Outlook Interview: SARAS × Amit Kataria

Amit Kataria, Co-Founder of SARAS AI Institute, has spent years at the intersection of enterprise execution and talent strategy—leading HR as CHRO for global IT firms and earning multiple recognitions along the way. Today, he’s channeling that experience into building SARAS: An AI-Exclusive institute, designed to turn learning into role-ready proof.

Q. For readers hearing about SARAS for the first time—what is it, in one line?
A. SARAS is World’ First, AI-Exclusive, Fully Digital ,Degree Approved ,Utah- USA based institute that makes learners role-ready through proof-based learning—real projects, real portfolios, real outcomes.

Q. “AI-first” is a phrase everyone uses. What makes SARAS truly different?
A. Most places teach AI like theory. We teach AI like execution. SARAS is built around evidence—what you built, what you tested, what you can defend. And it’s not “industry-themed”—it’s industry-built, shaped by practitioners from ecosystems like Microsoft, Google, Apple and more. The goal isn’t completion. It’s credibility.

Q. Why did you start SARAS? What personal frustration were you trying to fix?
A. This goes back to my time at Hanu, delivering enterprise work in the Microsoft ecosystem. Every hiring cycle showed the same reality: even strong graduates weren’t ready for real project execution on day one. We invested heavily in closing that gap—workflows, tools, documentation discipline, stakeholder communication, and delivery standards.
That experience shaped a bigger conviction: industry doesn’t need more certificates; it needs capability that shows up in outcomes. SARAS was built to create that capability—whether you’re a student entering the market, a working professional staying future-ready, or an enterprise building AI execution across teams.

Q. What do you mean by “proof-based learning”?
A. If learning doesn’t produce proof, it’s just motivation. Proof-based learning means: don’t tell me you learned—show me what you built. A portfolio that a hiring manager can review quickly and say, “This person can deliver.”

Q. Walk us through what a learner actually builds at SARAS.
A. Real work. Clear problem framing. Data stories. AI-enabled workflows where relevant. Evaluation notes. And outputs that look like something a team would actually use. In short: less “answer sheets,” more “decision packs.”

Q. SARAS works with universities. What’s your approach to partnerships?
A. Fast pilots first. Outcomes decide the next step. Universities don’t need another elective that sounds modern. They need a track that makes students visibly stronger—in portfolio, employability, and execution.

Q. Faculty readiness in AI is a real challenge. How do you address it?
A. We don’t try to replace faculty—we try to enable them and raise outcomes. We bring structured curriculum, rubrics, mentoring support, and a repeatable project system. Faculty gain a framework, students gain proof, and the university gains credibility.

Q. You’ve launched an MS in AI for working professionals. Why now?
A. Because “AI awareness” is over. The workplace needs AI execution. A lot of upskilling optimizes for hours and certificates. Professionals want something else: depth, structure, and outputs they can apply immediately. The MS in AI is built for tangible capability—so you don’t just feel future-ready, you can prove it.

Q. How does SARAS fit enterprise L&D and talent strategy?
A. Enterprises are done with “everyone attended an AI session.” What they need is capability embedded across functions—marketing, finance, HR, ops, product. SARAS fits organizations that want upskilling to show up in the only place that matters: work quality and outcomes, not just training dashboards.

Q. AI literacy vs AI employability—what’s the difference?
A. Literacy is knowing the terms. Employability is being able to ship outcomes responsibly. Literacy gets you interested. Employability gets you hired—and trusted.

Q. Responsible AI is becoming a big theme. How do you approach it?
A. We treat responsibility as a core skill, not a disclaimer. If you can’t evaluate outputs, document assumptions, and handle limitations, you’re not “AI-skilled”—you’re just fast at generating risk.

Q. What’s one unpopular truth about education in the AI era?
A. Content is cheap now. Capability is expensive. The world is not short of courses—it’s short of people who can execute.

Q. What’s your north star metric for SARAS?
A. When a learner graduates, they should have three things: confidence, proof, and credibility. If they can show what they built and defend it, we’ve won.

Q. What should India do urgently to stay ahead in AI talent?
A. Stop treating AI as a topic. Treat it as a capability across disciplines. Move from awareness to application—projects, evaluation, and outcomes at scale.

Q. Finally, what’s the one-line message you want the reader to remember?
A. In the AI era, your edge won’t be what you know—it’ll be what you can build, prove, and deliver.