Gen AI models may have wowed boardrooms, but a lot of effort may be required to manage the transition from GPT (generative retrained transformer) to enterprise grade AI. LLMs show some early utility (say summarizing documents) but they are far from production or enterprise ready state.
First of all, there are structural issues — integrating AI with the legacy IT systems, ensuring data generation, managing hallucination risks, regulatory compliance and maintaining explainability. AI roll out, thus, is tedious.
The IT firms can be enablers here. They are the ones who bridge the gap between AI models and usable enterprise applications. AWS and Microsoft are hyperscaler AI players. OpenAI, Google and Anthropic are model builders. These develop core technology — this core technology has to work in commercial organizations, say a bank or a pharma company or a logistics company. It is the last mile integration or operationalization. The real value lies here.
Enterprise solutions are thus like enterprise resource planning ( ERP) deployment. If an AI agent has to take approvals for HR procurement, it requires access to data (both structured and unstructured) across multiple systems and each with its APIs, security protocols and formatting challenges. Indian IT firms are well-versed in such integration.
Another important issue is customization of the models. LLMs are too generic. and there are hallucinations and context drift. these models need adaptation. the first step is fine tuning the model. there could be retrieval-augmented generation or hybrid architecture. Such architecture combines deterministic and probabilistic reasoning. All this means building complex orchestration layers. ML is applied as pipeline. There should be testing of outputs. Few enterprises could do all this in-house and there is a role to play for IT services companies here.
After deployment, these systems are to be governed and maintained. There has to be performance monitoring. There has to be retraining of the models with new data. Prompts will be refined, and feedback attended to. The outputs should comply with standards (both internal and external regulations).
Enterprise AI is beyond generic chatbot models or internal assistants. There are domain-specific applications. In banking, it can manage fraud detection. In manufacturing, it can manage supply chains.
There are monetization opportunities across the entire AI life cycle. There could be pre-implementation cosultation. Next, there is design and integration. Here there is data integration, prompt engineering, security compliance and system alignment. AI solutions must be embedded into existing IT system, Salesforce and Workday.
A new asset class comes into play — AI models and agents, monitoring tools, retraining pipelines, audit trails, governance dashboards and long-term support.
IT firms will not be able to do this with just talent pools. They should also build proprietary frameworks, accelerators and tools. Infosys has launched Topaz, TCS has launched Wisdom Next, Wipro is building ai360 and HCL has introduced AI Foundry. These are infrastructure wrappers. They deliver AI-as-a-service.
Only disciplined, methodical work will make AI usable and sustainable.
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