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What's A Human To Do? Use Robots To Get Smarter!

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Meaning Making
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What's A Human To Do? Use Robots To Get Smarter!

The data generated by automation will radically improve process outcomes. Our new research from the Cognizant Center for the...

5 Minutes Read

The data generated by automation will radically improve process outcomes. Our new research from the Cognizant Center for the Future of Work highlights the ability of intelligent automation to improve materially upon what people can do. 

“Tools help make delivery better” may sound simple, but it hides a significant trap: An automation-for-automation’s-sake strategy fails to focus on the real prize — an explosion of rich process-level data. That’s where analytics comes in. The importance of analytics for processing insight and meaning-making is immense. The reality is that today’s digital age — compared with last century’s industrial age — presents an unprecedented ability to make business meaning from massive amounts of data. If you aren’t “doing” big data, the story goes, you’re in trouble, and terrible things will happen.

The list of game-changing and differentiating examples of process analytics is immense; for example, real-time dynamic fleet optimization for destination and delivery capacity for logistics, millions of miles of analysis of “hard brakes” for dynamic auto policy pricing, or collation of huge volumes of clinical data for optimized pharmaceutical trials. But leaders we interviewed are currently limiting the use of analytics — for the most part — to process optimization alone. At the same time, they are succeeding with using analytics in customer-facing processes to boost revenues. That’s great news for their customers, and is a trend we see continuing across most customer-facing work.

  • Cost savings plus new revenue streams. Well over half (55%) of respondents say that reducing costs is the key outcome of their analytics efforts today — and importantly, another 47% say understanding customer requirements is a core strategic goal. In essence, buyers are using insights to improve operations, while also opening new channels to revenue by understanding customers better — directly as a result of their business analytics activities.
  • Keeping process mechanics in plain sight — but what about doing things differently? Optimizing processes and driving deeper business insights dominates current analytics thinking. For a lot of companies, analytics may present unanticipated insights that allow them to change and run their businesses differently — especially in the face of disruptive moves brought by digital innovation. Over 40% of survey respondents say they value better process mechanics — such as throughput, quality and streamlining of processes — more highly than outcomes such as prioritizing business needs, better market penetration and segmentation or, last on the list, creating new products/services.

Generating substantial revenue growth among customers is also a major outcome. Nearly half of banks (45%) have seen at least 10% revenue growth from analytics aligned with their front-office and customer-facing functions, a figure that is anticipated to rise to  nearly three out of every four banks in three to five years (a collective 73%). It’s a similar story among insurance companies and healthcare payers as well, where customer processes are already analytics-driven and revenue growth is already flowing today, and is anticipated to continue in the future.

But automation has its limits — and there are some things that robots just can’t do (medical management, underwriting, case reviews, speak or comprehend colloquial slang, etc.). While automation is currently perceived as the “hot” delivery model, hot things still need a trivet so the proverbial table doesn’t get burned. That’s where a blended model of automation working in tandem with people can provide complementary outcomes.

And there is no doubt that the domain skills of many subject matter experts will continue to exist outside the realm of what we can expect from robots, at least in the short term. In order to really capitalize on the interplay of people and automation, organizations must master the resulting data. And that data is the product of automation and digitization. By automating systems to better sense, predict and interpret the data they produce, employees can work heads up, not down, with intelligence from digital processes supporting their own.

As a result, people need to stay ahead of the curve, not by being “faster or cheaper” but by developing, honing and capitalizing on the capabilities that are uniquely human and cannot be replicated today by automated software. Such activities include collaboration and teamwork with a highly diverse workforce (and yes, that includes robots), creativity, curiosity, constructive problem-solving, inventiveness, empathy and physical touch (say, in medical management). And of course, humans must focus on jobs that require a high degree of intelligence — at least more than what can be applied today by any robot.

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