New Delhi, July 4 -- India's enterprise tech landscape is evolving. With core functions becoming more digitised and the value of software increasingly visible, especially in sectors like banking, financial services and insurance (BFSI) and transit, the market is shifting from cost-first thinking to long-term value focus. In a conversation with TechCircle, Ashish Rai, Group CEO of Aurionpro, shares his views on what's driving this change, how Artificial Intelligence (AI) and public digital infrastructure are reshaping enterprise priorities, and what it means for the future of Indian software on the global stage. Edited Excerpts:
Aurionpro recently reported its FY24 results. What drove demand in the Indian market, and which business lines performed above or below your expectations?
Over the past four years, we've consistently grown at over 30% annually. This growth is largely driven by demand in areas undergoing structural changes. Many of our product lines are seeing strong demand due to shifts in these segments. A key factor has been entering these areas with products that are more effective than what existed before, aiming to deliver measurable results for clients. The growth comes from two main factors: first, a favorable demand environment; and second, stronger market performance, including higher win rates compared to peers serving similar clients. Four years ago, we began shifting focus toward building global products and platforms in specific sectors like banking, payments, and transit.
Is your growth in India driven more by government contracts, BFSI, or smart mobility? And what structural challenges are you facing as you scale?
We're seeing growing demand across all the segments we operate in. In banking, demand is driven by digital transformation and the push to adopt best practices, especially in core areas like transaction banking and lending. AI adoption is also contributing to this trend. In the government sector, there is strong demand for digital transformation, particularly around smart city initiatives and transit payments. Transit payments are a major focus area for us in India. India is rapidly building out one of the world's largest metro networks. This is creating demand both for new transit lines and for upgrading existing ones. We're also active in data center consulting, including design and program management, often in partnership with strategic collaborators. Demand in this area is also strong.
Where do you see generative AI and other technologies heading in the BFSI sector, given its low tolerance for error?
The BFSI sector has been an early adopter of AI technologies, not just generative AI. Banks have been using specialized machine learning models for years, primarily for predictive use cases. That work continues. Last year, we acquired aria.ai, an AI startup focused exclusively on banking and financial services. The company was founded 12 years ago by Vinay and Dixit. For over a decade, we've been offering AI solutions tailored to the BFSI sector, supporting enterprise AI adoption and working closely with multiple banks. We see demand in two areas: integrating intelligence within application stacks we provide, and helping banks define and implement broader enterprise AI strategies. In both cases, the goal is to apply AI where it adds value, whether in workflows, decision-making, checks, or transaction processing. Generative AI is only one part of this; many use cases involve other forms of intelligence. Model development has become relatively easy. The harder part is moving from model to production, especially in regulated industries like banking. That's where we focus, on AI interpretability, explainability, and safety. We operate one of the leading labs globally for research in AI explainability, aiming to build production-ready frameworks that meet regulatory requirements. Our work helps ensure that models are not just accurate but also explainable, compliant, and safe for real-world deployment.
What's your India roadmap for the next financial year? Any new verticals, city rollouts, or target customer segments?
We will continue working with the banking sector in India, focusing on advanced applications in transaction banking. Last year, we secured deals with both public and private sector banks, including State Bank of India, to roll out the next generation of our products. This work will continue. We will also stay active in the transit payments space, working with our existing partners and new entrants across the country. We offer products that cover the full transit payments value chain-both hardware and software, developed and manufactured in India. We plan to maintain support for current clients and pursue new business in this area. We are also investing in data centre infrastructure in India. Although data centre growth has accelerated, it still falls short of the country's needs. We aim to continue investing and partnering with strategic clients on complex design and implementation projects. In addition, we will increase investment in AI. We operate one of the main AI explainability labs in India and are focusing on AI safety, interpretability, and explainability. We have developed new algorithms, which we are open-sourcing, and are building a developer community around our RAX AI toolsets. This includes both applied AI in sectors like banking and government, and ongoing research work.
How is the Indian enterprise tech buyer evolving in 2025, more outcome-driven, risk-averse, or platform-focused?
