From AI Policy to Classroom: Real Challenge is Implementation

The Transformation I am Watching Unfold

I have spent more than 27 years around healthcare, first as a research scientist, then inside an investment bank building a life sciences practice, and for the last decade and a half as an investor and venture builder working with early-stage Healthcare and MedTech companies

In that time, I have watched several waves reshape the sector, from genomics to digital health and telemedicine. None moved as fast, or touched as much of the business at once, as this one. The bigger shift is not simply that AI can do more work; it is that information is becoming cheaper and faster, which makes human judgment more valuable.

At BIORx Ventures (Investment Banking Firm), Indian Healthcare Angels (Angel Network), and VEOCAP Ventures (Family Office of Dr. Vishal Gandhi), AI is no longer a topic we discuss with founders. It has become part of how we work. It shapes how we find startups, how we evaluate them, how we support them after we invest, and increasingly, what the startups themselves are building.

Where AI is Already at Work

The most visible change is in deal sourcing and screening. A few years ago, spotting a promising diagnostic or Medtech Startup meant relying heavily on personal networks and industry events. Today, AI-assisted market intelligence tools help us scan patent filings, clinical trial registries, and funding databases to surface companies earlier, often before they have raised their first institutional round. In practical terms, an analyst can start with hundreds of companies, patents, or clinical programmes and use AI to narrow that universe to the opportunities that deserve deeper human attention.

Due diligence has changed just as much. Building a SWOT analysis or drafting a first-pass investment note used to take an analyst several days. AI now handles much of that groundwork, freeing our team to spend more time on what it cannot do: sitting across the table from a founder, visiting a manufacturing site, or stress-testing a clinical claim with an independent expert.

Regulatory and clinical intelligence is another area where AI is proving genuinely useful. Healthcare in India operates within evolving regulatory and R&D frameworks, including CDSCO requirements, device classifications, and DSIR-related processes. AI tools now help our team and the founders we mentor get a faster, more reliable first read on where a product sits in that maze.

And, of course, the startups themselves are changing. A growing share of the companies we evaluate are building AI directly into diagnostics, remote patient monitoring, and pulse or vitals-based screening tools. We are not just using AI to invest in healthcare; we are increasingly investing in AI that is reshaping healthcare delivery itself.

Roles in Motion

The analyst role I hired for a decade ago looks different from the one I hire for today. We are also seeing hybrid roles emerge around AI-enabled investment analysis, healthcare AI product development, clinical intelligence, and AI governance and risk. It used to reward people who could gather information quickly and accurately. It now rewards people who can ask better questions of the information AI gathers for them, and who have the judgment to know when an AI-generated answer does not hold up against ground reality.

That shift has not made junior talent less important. If anything, it has raised the bar. A young analyst today is expected to be comfortable with AI tools from their first week, but tool fluency alone does not make someone good at this job. The ability to read a founder's character in a room, to negotiate a term sheet, and to know when a confident-sounding AI output is quietly wrong are still deeply human skills, and they are the ones that separate a good investment professional from an average one.

My Advice to Students

Do not wait for a classroom to teach you AI properly. Pick a real problem, even a small one, and use these tools to work through it end to end. If you want to break into healthcare investing or venture building, build something you can show: a mini due diligence note on a public company, a market map of a subsector you find interesting, a short analysis of a recent healthcare policy change.

Pair that with the fundamentals, accounting, basic regulatory knowledge, and the ability to write and speak clearly, and you will stand out far more than someone who only knows how to use the latest tool. The goal is not to become a tool expert; it is to become someone who can use AI to solve a real problem better and faster.

Also, get close to people doing this work. Internships and Mentorship conversations remain the fastest way to understand how theory meets practice, and most people in this ecosystem are more willing to help than students expect.

Looking Ahead: The Next Three to Five Years

I do not believe AI will replace investment judgment, and I say that as someone who uses these tools every day. What I do believe is that firms and professionals who fail to build AI into how they source, diligence, and support startups will simply move slower than those who do, and in venture building, speed compounds

Over the next three to five years, expect AI co-pilots to become a standard part of every analyst's toolkit, wider and faster screening of early-stage healthcare startups, and more companies reaching seed and Series A stage with stronger evidence behind them. What will not change is what ultimately closes a deal: trust between a founder and an investor, built through honest conversation, careful diligence, and time spent together, not through algorithms alone.

For students entering this field, that is the balance worth mastering: use every tool available to move fast, but never let the tools replace the judgment, curiosity, domain expertise, and integrity that this industry, and healthcare itself, ultimately depends on.