The GenAI CTO: How India's Technology Chiefs Are Rebuilding Architecture, Teams and Strategy Around AI

In October 2023, Freshworks CEO Girish Mathrubootham stood at the company's Freshworks Developer Summit and made a statement that reverberated through India's technology industry: the company would be deploying generative AI across its entire product suite, and every engineer at Freshworks would be expected to develop AI-native development capabilities. It was not a soft aspiration. The company subsequently reorganised its engineering organisation around AI integration, retrained significant portions of its 3,500-strong engineering team, and shipped Freddy AI — its generative AI assistant — across the CRM, ITSM and customer support product lines within eight months.

What Freshworks did at scale, hundreds of Indian technology companies have been doing in various forms since the release of GPT-4 and the subsequent explosion of large language model tooling. The CTO role has been at the centre of this transformation — and the experience of navigating it has been, for many technology leaders, the most intellectually demanding and organisationally complex challenge of their careers.

This piece examines how India's technology chiefs are approaching the generative AI transformation across three dimensions: architecture decisions, team restructuring, and strategic repositioning.

Architecture Decisions: The Platform Choices That Define the Next Decade

The first and most consequential set of decisions India's CTOs have faced in the generative AI era is architectural. The choices made in 2023–2025 about which AI platforms to build on, how to integrate LLMs into product workflows, and what data infrastructure is required to make AI applications effective are decisions that will be difficult and expensive to reverse.

The fundamental architecture question for most Indian technology companies is: build or buy the foundational AI capability? The options range from using proprietary API-based models (OpenAI's GPT-4o, Anthropic's Claude, Google's Gemini) through to training or fine-tuning open-source models (Meta's Llama, Mistral's models, Google's Gemma) on proprietary data. Each choice involves different cost structures, data governance implications, capability trade-offs and vendor dependency risks.

India's largest technology companies — TCS, Infosys, Wipro, HCL Technologies — have taken a pragmatic multi-model approach, building infrastructure that can route different types of AI tasks to different models based on capability, cost and data residency requirements. Infosys's Topaz AI platform, launched in 2023 and now deployed across over 200 enterprise client projects, uses a model orchestration layer that abstracts the specific underlying model from the application layer — allowing the company to switch models as the market evolves without disrupting client solutions.

Indian SaaS companies face a different set of architectural choices. For companies like Zoho, Chargebee and CleverTap, the question is how to integrate AI capabilities into products that are used by thousands of mid-market and enterprise customers globally, each with different data environments, compliance requirements and appetite for AI-generated outputs. The GDPR in Europe and India's Digital Personal Data Protection Act 2023 create specific constraints on how customer data can be used to train or fine-tune models — constraints that CTOs must navigate in their architecture choices.

"Everyone is talking about LLMs as if choosing the right model is the hard problem. The hard problem is data. If you do not have clean, structured, well-governed data that you can use to ground your AI applications, the best model in the world will not save you. India's legacy data infrastructure problem is the real AI blocker." — CTO of a large Indian fintech company, at the India AI Summit, May 2025.

The retrieval-augmented generation (RAG) architecture has emerged as the dominant design pattern for enterprise AI applications in India, as it has globally. RAG allows LLMs to generate outputs grounded in an organisation's specific knowledge base — proprietary documentation, customer interaction history, product manuals, regulatory guidance — rather than relying solely on the general knowledge embedded in pre-training. For Indian companies whose competitive advantage lies in domain-specific expertise (tax advisory, agricultural lending, healthcare protocols, legal research), RAG-based architectures provide a practical path to AI applications that are both powerful and contextually accurate.

Team Restructuring: The Skills India's Engineering Organisations Are Building

The generative AI transformation has created what many Indian CTOs describe as the most significant talent challenge of the past decade: the need to rapidly develop AI engineering capabilities in engineering organisations that were built for a pre-LLM world.

The skill sets required for effective AI application development differ meaningfully from traditional software engineering. Prompt engineering — the craft of designing, testing and iterating on the inputs to LLMs to reliably produce desired outputs — is a new discipline that did not exist as a professional practice before 2022. AI evaluation — the rigorous testing of AI system outputs for accuracy, hallucination rates, bias and safety — requires methodologies and tooling that are still being developed. MLOps — the operational infrastructure for deploying, monitoring and updating AI models in production — has evolved from a data science discipline into a full engineering specialisation.