Enterprise tech buying is shifting in response to AI, digital transformation, and changes in economic models. Spending is increasingly focused on outcome-driven initiatives, particularly in AI and digital transformation, rather than on routine operational projects. A significant portion of spending is moving toward these types of projects. Another trend is the shift from legacy or custom-built software, especially in banks, toward standardized enterprise software that reflects industry best practices. This shift is evident in areas like transaction banking, where banks are replacing custom solutions with more mature products. We are seeing increased participation in RFPs related to this transition. Government spending on digital transformation projects is also rising and appears to be accelerating. The tech buying process itself is becoming more structured. Organizations are conducting more detailed evaluations, often with third-party consultants, to define future-state requirements and assess how well new technologies align with those goals. This process has become more mature over time and is now quite established.
Which emerging technologies are you actively building into your roadmap, and which are still in "watch and wait" mode?
AI is a primary focus of our investments. We're working on both integrating AI into our existing application stack and identifying the frameworks that will support AI adoption across the enterprise. We're also continuing to build on existing digital technologies. Open loop transit payments remain a major area of investment. We're developing end-to-end capabilities across hardware, embedded software, and application software. We've developed our own EMV-certified card readers, and we're among the first in India to do so. We're also investing significantly in hardware and Internet of Things (IoT). Where IoT connects with AI, that's another area of focused investment. In parallel, we're working on new data center products currently in R&D, with plans to roll them out gradually through the year. In terms of areas still under evaluation, we're not simply waiting-we're running pilots and experiments. One area is AI explainability. We're conducting research, primarily in our labs in India and some international locations, with the aim of making progress over the next 12-24 months. Another area is LLM-based, vertical-specific applications in banking. Currently, AI is being embedded into existing workflows, but we expect full-scale AI-native applications-such as agent-based orchestration systems or other targeted solutions-to emerge over the next 1-2 years, depending on how the technology matures.
What's one India-specific risk that concerns you in the next 12-18 months, and one opportunity that could significantly grow your business?
The main risk for Indian tech today isn't about doing business in India, it's the lack of a strong sovereign LLM. Current efforts to build one are limited and not progressing as needed. Over time, this could be a significant problem for India's tech competitiveness. Globally, we're likely to see four to six major LLMs emerge. India doesn't currently have one, and there's no serious push to create one. This gap could grow into a major weakness. On the opportunity side, AI is driving big changes. While we usually look at the banking sector, this shift applies across most enterprises. India has not historically played a major role in enterprise software, but that may be changing. Traditionally, enterprise software, whether banking systems, ERP, or others, has been hard to replace. The cost and complexity of switching has outweighed the benefits, so legacy systems have stayed in place for decades. But AI is changing that. The value of switching is starting to exceed the pain of the switch. This opens the door for newer enterprise software companies from India, such as Orion Pro and others, to build the next generation of applications designed for the AI era. The opportunity isn't limited to India, it's global. India is well-positioned to take advantage of this shift and move up the ranks in enterprise tech quickly.
Is there a risk that Indian enterprise buyers focus too much on cost and overlook long-term value, especially in critical infrastructure tech?
I think it's possible, but what actually happens is that the value of enterprise technology becomes clearer every year. When the value created by core business applications is obvious, it also becomes easier for software vendors to capture that value. India will likely remain a cost-conscious market compared to others of similar size, at least for some time. This is partly due to historical market behavior and partly because of the influence of large tech service providers that push pricing down. Enterprise tech adds clear value to an organisation's core functions. The clarity of that value depends on the nature of the business. For example, in a bank, lending is core, so the value of software for lending is easy to see. That may not be the case for peripheral or support functions like HR, where the value of software isn't as immediately apparent. Where the business value is clear, typically in core functions, software vendors are more likely to be paid appropriately and capture part of the value they help create. That space is improving steadily. This is also why competition in core functions is fairly balanced. In areas like transaction banking, both global and Indian vendors compete for the same deals, and it's roughly an even split. Price sensitivity is decreasing in core areas, with enterprises more willing to spend. In contrast, for peripheral or support functions where the value is less clear, pricing pressure remains.
Published by HT Digital Content Services with permission from TechCircle.