THAT CREATE IMPACT’

Gunasekar says his long-term goal is to become CEO of what he calls “a formidable company,” in service of what he describes as his broader mission: “to transform institutions that create impact in the universe.” He’s pursuing coursework in data science and AI at SEAS, he says, “to evolve my strategic acumen by learning how to ask better questions to find the right problems to tackle to unlock enterprise value as an executive” – rather than to become a technologist himself. “True transformation cannot exist without harnessing technology in a thoughtful way,” he says.

His advice to incoming students leans deliberately old-fashioned. 

“I think the old-school virtues still work,” he says. “The golden rule of life is you only get success when you deserve it, and that’s not going to change in the age of AI. Agency still matters. Empowerment still matters. Hard work still matters. Empathy still matters. Reliability is going to be even more important – showing up on time, showing up prepared, making the other person feel heard.

“If you keep doing that, regardless of how education evolves, regardless of how industries evolve, there’ll definitely be a spot for you.”

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