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Integrating automation into global business services

From robotics to cognitive computing, current trends in automation are reshaping global business services (GBS). While new technology, such as robotic process automation (RPA), can enable the “holy grail” of analytics and other higher level services, it may not be a quick fix. In order to reap the potential benefits, organizations must first understand the disruptive aspects of automation, along with the pre-requisites for integrating it into their GBS models.

How is automation disrupting traditional GBS models?

Traditional global business services (GBS) models deliver services via in-house captives, third-party business process outsourcers, or most commonly, a hybrid thereof. Regardless, they typically rely on labor arbitrage gained through off-shoring to create value. Today, those models are being disrupted by technology, particularly robotic process automation (RPA) and cognitive computing.

By automating repetitive, rules-based processes, RPA offers the potential for big gains in productivity and quality. In terms of GBS, the “disruptive” aspect of RPA is not only that it displaces human activity, but also that it reduces the need to go offshore to find less expensive labor and more standardized processes.

At least hypothetically, RPA can be implemented anywhere and at any point in a global business process. In service delivery transformation, an automation model reduces the need to go offshore.

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Why is RPA conducive to analytics and reporting?

There’s a rule of thumb that a robot is capable of doing the work of three full-time equivalents. “Bots” can process transactions much faster because they are electronically integrated into the system. That said, while they can go much faster than human beings, their processing speed remains constrained by the inevitable latency in between different tasks, such as accessing a database, waiting for it to respond, or opening an application.

In addition, qualitative service-level metrics are traditionally measured in timeliness and accuracy. Since robots do exactly what you tell them to, then by definition, they are completely accurate—not to mention that they don’t get sick or come to work tired because they stayed out late the night before. For these reasons, the service level with a robot is greater than a service level with a human.

But, the potential benefits of RPA go beyond processing transactions faster and more accurately to aggregating data instantly. That’s what makes RPA so conducive to analytics.

Because robots are seamlessly linked to the database, they can summarize information in real-time. Take invoicing process for example. Imagine being able to know immediately how many invoices are outstanding, where discounts have been given and taken, what the payments terms are, and how accurate reconciliations have been.

In a traditional model, this data has to be assembled manually, and it can take weeks to produce reports, which is often too late to support managerial decision-making. But if summary data can be generated and consumed in near real-time, it can be used to improve business performance.

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How are the frontiers of robotics expanding beyond the transactional?

About 70 percent of the respondents to Deloitte’s 2016 Global Outsourcing Survey said they are in the process of exploring RPA internally, with only about 13 percent indicating they are in the process of implementing it. This suggests RPA is still in the early stages. As such, service delivery organizations are mainly using it for simple forms of transactional processing, as in managing employee data in HR or creating requisitions in procurement.

Nonetheless, RPA is the first step to intelligent automation and cognitive computing. For instance, we’re working with some financial institutions to integrate automation into their delivery models, not only in terms of scanning contracts and assembling key contractual terms, but also in terms of reviewing those contractual terms and assessing them against other legal cases on a particular topic. Applying automation in this way is moving much more toward cognitive computing.

We’re also beginning to see the frontier of robotics expanding beyond the transactional in health care and radiology, for example, where doctors are now using cognitive computing to support diagnosis of illnesses.

While the benefits are compelling, integrating automation into the service delivery model isn’t as simple as writing some code and flicking a switch. The impact of automation on the organization must be considered, and the prerequisites of digitization and standardization must be met. Many large, distributed organizations have a long way to go in eliminating manual effort and in creating common processes across business units and functional silos.

Ironically, growing interest in RPA may catalyze this transformation, motivating companies to digitize and standardize so they can automate.

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