Global buyers care less about where SaaS is built, more about what it delivers: SmartWinnr CEO

AI is quickly transforming how businesses enable their sales teams, though SmartWinnr's Anindita Banik believes the future will focus more on tangible results than just AI implementation. In a conversation with ET Digital, Banik suggests that as regulated industries like pharma, healthcare, banking, and insurance shift from traditional training to ongoing coaching, role-play, and sales readiness, AI has the potential to grow the market while simultaneously eliminating products that simply apply an AI layer to current processes. With SmartWinnr now serving more than 400,000 enterprise users, she also sees India’s SaaS story moving beyond its services legacy, with global buyers increasingly judging Indian products on technology, security and outcomes rather than their origin. Edited excerpts.

Economic Times (ET): Global sales enablement has grown fast on the back of AI, yet enterprise software as a whole was repriced hard in early 2026. Where is this industry heading over the next two to three years, and is the AI wave expanding the market or thinning the field?

Anindita Banik (AB): The next two to three years will be decided by what enablement delivers, not by how big the category looks on a slide. The work is shifting from content delivery and training completion towards continuous readiness, coaching and performance you can actually measure, and that is where AI genuinely expands the opportunity.


It will also sort the field, and I see that as healthy. A lot of products are essentially an AI layer sitting on top of an existing workflow, and buyers see through that quickly. The platforms that last will be the ones that tie AI to a real business problem and can show the outcome it produced.

For us, that means moving enterprises from training their people to knowing whether their teams are ready for the conversations they will have in the field. AI role plays, personalised coaching, and real-time feedback make that possible at a scale that was hard to reach before. AI is expanding the market and raising the bar for what a platform has to prove, at the same time.

ET: There is a debate that AI agents will replace what software used to sell as per-seat licences. For enablement specifically, does that shrink the category or reshape it into something larger?

AB: I see it as a reshaping, and one that makes the category larger rather than smaller. As AI takes on administrative and repetitive work, the value of enablement moves closer to the moment where performance actually happens.

The question stops being how many people hold a licence. It becomes how many customer-facing conversations we improved, and what that changed for the business. AI can simulate a conversation, assess the response, spot the knowledge gap and tell a rep exactly what to practise next. That turns enablement into something continuous and personal.

Enablement stops being a library people log into and becomes an intelligence and coaching layer around the workforce. That is a far bigger opportunity than seat-based training software ever was.

ET: Regulated industries like pharma, medical devices, and financial services were historically the slowest to adopt AI in customer-facing work. Has that reluctance finally broken, and what shifted in the buyer's mind to unlock the budget?

AB: The reluctance has eased, though I would describe what is happening as responsible adoption rather than simply faster adoption.

A few years ago, the first question from a regulated enterprise was whether they could use AI at all. Today, it is how they can use it safely, and what controls need to sit around it. That is a far more productive place to start.

What has changed is the recognition that AI can strengthen compliance when it is built into the workflow rather than bolted on. In enablement, a rep can practice a difficult conversation in a controlled environment, get immediate feedback and be assessed against approved messaging before they ever sit in front of a customer. The budget opens up when AI stops being an experiment and starts solving a measurable problem, whether that is faster onboarding, stronger launch readiness, consistent messaging, or more coaching capacity than a manager can provide alone. Those are outcomes a buyer can defend internally.

ET: As buyers move off per-seat pricing towards usage and outcomes, how is the economics of enterprise enablement changing, and who wins as that shift plays out?

AB: The economics are moving from access to value. A seat-based model measures how many people can open the software. An outcome-based model asks what changed because they used it, and that is a much healthier question for the enterprise to hold a vendor to.

If an AI coaching platform shortens onboarding, lifts field readiness, or gives managers more coaching capacity, the customer should be able to connect the spend directly to those results. For vendors, it means you can no longer hide behind adoption numbers. You have to show that the product changed behaviour and performance.

The winners will be the platforms that pair genuine usage with measurable business impact. AI makes that more achievable because we can now capture far richer signals around practice, readiness, knowledge gaps, and performance than a seat licence ever revealed.

ET: India is being recast from a services back office into a genuine product nation in SaaS. How real is that shift from where you sit, and does the India-origin label still carry a discount with global enterprise buyers?

AB: The shift is real, and we have lived it. India has moved well past being only a services and engineering destination. There is now a generation of companies building products for global customers from the first day.

At SmartWinnr, the conversations we have with large global enterprises in life sciences, healthcare, banking, and insurance are about product quality, security, scalability, and business outcomes, not about where the engineering team sits. The platform is used by over 400,000 enterprise users today.

Enterprise buyers are demanding, and every SaaS company still has to earn trust. But the inherent discount has gone. If anything, the combination of strong engineering talent, sharp product thinking, and cost-efficient innovation is becoming an advantage. The label matters far less than whether you can build enterprise-grade technology and stand behind customers anywhere in the world.

ET: You have built a business selling into some of the most heavily regulated fields in the world. How large is that opportunity as you see it today and is the growth coming from new enterprises or from going deeper inside existing ones?

AB: The opportunity is large because the underlying problem shows up in every organisation with a distributed, customer-facing workforce where knowledge, compliance, and execution all matter.

Life sciences are the clearest example. A pharmaceutical or medical-device company can have thousands of representatives who need to stay current on products, clinical information, policy and approved messaging, all at once. The same problem exists in banking, insurance, and other regulated sectors.

Growth is coming from both directions. We are adding new enterprise customers, and there is room to go deeper inside the ones we already serve as they move from a single use case to many. A customer often starts with training or gamification and then expands into AI role plays, coaching, certification, onboarding, and launch readiness. That expansion matters because once the platform becomes part of an organisation's operating rhythm, the value compounds.

ET: Compliance is often described as a burden. For SmartWinnr, is the regulatory complexity of your customers actually the moat that keeps horizontal enablement players from following you upmarket?

AB: Yes, though I would frame it as a moat and a responsibility together. Building for regulated industries is not about adding a compliance checklist. You have to understand how these organisations actually operate: how information gets approved, how customer-facing teams are trained, and how consistency is held across a large field force. That understanding is hard to acquire, and it creates a real barrier to entry.

A horizontal platform can add an AI roleplay feature fairly quickly. Building something that works reliably inside the requirements of pharma, medical devices, or financial services takes domain knowledge, enterprise-grade infrastructure, and years of learning alongside customers. We have deliberately built compliance into the product rather than treating it as an extra layer, and that becomes more valuable as AI moves closer to the customer conversation.

It is precisely because the complexity is hard that solving it well becomes a durable advantage.

ET: What are your revenues, how many clients do you work with, and what is your growth rate?

AB: We are a privately held company, so we do not disclose revenue or detailed growth figures publicly.

What we can talk about is the scale and depth of adoption. The platform is used by over 400,000 enterprise users, across leading organisations in life sciences, medical devices, banking and insurance. Our focus has been on building long-term enterprise relationships and expanding within those accounts as customers adopt more of the platform, from learning and gamification through to AI roleplays, coaching and sales readiness.