The evolution of AI: a practical agenda for Indian businesses
By Kishan Tummala, CEO, K Computers ·
Artificial intelligence has moved from specialist research into everyday business conversations. For Indian enterprises, the useful question is how it can improve a real task: finding information, preparing a document, supporting a customer or helping a team make a better-informed decision.
This evolution has unfolded over decades. Early approaches often relied on rules written by people. Machine learning made it possible to learn patterns from examples, while advances in neural networks widened the range of problems computers could tackle. The Transformer architecture introduced in 2017 was another significant research milestone. Generative AI has since brought the ability to produce text, images and code to a much wider audience.
The next challenge is turning these capabilities into dependable work. An assistant that drafts a response is useful only when the answer is relevant and accurate. Systems that coordinate several steps in a workflow need clear permissions, checks and a way for people to intervene. More capable software increases the importance of thoughtful management.
For businesses in Hyderabad, Telangana and Andhra Pradesh, I believe the best starting point is a focused pilot. Choose one recurring problem, identify the information needed and define what success would look like. Measure the time saved, the quality of the result and the cost of operating the solution. Expand when the evidence supports it.
Data deserves attention before deployment. Documents need to be organised, access should reflect each employee’s responsibilities, and sensitive information needs appropriate protection. Generative AI can produce convincing errors, so staff must know when to verify an answer and when a human decision is required. Training people is part of the investment.
Infrastructure should follow the workload. Cloud services may suit some projects; others may benefit from local or hybrid systems because of data requirements, connectivity, latency or operating costs. Servers, memory, storage, networking and, where needed, GPUs should be sized against the chosen application. Backup, security and support belong in the same plan.
At K Computers, our approach is to begin with the business requirement and explain the infrastructure choices clearly. The goal is a solution that teams can use, maintain and improve. Clear configurations, an agreed scope and transparent quotations help customers make that decision with confidence.
AI’s evolution will continue. My advice to business leaders is to start with a useful problem, involve the people who do the work and measure the outcome. Practical learning today can build the judgement needed for larger decisions tomorrow.
Further reading: The 2017 Transformer research paper · NIST guidance on generative AI risk management.
