Unlearning the Lab: From Research to Revenue in Deep-Tech

Building a working prototype is just the first step. For many Indian deep-tech founders – whether in AI, life sciences, robotics, advanced materials, or defense & aerospace – months, even years, get poured into R&D. Yet, they stall when it’s time to sell. Why? They cling to lab habits. To truly win in the market, you must unlearn your old ways. Here are three crucial shifts that will speed you from a brilliant idea to your first ₹10 Lakh pilot.

I’ll never forget the day I demoed our AI model to a government lab. After a decade in R&D and product management, I was bursting with pride—95 percent accuracy felt like a monumental win. But the officials barely looked up. My heart sank. In that moment, I had an epiphany: accuracy was meaningless if it didn’t solve their real pain. So, I swapped my intricate code for simple questions: “What slows you down?” Two weeks later, I delivered a basic scheduling tool. They ran it on live data—and it transformed their world.

That lesson taught me something profound. In deep tech—whether you’re fine-tuning machine-learning models, developing novel biomaterials, calibrating robotic arms, or engineering defense sensors—success isn’t about perfect metrics. It's about solving urgent problems. To do that, you must unlearn three core lab habits.

Shift 1: Cultivate Cognitive Flexibility

In research, you follow proven, often linear, steps: tweak algorithms, refine assays, test material blends to perfection. You prize precision above all. In the market, you need speed. You must test ideas, learn rapidly, and be willing to pivot your direction. Psychologists call the tendency to stick with familiar solutions the Einstellung effect. It’s a common trap for experts: a data scientist insisting on an overly complex model, a materials engineer unwilling to simplify a process.

You can break free with one powerful habit. Every Friday afternoon, hold a “Question Everything” session with your core team. List your top two assumptions—like “Our sensor must measure at micrometer resolution” or “This AI model must require a GPU.” Then, pick one and design a quick test: a simple mockup, a two-minute survey, or a simplified lab trial. The goal here is learning, not perfection. Over weeks, these small experiments add up. You'll spot faster paths to value and avoid months of wasted work.

Shift 2: Embrace Customer-First Discovery

In labs across Bengaluru, Delhi, and Hyderabad—whether in biotech, robotics, or advanced materials—success is often measured by experiments and papers. In product management, success is measured by adoption and revenue. If you never talk to the people who will actually use or pay for your tech, it’s destined to stay on the shelf.

Before writing another line of code or running another experiment, conduct five “Problem-Only” Interviews. Pick potential users across your space—an operations manager at a factory, a clinician in a hospital, a research scientist in aerospace. The key is not to demo your product. Just ask:

• What is your biggest pain today?

• How does it affect your work?

• What would you pay to solve it?
  

You’ll learn more in two hours than in two months of pure R&D. Then, reshape your roadmap: focus relentlessly on features that address real pain, and be disciplined enough to drop or defer the rest.

I once spent weeks refining a feature in our analytics dashboard—perfect graphs, slick filters, all the bells and whistles. Then, in a user feedback session, a product manager simply asked, “Can I set an email alert when this metric jumps?” I felt a sting of embarrassment. All that work, and I hadn’t solved their real, urgent need. So I shelved the fancy visuals and built a one-click alert instead. Within days, adoption shot up—and users told me it was exactly what they needed. That taught me that empathy drives traction far more than technical polish.

Shift 3: Invite Stakeholder Challenge into Your Process

In my product management career, I’ve run dozens of steering-committee meetings and internal reviews. Too often, teams treat these as mere status updates—slide decks full of features and timelines. Real value comes when you use these meetings to challenge assumptions. Fresh eyes on your plan force you to unlearn unhelpful habits.

In your next review, start not with features but with an “Assumption Audit.” List your top three hypotheses. Share what you learned since the last meeting. Propose concrete tests for the next quarter. Whether you’re building a defense-grade sensor, an advanced composite, or a bioinformatics platform, this practice keeps you honest and agile.

I’ll never forget one product-review session early in my career. I walked in with my “perfect” roadmap for a materials testbed. My director asked two simple questions: “What’s the riskiest part of this plan?” and “How will you know if you’re wrong?” I froze. It dawned on me I’d built features without checking my own biases. After that meeting, I created a one-page “assumption log.” At our next session, I presented real data on what we tested and what we retired. The difference was dramatic: we cut six weeks of work and focused only on high-value tests.

These three shifts—cognitive flexibility, customer-first discovery, and stakeholder-led unlearning—are the heart of a commercialization mindset. Without them, every feature you build remains untested, every pilot theoretical, and every investor deck looks more like a research paper than a compelling business case.

Your Unlearning Checklist:

• Friday “Question Everything” Ritual: Challenge one core assumption with a quick test each week.

• Five Problem-Only Interviews: Run them before writing any more code or running another experiment.

• Assumption Audit: Lead your next review by discussing risks and hypotheses, not just features.

  
  

Unlearning is not failure. It’s the fastest path to market traction. It’s the mindset that separates successful deep-tech products from shelved prototypes. It’s how you turn years of R&D and product experience into real, tangible impact. So, step out of the lab. Pick up the phone. Ask your users the hard questions. Then, watch the traction follow.