Our every engineer must be GenAI-enabled by year-end, it’s now a mandate: Biju Davis, Model N

As GCCs in India evolve from cost centres to core innovation hubs, Model N’s Hyderabad facility stands out as a case study in transformation. In an exclusive conversation, Biju Davis, SVP of Engineering at Model N, walks us through the company’s India journey, from a BOT-model setup to a global engineering powerhouse driving product ownership, AI integration, and SaaS transformation. With nearly half the company’s workforce now based in India, Davis shares insights on how generative AI is redefining engineering productivity, why domain knowledge matters more than ever, and how India’s tier-2 cities are shaping the future of global R&D.

Model N has significantly expanded its footprint in India with a dedicated product innovation facility, positioning it as a central hub for global engineering. Could you walk us through the journey of establishing and scaling Model N’s Hyderabad centre? What were the key objectives at its inception, and how has its mandate evolved to support global R&D, revenue management, and analytics?

Model N has had a presence in India for 15 years, beginning with a classic build-operate-transfer (BOT) model in partnership with Sierra Atlantic. Sierra Atlantic helped set up the initial operations and managed early hiring, with support from senior colleagues from our headquarters. I’ve been with Model N for five years now, and during this time, I’ve seen the Hyderabad centre grow into a core hub for global engineering.

Unlike many companies that start with support functions, we focused on product engineering and professional services from the beginning, developing our core products and implementing them for customers. While the initial headcount was likely under 100, growth was steady and organic.

In 2017, we acquired Revitas, a competitor with a small office in Ahmedabad. While we still support both platforms, Ahmedabad remains a lean centre with around 25 people, and Hyderabad has always been our primary engine of growth.

Until 2020, we had around 300 employees in India. Over the last three to four years, we’ve doubled that number, largely driven by our strategic shift from on-premise software to SaaS. I joined during this scale-up phase to lead core engineering, cloud operations, and cloud engineering. That SaaS transformation is now nearly complete.

Today, we have over 530 employees in India, nearly half of our global workforce. About 70% of our engineers are based in Hyderabad, where they have full product ownership. After outgrowing our earlier space, we moved into a new facility in 2021 with over 500-seat capacity. From a BOT setup to a strategic innovation hub, it’s been a remarkable journey.

Industry insights highlight how GCCs in India are transitioning into AI-first, automation-driven innovation labs and hubs. How are you integrating new technologies like AI, ML, and automation into your engineering workflows, especially around revenue management solutions? What outcomes and efficiencies have emerged?

As you know, AI is everywhere now, it’s become a major buzzword. There are two sides to it, some are genuinely exploring its potential, while others are driven by FOMO. At Model N, we started experimenting with AI well before the current wave of GenAI hype.

For nearly a year, we’ve been running multiple POCs and have taken a two-pronged approach. First, we’re focused on improving organisational productivity by using tools like GitHub Copilot. This isn’t about reducing headcount, it’s about enhancing efficiency. Even a 10% improvement means we can get more done with the same team. This approach has been working well and is now a company-wide mandate across all Vista portfolio companies. Our CEO is a strong advocate of GenAI and has mandated that every engineering developer be GenAI-enabled by the end of this year. We’ve already rolled out licenses and are actively tracking efficiency gains.

Second, we’re investing in innovation, specifically, how AI can enhance our products and deliver value to customers. We’ve identified several use cases, especially in support functions, and are now beginning to productise a few of them. Given the volume of data we handle, we’re leveraging AI to extract deeper intelligence and embed it into revenue cycle management and government pricing solutions. The early results are promising, and traction is building.

Organisations in regulated sectors, which deal with people’s or customer data, are often skeptical about incorporating AI into their workforce initially. Where does Model N stand on this front?

At Model N, security is the top priority when it comes to incorporating AI. Before using any tool, it must go through a thorough vetting process by our security team. Only approved tools are brought in-house. We do not expose our data to public tools. ChatGPT, for instance, is not a preferred tool internally, and employees are discouraged from using it to share any Model N information. While it may be used for general research or content generation, we are not building our own LLMs at this point. Instead, we are exploring secure, vetted third-party tools.

For example, our customers’ contracts can be extremely complex, often running into tens of thousands of lines. These need to be managed carefully to prevent over-discounting. Since revenue leakage in the life sciences industry is around 4-5%, close to a trillion dollars globally, even saving 0.1% is significant. We’ve recently run a POC with a third-party tool (security-approved) to extract data from encrypted PDFs, enabling faster and more accurate contract analysis.

In another case, we’re applying AI tools in pricing, especially government pricing. During COVID, for instance, vaccine pricing varied, and programs like Medicaid required special handling. We’re exploring how tools can help analyse such data to ensure efficient, compliant pricing.

You mentioned your aspiration for all employees to be GenAI-friendly and fully efficient with Copilot and similar tools. Once they acquire these GenAI skills, how do you foresee their productivity increasing, and what new use cases might emerge?

Productivity, in this context, largely comes down to prompt engineering. knowing how to effectively prompt GenAI tools for content creation or engineering tasks. Right now, every developer and engineer is encouraged to use Copilot. Our expectation is that before writing any code manually, they should first prompt the system to generate it, and then review and refine it as needed.

