The IT industry appears to be a challenging field for freshers to enter in the age of AI, with companies increasingly relying on lateral hires as they battle AI related costs. Despite these complexities, Hexaware’s CEO R Srikrishna puts his faith in the younger workforce, arguing that while lacking in experience, the younger employees have a longer experience interacting and using AI.
In a conversation with businessline, Srikrishna shares his expectations from newcomers entering the workforce and explains the rationale behind offering multiple pricing models to customers to encourage spending.
Edited excerpts:
What is the objective of offering multiple pricing models to customers?
The first objective is to participate in token economics. The token cost is going to become a material part of IT budgets. We want to be in that line item of budget where we add value to our clients. We do not want to be a reseller. We want to participate in the token revenue and still optimise it for clients. That’s the principle behind the models.
As you rethink pricing, how are you going about hiring freshers?
What is true is that the percentage of freshers in our hiring process has seen a reduction. My belief is it’s temporary. Our industry has been very good at commoditising and making factory out of scale creation. There was a hype period when we struggled to find talent but soon enough we created training factories and churned out masters. That time will come for AI too. Younger talent is actually better in AI because they learn fast and many of them are AI-native as well. They are kids out of college, who have been using AI in schools for at least three years already. Though our customers understand this, they’re are still not ready to have their program start with younger people. That will also change.
How much of hesitation for fresher hiring is coming from the clients?
Much of it comes from clients. It has always been the case that they want more senior talent. In AI, that is even more true. But truer still is that that younger talent are better at AI. In our own AI labs and in pure R&D, the average age is around 26 years. Our platform building and engineering teams are also full of very young people.
How are you focusing on R&D in that sense?
What would take us months to build, we can do it in weeks. We are able to build a minimum viable product (MVP) in just eight weeks and the first release in 12 weeks. That’s the kind of speed at which we can build on the platform side. We have significant investments, disproportionate to our size. We will have to increase it further.
How are clients using AI considering legacy tech debt?
Historically, despite huge opportunity, customers don’t spend money on tech because it is too time consuming, risky, and expensive. AI has changed that, and has made it more deterministic. So, customers are using AI for tech debt. We are now seeing deals that are above $10 billion in tech debt remediation and in modernisation. The second issue of tech debt is ahead of us. Once Mythos becomes public, and cloud security starts being more widely used, the volume and velocity of vulnerability discovery is going to be staggering. One of the remediations for that is going to be necessarily tech debt elimination. All these vulnerabilities are resident because of bad software, old components which have not been patched or upgraded or even supported anymore that are open source. That’s the biggest source of vulnerabilities and the biggest opportunity in a bad way.
Published on August 18, 2026
