Fourteen years after engineering school, he turned to economics and business research to bring sharper logic to enterprise AI decisions.
Sarthak Ghosh did not return to school because he needed a new title. He returned because he wanted a better explanation of why organizations choose one path over another. He could already build data analytics AI projects. He wanted to understand how strategy, capital, and risk actually move through a company.
“I wanted the language of decisions,” Ghosh says. “AI lives inside incentives, not inside a lab.”
He began with engineering. He earned a Production Engineering degree at Jadavpur University, in his native India. His undergraduate GPA was 9.19 on a ten-point scale. Numbers came easily. Business context came later.
January 19, 2022 was his reset. He started an MBA after a fourteen-year gap and went straight at the subjects he missed the first time around. Strategy, finance, and economics became the core.
Economics was new territory for him. He earned an A plus. Macroeconomics followed with an A. Then Microeconomics with an A plus. He closed the program at 3.96.
“Economics forced me to name the assumptions,” he says. “Once you name them, you can test them.”
The classroom shift changed how he evaluates technology claims. He started to look at AI proposals the way he looks at an economic model. He asks what the incentive is, who benefits, what gets ignored, and what happens when the assumptions fail. He also asks how leaders will measure success without moving the goalposts.
“Good decisions need a frame,” he says. “Otherwise, people argue about opinions and call it strategy.”
He also learned to translate between technical teams and business leaders. Economics gave him a way to discuss tradeoffs without turning every conversation into ideology. He says that skill is essential when AI touches budgets, staffing, and risk.
He kept going. He began economics coursework at Purdue University as preparation for doctoral candidacy and held a 4.0 across those classes. He also carries a 4.0 in his DBA studies.
Academic performance earned him membership in Beta Gamma Sigma, the business honor society.
He describes studying as a daily habit, not a sprint.
“I do not like vague understanding,” he says. “I like proof.”
Teaching became part of that proof. He has tutored graduate level courses at the University of Illinois Urbana Champaign and at Purdue University. He describes tutoring as a discipline that exposes weak explanations.
“If a student cannot follow me, I did not explain it. That is on me,” he says.
His goal is not only to learn. He wants to contribute through research and education, with public policy and sustainability in view over the long term.
Enterprise work keeps the learning grounded. Ghosh leads enterprise AI architecture and manages a distributed team across geographies. He describes his motivation as a mix of statistics, mathematics, data, analytics, economics, and AI, and he sees those as complementary disciplines rather than competing ones.
He is known at work for asking difficult questions. Co-workers often point to his habit of stopping a meeting to test the logic under the plan. He treats that as part of his job. He thinks AI programs break when teams stop questioning inputs and start trusting momentum.
“Fast plans can hide weak logic,” he says. “The best question is the one that slows a bad plan down.”
He also builds systems with direct operational stakes. His work includes AI applications that support workplace safety through real-time analytics based on large datasets. He cares about what happens after deployment, not only the first demo.
“Safety work does not tolerate guesswork,” he says. “The system has to be dependable.”
His routine is built for momentum. He writes down what matters for the week, then checks progress against that list. He asks for feedback early, not after a milestone slips. He says small corrections protect long projects.
He keeps his own routine simple. He breaks large goals into weekly tasks and asks for feedback often, so improvement stays continuous.
He summarizes his values as truthfulness, honesty, bravery, respect, and love. He also sees learning as a shared responsibility.
“Knowledge has to circulate,” he says. “Otherwise, it does not matter.”
Ghosh’s long view connects two tracks. He plans to launch an AI and data firm. He also intends to stay active in economics scholarship and teaching, with an emphasis on policy and environmental challenges. He wants work that holds up in both rooms, the boardroom and the classroom.
“I am not trying to sound smart,” he says. “I want to build and teach in a way that helps people make better decisions.”
More information is available on his LinkedIn.
