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    IT underestimated hostile business climate, technology change speed

    Infosys former CFO T V Mohandas Pai acknowledged that ‘desi’ companies may have underestimated the hostile external business environment and the speed of change in the technology space amid signs of IT slowdown.

    IT underestimated hostile business climate, technology change speed
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    T V Mohandas Pai

    Hyderabad

    “... the external environment is hostile, more than what they had estimated and change has been more rapid,” he says, when asked about repeated downward revision of guidance by leading IT players due to fall in export revenue growth.  

    On the impact of President-elect Donald Trump, Brexit and possible trade barriers on Indian IT, the technology investor said he expects it to be “marginal”. “I think more work will come offshore, more innovative work will shift outside the US and the UK. There is a real shortage in these countries. If costs go up because of such shortage, then more work will get automated, move offshore in the next 3-4 years,” he added. 

    Asked if he sees troubled times for Indian IT engineers, Pai said: “Nothing more than what is today. As the industry has grown very large, market is adjusting to lower growth rates. If the industry grows at 7-9 per cent a year when OECD (Organisation for Economic Cooperation and Development) economy is growing at 2-3 per cent, it is pretty good.” 

    Observing that the Indian IT is already “rebooting” itself, he said the companies in IT and software services would “sort out” the stress they are facing as they are a globally dominant industry in a very competitive space under pressure. 

    On doomsday prophecy of “death of code,” Pai said: “Code will continue, huge legacy of around $4 trillion of code to work on. A large part of new code is getting automated, but building blocks (are) available. The breadth of need is so large that code will never die but can be done easier because of building blocks and automation.” 

    “Training a computer by AI, ML (Artificial Intelligence, Machine-Learning) needs large number of cases to be out into databases to enable pattern recognition. Analytics would be needed on top and interpretation of analytics and decision-making. Rule-based work would be diminished,” he added.

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