IT Spending Remains Strong, But Focus Has Shifted To Efficiency & Value Creation

Artificial Intelligence (AI) & generative AI are sweeping the technology world. What used to be many days of work in coding is now being done in few hours, deploying AI applications. Emergence of LLMs (large language models) is bringing in changes across sectors- be it retail, marketing, healthcare or manufacturing. Owing to such changes, there are slow changes happening to business models of Indian IT industry.

In a conversation with the Bizz Buzz, Rahul Nawab, President, Data & AI at Prowess Consulting said that enterprises are excited about leveraging AI & GenAI solutions for improving efficiency. The construct of outsourcing deals is becoming more outcome based than relying on deployment of human resources, he added. According to him, technology spend is very much continuing at sound pace, though focus has shifted to more outcome-based delivery

Many analysts and industry participants have started talking about imminent danger to traditional business model of Indian IT industry amid a sweeping AI (artificial intelligence) wave globally. What is your opinion about this matter?

The biggest foundation on which Indian IT industry has been set up is that India is a hub for technology talent. Interestingly, the hiring of these talents can be done in different models. For instance, some IT firms are hiring from tier-II engineering and MBA colleges, while some IT and global consultancy firm are hiring from IITs and IIMs. So, all kinds of cost structure are available in India. Now, the time taken for coding has already been shortened (due to AI applications). The coding time frame has come down drastically.

The second thing happening now is that enterprises want to know how AI is going to transform their businesses. They want to know about return on their investments. They are asking for value addition that GenAI will bring in. So, the conversation for awarding a project has shifted. Also, there is a lot of confusion around how the technology will be deployed; what is the best use case; and what is the best strategy. However, things are now changing as the technology is maturing. There are solid use cases.

There are many views about the impact of AI wave on enterprises’ IT spending in the future. Some industry leaders have started arguing that there is a lot of inefficiency in the technology spending of enterprises, which may not continue in the future. Do you agree with such assumptions? What will happen to global IT spend if that happens?

This AI wave should be seen as both a threat and an opportunity. The threat is, we may not see every company survive. If you are in the business of supplying talent, then that may not continue. Because the demand is now more about strategy and about thought leadership. There is, of course, much wasteful spending happens. So, my opinion that there is disruption and there will be winners and laggards. And some companies may not survive if they don’t adapt to the changing technology landscape.

Reports indicate that developers of LLMs are now falling short of data to feed the algorithm. If that is true, then will these foundational models be able to solve industry-specific problems? What is your opinion about this aspect?

LLMs (Large Language Models) can take you so far. You need to understand the business context in which these models will be applied. The way we think about it is that there is LLMs but not without human on the loop. For example, we have built content creation tool with all the prompts. But we also employ an intelligent content writer, who understands the context and make it ready for our clients. But it is cutting down cost due to increased efficiency.

What kind of conversation is going on with regard to AI and generative AI from client side? Can you give us a view in this regard?

Clients are expecting around 2X return on their investment in this space. This used to be 5X but now has come down to around 2X on their investment. The time frame of this return depends on the complexity of the problem. The reality on the ground is that there is a lot of concern about privacy of the data. So, service providers have to bring their LLMs to customise the solution for the client. Every industry has different use cases. Certain use cases are customer facing, which are most complex. There are internal-facing ones, like coding, which are easier than the first one. These are all efficiency-related ones. There are others which combine both. We are seeing the most amount interest in the consumer-facing segments, which are most difficult to execute.

Some industry experts are of the opinion that despite the change potential of AI & GenAI, it will be implemented slowly (like cloud technology) so that minimum disruption happens to the portfolio of IT firms and related service providers. Is this something you agree with?

Pretty much. Because, they have tried the big bang approach but it didn’t work. So, the implementation is being done slowly.

What is happening in implementation of AI & GenAI solutions in the financial services space? How data privacy laws are being complied amid such implementation? Can you give a view in this aspect?

Like I said before, it is a bigger concern. Financial services industry had been the last to implement the cloud technology. They are usually the last in implementation of any new technology. However, in this space, they are joining the party early. Usually, retail industry is the first mover in leveraging AI & GenAI because they don’t hold any sensitive data of customers. As of now, financial services industry is embracing AI at a sound pace. That is my view.

How is the technology spend panning out in the US given the uncertainty about growth? Are clients closing deals or delaying decisions with regard to this? Can you give us a perspective in this matter?

For us at Prowess Consulting, we have actually several marquee clients. So, deals are happening. The concept is more focussed on solving problems than providing human resources. We are seeing tremendous amount of interest in AI space. So, the question is not ramping up certain resources any more but the construct is more on how to solve business problems by leveraging emerging technologies.