Mphasis chief sees discretionary tech spends accelerating
Discretionary spending on technology will accelerate in 2026, though budgets will be relentlessly reprioritised, said Nitin Rakesh, chief executive of mid-tier IT services firm Mphasis, as enterprises move over macroeconomic concerns and comprehend ways to incorporate artificial intelligence (AI) in their business.
"When ChatGPT came around in 2022, everyone was amazed at what it could do... it could code, write and summarise. But enterprise adoption takes time. With that in context, investment in technology is only going to increase," Rakesh told ET in a virtual interview.
However, with AI fundamentally changing how enterprise technology is becoming "more ingrained and more embedded," companies' limited capital is leading them to reprioritise how they spend on technology and, by extension, AI, he said.
"We have validated proof of concepts and use cases. The focus is now on scaling them, with questions like what data models are to be used, which AI stack will be most suitable or what data governance will look like," Rakesh said.
Clients are identifying pockets of their business to make it efficient through the use of AI, automating tasks that don't need as much manual effort, such as some business operations and software engineering, according to the chief executive.
The common thread across large contracts, he said, is a transformation thesis that could span data centre consolidation, movement to co-location and cloud, application modernisation and decisions such as how much compute should be on graphics processing units versus central processing units for AI workloads.
"It is not that they want fewer engineers. It is just that they can write more code... there is always more coding to be done in the backlog," he said.
"We have to pivot as an industry away from being compensated for inputs to being compensated for outputs," Rakesh said, arguing that classic labour arbitrage models built on the "average cost per person plus margin model" are giving way to outcome based constructs where software agents sit alongside human teams.
Mphasis' fixed price component has increased about 50% year on year, as clients re-bid contracts early to lock in AI led savings and expect providers to figure out how to deliver them, he said.
"When ChatGPT came around in 2022, everyone was amazed at what it could do... it could code, write and summarise. But enterprise adoption takes time. With that in context, investment in technology is only going to increase," Rakesh told ET in a virtual interview.
However, with AI fundamentally changing how enterprise technology is becoming "more ingrained and more embedded," companies' limited capital is leading them to reprioritise how they spend on technology and, by extension, AI, he said.
"We have validated proof of concepts and use cases. The focus is now on scaling them, with questions like what data models are to be used, which AI stack will be most suitable or what data governance will look like," Rakesh said.
Clients are identifying pockets of their business to make it efficient through the use of AI, automating tasks that don't need as much manual effort, such as some business operations and software engineering, according to the chief executive.
The common thread across large contracts, he said, is a transformation thesis that could span data centre consolidation, movement to co-location and cloud, application modernisation and decisions such as how much compute should be on graphics processing units versus central processing units for AI workloads.
"It is not that they want fewer engineers. It is just that they can write more code... there is always more coding to be done in the backlog," he said.
"We have to pivot as an industry away from being compensated for inputs to being compensated for outputs," Rakesh said, arguing that classic labour arbitrage models built on the "average cost per person plus margin model" are giving way to outcome based constructs where software agents sit alongside human teams.
Mphasis' fixed price component has increased about 50% year on year, as clients re-bid contracts early to lock in AI led savings and expect providers to figure out how to deliver them, he said.
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