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Disrupt or be disrupted: Companies change the way they run

By NV "Tiger" Tyagarajan President & CEO Genpact.

"Tiger" Tyagarajan
"Tiger" Tyagarajan
A recurring question I get from clients, advisors, industry insiders - the people I talk to every day - is what are the latest industry trends?

The fact is that we are at a point in time when technology is changing the way companies run. Macro trends around low growth, uncertainty, and volatility continue to hold across industries, even where the source of disruptive change is not technology but, for example, regulation in the banking sector or patent cliffs in life sciences and pharma.

What this means is that companies want to change the way they run. They believe that if they don't drive disruption, they will get disrupted.

While the word "disruption" is used a lot today, it applies directly to the work we do. Not only do we tend to be disruptive in the work that our clients do with us, but that disruption tends to be more dramatic than it used to be, at a time when people are more willing to take risks.

All of that is playing out in more opportunities. Take banking or insurance back office, which not only tends to be already consolidated, but tends to be in one or two locations within that consolidated world. Restructuring the back office and middle office easily yields a 30%–40% cost benefit straight out of the gate. Of course, the initial transition has a cost attached to it, but then you start seeing the benefit - and the cycle time to seeing that benefit is rarely more than 12 months because so much is already consolidated. Where sufficient scale already exists, for example, in the banking and insurance world, that is almost table stakes.

The question then arises: What happens after that? How do you change the way business processes run, the way business gets done? To what extent can you actually automate, digitize, or eliminate work? Can you implement analytics to support increasingly predictive ways of getting work done?

Think about a process that uses data to generate output, then uses that output to determine what data it processes in the next iteration. By building intelligence into operations, processes become more efficient and effective. Consider insurance claims, for example: Yes, you can use technology to process claims faster, but can you use it to catch claims-related fraud when it happens 2% of the time? If you can, then the value created is tremendous.

One could easily talk about a 40% initial cost-of-process benefit, with a 5%–7% benefit each year thereafter. I would consider that, again, table stakes. From there, how much outcomes can improve would be multiples of the cost of doing that, anywhere from five to 100 times. If you think about procurement, for example, the benefit could be 100 times that cost. If you think about receivables, it could be a billion-dollar cash benefit in working capital.

Flip that to the finance and accounting world and think about life sciences or consumer products on a global scale: 100 countries, fragmented processes, the same thing being done in 100 different ways.

The first question is: How do we consolidate and bring it all together into five locations, five places with five ways of doing it? And, as part of that, how do we automate? How do we leverage robotic automation and intelligence? How do we deal with regulation?

While understanding the underlying technology is important, it is not the ticket to the party. Do we understand how underwriting gets done? Do we understand what kind of leases require what kind of treatment and why? Do we understand what kind of information clients find it difficult to give? Then what are the proxies to that information?

Understanding the domain, understanding the industry vertical, is what counts. And that requires expertise gained over many, many years. In the commercial lending space, for instance, we built our expertise over the course of 17 years, having started out with GE Capital.

Old legacy technologies of the past are changing very slowly - too slowly, I suspect, for most of us, and certainly too slowly for our clients. Robust legacy platforms are built to last. They are expensive to change and less flexible, less agile, by design. Clients want change fast. They want access to data and they want access to build insights.

This is what we are doing with our Systems of EngagementTM, which are layers of technology that sit on top of legacy technologies, are easy to integrate, and provide a solution that includes analytics and visualization plus data and decision-making slices. If you're a comptroller, for instance, this means having the ability to pull and cleanly present the same data from 100 countries.

Now, who is best positioned to bring all of this together? Our argument is that it's the people with domain expertise - the people who understand what design works best in a given domain - rather than the people who simply understand the technology, because the technology is now available.

Suppose you have a regulatory change. There's going to be a lot of scrambling to make the legacy changes required to match the new regulation. On the other hand, if you have a Systems of EngagementTM layer on top, you can make changes at that level - you don't need to make changes at the legacy level.

So one trend we are seeing is greater traction around discussions of domain expertise. These are discussions driven by end-to-end process understanding, by understanding demand and outcomes, and by building insights from data using component, cloud-based, easy-to-integrate technology.

Which is why we build expertise in our chosen areas. One of our mottoes during the last two years has been, "Let's choose our spots." I think part of the reason why we are seeing some of the growth we are seeing is because we chose our spots. I don't think anyone can do this across the board. You've got to pick your spots if you really want to help companies reimagine their enterprise architecture, and the way that they run, using technology and analytics that work.


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Mardi 16 Juin 2015

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