By Siddharth Pai
In the rush to embrace generative artificial intelligence (GenAI), enterprises around the globe have found themselves staring into a mirror—not of their future, but of their technical debt. Demos and proof-of-concepts built on large language models (LLMs) may have wowed boardrooms, but the transition from a generative pre-trained transformer (GPT)-powered chatbot to a true enterprise-grade AI agent is less of a sprint and more of a meticulously staged relay race. For Indian IT firms, long accustomed to riding successive waves of technological transformation, the enterprise AI rollout is both a challenge and an enormous growth opportunity.
Despite the headlines, most enterprise AI use cases remain in experimental or pilot phases. An LLM-based chatbot or an automated meeting note summariser may demonstrate some early utility, but these systems are far from production-ready. The obstacles are not flashy but deeply structural. Integrating AI with legacy IT systems, ensuring data governance, managing hallucination risks, complying with regulatory frameworks, and maintaining explainability all stand in the way of simple deployment. These hurdles turns the AI rollout into a drawn-out, services-heavy journey.
AI last mile edge
This is where Indian IT firms find their niche—not as model creators, but as enablers who bridge the gap between powerful AI models and usable enterprise applications. The major AI players, from hyperscalers like AWS and Microsoft to model builders like OpenAI and Anthropic, may have developed the core technology. Making it work inside a large bank, pharmaceutical company, or logistics giant involves a different kind of expertise. That last-mile integration and operationalisation is where the real value lies.
Unlike consumer AI apps that can be downloaded and used instantly, enterprise AI solutions resemble enterprise resource planning (ERP) deployments. Take, for example, the common enterprise ambition of deploying an AI agent that can handle HR requests, procurement approvals, IT support, and knowledge retrieval. This requires access to structured and unstructured data across multiple systems, each with its own application programming interfaces (APIs), security protocols, and formatting challenges. Indian IT firms, with their longstanding focus on enterprise integration, are well-positioned to take on this task.
Customising models is another critical piece of the puzzle. Off-the-shelf LLMs are often too generic and can suffer from hallucination or context drift. Enterprises need to adapt these models through fine-tuning, retrieval-augmented generation, or hybrid architectures that combine deterministic logic with probabilistic reasoning. This means building complex orchestration layers, establishing robust machine learning operations pipelines, and rigorously testing outputs. Few enterprises have this capability in-house, and fewer still want to build it. For IT services firms, these requirements translate directly into high-value projects and long-term engagements.
Once deployed, these systems need to be governed and maintained. That includes monitoring performance, retraining models with new data, refining prompts, managing user feedback, and ensuring the outputs remain compliant with internal standards and external regulations. The result is a new category of managed services focused on managing AI behaviour itself.
The real use cases for GenAI in enterprises go well beyond chatbots or internal assistants. They lie in domain-specific applications where industry knowledge and process expertise matter. In banking, for example, AI agents can support compliance teams, watch for fraud, and help personalise customer outreach. In manufacturing or logistics, agentic AI systems could help manage supply chains by reacting to disruptions in real time. These are not basic deployments. They demand a deep understanding of sectoral processes, workflows, and regulatory boundaries—areas where Indian IT firms have worked for decades.
This opens monetisation opportunities across the entire AI life cycle. First, there is advisory and consulting work. Many clients are still in the initial stages of understanding AI’s potential and limitations. They need help evaluating readiness, identifying viable use cases, building road maps, and estimating returns. This kind of front-end work is often high-margin and consultative in nature. Next is the design and integration phase. This includes data engineering, prompt orchestration, security compliance, and system integration. AI solutions must be embedded into Salesforce, Workday, or even legacy mainframes, depending on the enterprise. This integration is a natural extension of the core strengths Indian IT firms have developed over the years.
The “long train” is the ongoing management of the AI systems after deployment. This is similar in structure to traditional managed services contracts but focused on a new asset class—AI models and agents. Indian firms can build monitoring tools, retraining pipelines, audit trails, and governance dashboards, offering them as part of long-term support contracts. Fourth, there is regulatory and ethical compliance. As more countries develop AI-specific regulations, enterprises will need help in ensuring their AI deployments are compliant. Bias testing, explainability, auditability, and data lineage will be part of standard enterprise requirements, and meeting them will require technical and domain expertise.
Building AI platforms
To fully capture this opportunity, Indian IT firms will need to build more than just talent pools. They must also develop proprietary frameworks, accelerators, and tools. Several firms have already moved in this direction. Infosys has launched Topaz, TCS has WisdomNext, Wipro is building ai360, and HCLTech has introduced its AI Foundry. These platforms are not foundation models but infrastructure wrappers—designed to manage prompts, integrate APIs, monitor performance, and deliver AI-as-a-service within client environments.
At the same time, talent strategy is evolving. AI orchestration and prompt engineering are becoming core skills, and the traditional pyramid structure of delivery teams is being reshaped. Fewer junior coders and more experienced AI engineers are being deployed on client engagements. Some firms are building AI-only delivery centres to serve global demand.
Generative and agentic AI are promising, but their real impact in the enterprise will take years to unfold. That very complexity is an opportunity for Indian IT firms. As was the case with ERPs, cloud migrations, and digital transformation programmes, the real value lies not in the technology itself but in the disciplined, methodical work required to make it usable and sustainable and in that slow, steady, service-heavy journey, there is immense value to be captured by Indian IT services firms in 2026 and beyond.
The author is a technology consultant and venture capitalist