India's engineering organisations have approached the AI skills challenge through a combination of reskilling, new hiring and organisational redesign. TCS's AI.Cloud unit, Infosys's AI-first training mandate (requiring all engineers to complete AI literacy training through the Lex platform), and Wipro's TalentNext AI specialisation track represent the large-scale reskilling approach. Nasscom's Future Skills Prime platform has separately credentialed over 150,000 technology professionals in AI-related skills since 2023.',

At the startup and product company level, the talent strategy has been more focused on targeted hiring of ML engineers, AI researchers and data engineers — often from the domestic talent pools of IIT and IISc, and increasingly from the Indian diaspora in the US and UK who are willing to return for compelling AI-native product building opportunities.

The organisational design dimension is equally important. Many Indian technology companies have created dedicated AI centres of excellence — small teams of senior AI practitioners who provide technical leadership, set standards, evaluate tools and vendors, and support AI integration across product teams. Razorpay's AI Lab, Swiggy's AI platform team, and BYJU'S (despite its financial challenges) AI content personalisation unit are examples of this model.

The CTO as AI Strategist: New Expectations from Boards and CEOs

The generative AI transformation has significantly elevated the strategic expectations placed on India's CTOs. In the pre-AI era, the CTO's strategic mandate in many Indian technology companies was primarily operational: ensure the technology function delivers reliably, manages costs effectively, and keeps the organisation's technology infrastructure current. Strategic technology direction — which markets to enter, which products to build, which platforms to adopt — was often driven by product leaders, business development teams or the CEO directly.

The AI era has changed this. Boards and CEOs are asking CTOs questions they were not asking two years ago: What is our AI strategy? How are our competitors deploying AI and what are the implications for our competitive position? What AI capabilities do we need to build versus buy? How much should we invest in AI infrastructure, and what returns should we expect? These are strategic questions, not operational ones, and answering them credibly requires the CTO to be a genuine business strategist as well as a technology leader.

Indian CTOs who have risen to this challenge are those who have invested in understanding the business context of their AI decisions — who can articulate not just which AI capabilities to build, but why those capabilities will create competitive advantage, how they will affect the cost structure and revenue model of the business, and what the risks of alternative strategic choices are.

Data Governance: The Unsexy Foundation

No dimension of the GenAI CTO's mandate has proven more practically challenging — or more consequential for long-term AI capability — than data governance. Every serious AI application requires clean, structured, accessible, well-documented data. The reality in most Indian organisations, including many large and sophisticated ones, is that their data estates are fragmented across legacy systems, poorly documented, inconsistently structured, and governed by a patchwork of policies that were designed for a pre-AI world.

Closing this gap — building the data infrastructure that makes AI applications reliable and safe — is unglamorous work that rarely generates the enthusiastic board presentations that AI demos do. But it is the foundation on which all other AI investments rest. CTOs who have prioritised data governance investment alongside AI application development are building sustainable AI capability. Those who have invested in AI applications without addressing the underlying data foundation are building on sand.

India's Digital Personal Data Protection Act 2023, which came into full effect in 2024, has added a compliance dimension to data governance that India's CTOs must now navigate. The Act's requirements around consent, data localisation for sensitive personal data, and the obligations of 'data fiduciaries' (organisations that determine the purpose and means of processing personal data) have significant implications for how AI applications can be designed and deployed. The CTOs who have engaged seriously with this regulatory framework — working with their legal and compliance teams to understand its implications before deploying AI at scale — are considerably better positioned than those treating it as a future compliance problem.

Building the AI-Native Engineering Organisation

The endpoint of the generative AI transformation, from the CTO's perspective, is not the deployment of specific AI features or even the completion of a model training programme. It is the creation of an engineering organisation that is genuinely AI-native: one in which every engineer uses AI tools as a standard part of their workflow, AI capabilities are considered in every architectural decision, and the organisation's ability to develop and deploy AI applications is a genuine competitive differentiator.

The evidence that this transformation is delivering results is beginning to accumulate. GitHub's 2024 Developer Productivity Report found that engineers using GitHub Copilot (which is widely used in India's technology sector) complete coding tasks 55% faster on average — a productivity improvement that compounds when applied across a large engineering organisation. Nasscom's AI Adoption Index 2024 reported that Indian technology companies with mature AI adoption programmes are reporting 20–35% productivity improvements in software development and quality assurance.

For India's CTOs, the mandate of 2025–2030 is clear: build the architectural foundations, the talent capabilities, the data infrastructure and the organisational culture that allow their engineering organisations to capture these productivity gains reliably and to build AI applications that create genuine value for customers. The CTOs who succeed at this will define what India's technology leadership looks like for the next decade.