Nandan Nilekani, Co-founder Infosys
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Legacy system modernisation can not be postponed if enterprises wish to harness AI, Infosys co-founder Nandan Nilekani mentioned, warning that many years of technical debt are clashing with a widening gap between speedy AI advances and corporations’ skill to deploy them.
Throughout his keynote on the Infosys Traders AI Day on Tuesday, Nilekani highlighted that AI is transferring at an unprecedented tempo, exceeding different technological transitions. AI can be non-deterministic.
The central problem for enterprises, he famous, is methods to construct robustness, reliability, and resilience in a non-deterministic AI surroundings — a shift he described as “root-and-branch surgical procedure” for companies, making this transition structurally totally different from any earlier wave of expertise.
“During the last 60-70 years, individuals would simply add to the legacy techniques as an alternative of changing them. A agency can only reap the benefits of AI by essentially cleansing this up. The explanations are monetary drain and safety breaches. Many giant firms are spending 60 to 80% of their IT spend on sustaining techniques, which gives no enterprise worth. They wish to flip how they spend cash. Furthermore, most of the techniques had been designed when safety breaches weren’t as prevalent. Now, state and non-state actors are getting higher at it utilizing AI. And since the info is in silos, you’ll be able to’t innovate quick. These are the basic structural points.”
He mentioned demand is more and more being pushed by large-scale modernisation, including that for the primary time, AI gives the instruments to execute that clean-up sooner and extra economically. With many years of accrued technical debt now unavoidable, enterprises can not defer overhaul, creating a big opportunity for Infosys to ship it.
He mentioned that as AI turns into a bigger a part of general expertise spending, the stability of benefit is shifting from purchase to construct, which can be driving considerations round the way forward for some SaaS fashions. With AI making it far less complicated to develop purposes, enterprises could select to construct or substitute software program they beforehand bought, a shift that in the end advantages service suppliers who can design and implement these techniques.
Nilekani additionally noticed that as a result of speedy tempo of AI innovation, expertise development is racing forward of enterprise deployment, making a widening gap between mannequin functionality and real-world implementation. This, he described, as a deployment gap.
“The expertise is transferring sooner than the power of enterprises to deploy it. Whereas the mannequin efficiency is growing, the progress in implementation is just not as a result of it’s laborious. Essentially, it’s about organisational change, enterprise change, retraining your individuals, enthusiastic about non-deterministic approaches, and altering your knowledge so it’s not in silos. This deployment gap is what we will help tackle,” he mentioned.
He mentioned expertise transformation might be crucial, with roles shifting from conventional QA and growth to AI engineers, deployment specialists, and data-focused features, requiring large-scale reskilling throughout enterprises. Whereas demand for individuals will stay, the best way companies rent, prepare, and deploy expertise will essentially change.
In the end, he burdened that first-principles pondering, deep enterprise context, technical debt clean-up, and robust change administration will decide whether or not organisations can efficiently execute large-scale AI transformation.
“There isn’t any opportunity gap. If something, the opportunity is larger than ever earlier than. However there’s an execution danger. The stability of evaluation is every agency’s execution plan to get to the place they should. Can they do it effectively, with velocity, scale, and new mindsets, is the query.”
Printed on February 17, 2026
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