There are two key benefits here. First, it leads to more efficient code. When ten people write the same logic, you often get ten different outputs with varying levels of efficiency. With GenAI, the generated code tends to be more standardised and optimised, thanks to the maturity of the models.

Second, while GenAI excels in new development, it’s still less effective when it comes to legacy systems, mainly because we haven’t exposed the full codebase to the AI yet. So while new builds benefit greatly, legacy code requires more manual refinement and integration.

Our goal is to get everyone to start using it regularly. We’ve already seen encouraging adoption, around 40% of developers are using it daily since the rollout two to three months ago. That said, not everyone writes code every day, many are involved in analysis or bug fixes, but the overall engagement has been strong.

So developers believe that in the coming few years the role of developers will diminish due to the incorporation of AI. Do you agree with this, and what kind of impact do you foresee on both developers and testers?

I don’t believe the role of developers or testers will diminish, but it will definitely evolve. We’ve already seen this happen in the testing world, where manual testing shifted to automation. People didn’t lose jobs; they became automation testing experts. The work changed from writing detailed test scripts and documents to coding and building automation frameworks using tools like Selenium. The core function, testing, remained, but the method changed.

At our end, for example, we use a homegrown tool called RTS for our products. Automation testers now write scripts and templates, run tests, and analyse results within that system. The same kind of evolution is coming for developers. Prompt engineering will become a key skill, but more than that, domain knowledge will grow in importance. Engineers will need to deeply understand the business context and apply AI tools accordingly.

Model N has embraced talent beyond metro areas, leveraging Hyderabad’s talent pool and using a “micro-GCC” and tier-2 city strategy for resilience. What is your approach to sourcing, upskilling, and retaining engineering talent in Hyderabad and emerging tier-2 cities? How do you maintain operational excellence and culture across these locations?

COVID completely transformed how GCCs and IT companies operate. Before the pandemic, remote work in India was rare, rather seen as a privilege. I remember we had policies where only after five years with the company could someone work remotely once or twice a month. That changed overnight.

With remote work becoming the norm, our approach to hiring also evolved, we were no longer limited by geography. During COVID, we hired aggressively from across the country. In fact, I was one of the first to join Model N remotely in April 2020 from Bengaluru and worked remotely for nearly a year and a half.

Once our Hyderabad facility was ready, we encouraged a return to the office in pockets. Those nearby started coming in a couple of days a week. We didn’t change policies, we continue to follow a hybrid model. Remote employees also come in once a quarter, often aligning their visit with leadership presence.

Talent attraction and retention have been strong. We’ve retained several benefits introduced during COVID, unlike many others. For instance, we added six “wellness days” to the usual leave. If a public holiday falls on a Friday, the following Monday is also off, creating a long weekend. We also continue with our “wellness week off” around July 4th, where the entire company shuts down.

Speaking of culture, do you think the exponential growth of GCCs in the Indian market over the past five to six years is also bringing about a culture change in the way people work?

It is indeed. Initially, every GCC tries to understand the local flavour of the company culture, often borrowed from their home country. While Indian tech stalwarts like Infosys and TCS set certain benchmarks, every GCC has started implementing its own variant of the culture. A new GCC will likely aim for common benefits and culture across the company, trying to apply what works in the US to India, then gradually tweaking it to be more localised. They also have other GCCs to borrow from and share best practices.

You might observe a significant difference between typical IT services companies and GCCs. Each GCC has the flexibility to introduce unique benefits. There isn’t a single format expected across all these companies, so it’s beneficial as each offers its own advantages.

Right now we’ve about 1,700 GCCs set up in India, employing over 1.9 million people. From your perspective as an engineer who started 20-25 years ago, when India was primarily the “BPO of the world,” how do you perceive this shift to an innovation hub, and how are we preparing for the next wave of challenges, such as agentic AI?

The shift we’ve seen in India, from being the “BPO of the world” to an innovation hub, is the result of decades of talent maturation. People like me, who entered the industry 20–25 years ago, have witnessed this transformation firsthand. We started with a service-oriented approach, but over time, strong, product-focused teams began to emerge, both within product companies and among those in traditional IT services.

We owe a lot to the early IT service giants like Infosys, TCS, Wipro, IBM, Cognizant, and Accenture. Their investments built the foundational talent pool we have today. Initially, GCCs relied on these firms for back-office work, but as confidence in local talent grew, the focus shifted to core engineering and product development. Companies like Persistent and Mindtree evolved from pure services to product development outsourcing, further strengthening this trend.

That evolution made GCCs rethink their strategy, why not set up their own centres and build real tech in-house, right here in India. Over the past 25–30 years, we’ve seen Indian IT embrace new technologies like DevOps and cloud rapidly. When AI entered the picture, companies didn’t hesitate, they invested in both technology and talent.

Now, we’re heading into the next wave, GenAI and Agentic AI. And just like before, we’re ready. While cutting-edge R&D in GenAI might still be driven by smaller, focused global teams, India will continue to play a major role in scaling, adoption, and innovation across industries.