Vijay Raina is a distinguished expert in enterprise SaaS technology and software architecture, specializing in the strategic intersection of cloud platforms and artificial intelligence. With extensive experience in guiding large-scale digital transformations, he provides critical thought leadership on how legacy software systems can be modernized to thrive in an increasingly automated global economy.
The conversation explores the evolving relationship between Silicon Valley’s AI pioneers and the established powerhouses of the Indian IT sector. We discuss how new service-oriented ventures from major AI players are actually validating the traditional IT model, the necessity of wrapping professional services around raw technology to achieve business outcomes, and why the “last mile” of integration remains the most significant hurdle for enterprise adoption.
OpenAI and Anthropic are creating their own service entities to assist with AI deployment. How does this move validate the traditional “last mile” work of Indian IT firms, and what specific challenges do these AI giants face regarding security reviews and legacy system integration?
The decision by these AI leaders to launch dedicated service arms is a massive vote of confidence for the business model that firms like TCS and Infosys have championed for decades. It signals that even the most advanced AI is not a “plug-and-play” solution; it requires grueling, hands-on labor to bridge the gap between a raw model and a functioning enterprise tool. These AI giants are realizing that the “last mile”—which includes integrating with ancient systems of record and passing rigorous security reviews—is incredibly complex and resource-intensive. While a model can write poetry, it cannot easily navigate the labyrinth of a bank’s legacy architecture or ensure compliance in a highly regulated deployment without deep, manual intervention. This is exactly the kind of heavy lifting Indian IT has perfected over the last 20 years, proving that the human element of technology adoption isn’t disappearing; it’s becoming more essential.
Enterprises often prioritize business outcomes over just having access to new tools. How does wrapping professional services around AI technology change the value proposition for mid-size companies, and what steps should providers take to ensure these tools actually deliver tangible results?
For a mid-size company, a powerful AI model is often just another line item on a budget until it is transformed into a specific business outcome, such as reducing churn or automating a supply chain. Wrapping professional services around the technology turns a raw product into a functional solution, moving the conversation from “what this tool can do” to “what this tool will achieve for you.” To ensure tangible results, providers must act as “accelerants,” moving beyond simple installation to focus on change management and operationalizing the software over time. This involves setting clear benchmarks, such as specific efficiency gains in workflows, and ensuring the AI is deeply integrated into the company’s daily “operating system” rather than sitting on the sidelines as a novelty. Ultimately, clients aren’t buying AI; they are buying the result that the service-wrapped AI promises to deliver.
Standing still is a major risk as AI-native workflows and agentic experiences become the new standard. How can established IT firms redesign their existing products to meet this specific demand, and what metrics should they use to track the success of these updated workflows?
Established firms cannot afford to be complacent; they must aggressively redesign their existing SaaS products to center on AI-native workflows where “agentic” experiences—AI that can take independent action—are the core. This means moving away from traditional user interfaces that require manual data entry toward systems that proactively manage tasks and provide insights without being asked. To measure success, firms should look at metrics like the reduction in manual touchpoints per transaction or the speed of “concept-to-execution” for their clients. If a redesigned workflow is successful, we should see a dramatic uptick in adoption rates among employees who previously found the software cumbersome. The goal is to move from being a tool that people use to an ecosystem that works alongside them, and the primary metric for that shift is the “autonomy ratio” of the tasks the system manages.
While AI companies are launching their own SaaS entities, partnerships involving coding agents and industry-specific models are also increasing. How will the competitive landscape shift as these giants balance competition with collaboration, and what does this mean for client trust in regulated sectors?
The landscape is shifting toward a “co-opetition” model where AI developers provide the core “brain” and IT giants provide the “nervous system” and industry context. We see this already with Infosys leveraging Anthropic’s models while bringing their own deep industry knowledge and long-term client trust to the table. In regulated sectors like finance or healthcare, this partnership is vital because clients are often hesitant to hand their data directly to a tech startup, no matter how advanced its AI is. They trust the established IT firms that have managed their systems for twenty years to handle the security and integration aspects. By combining the model capability of an AI giant with the integration depth of an IT veteran, the industry creates a balanced ecosystem that satisfies both the need for innovation and the requirement for ironclad compliance.
Global demand for AI integration is expanding across millions of companies simultaneously. Given the massive scale of this transition, how can traditional IT experts leverage their existing industry knowledge to capture a larger share of the market, and what role will human-led change management play?
The sheer scale of this transition—affecting millions of companies at once—means the “pie” is growing so fast that there is more than enough room for both the new AI service companies and traditional IT giants. To capture a larger share, IT experts must lean into their greatest asset: their intimate understanding of how specific industries actually operate on the ground. AI companies can provide the math, but IT firms provide the context, such as knowing exactly how a global retailer’s inventory logic differs from a local distributor’s. Human-led change management will be the make-or-break factor here, as the cultural resistance to AI within an organization is often a bigger hurdle than the technical one. Experts who can guide a workforce through the emotional and structural shifts of an AI transition will be the ones who secure the most significant, long-term contracts.
What is your forecast for the Indian IT sector?
I believe the Indian IT sector is on the cusp of a major renaissance rather than the decline many predicted. While the arrival of OpenAI and Anthropic into the service space seems like a threat, it actually validates the essential nature of the work done by firms like TCS, Infosys, and Wipro. My forecast is that we will see these traditional giants move from being “labor providers” to “outcome orchestrators,” increasingly forming deep, symbiotic partnerships with AI creators to handle the massive global demand for integration. As long as they evolve quickly to embrace agentic workflows and AI-native designs, the next decade will likely see Indian IT firms becoming the indispensable bridge between Silicon Valley’s innovation and the complex reality of global enterprise operations.
