Save More Than 300,000 Hours: How RPA Boosts Productivity

Recently, UiPath conducted an internal study, trying to figure out the benefits of adopting business intelligence.  

They found that the previous quarter, automating processes with business intelligence tools like Robotic Process Automation (RPA) saved their workforce more than 300,000 hours.  

Yes, you read that right. 300,000.  

For a workday of 8 hours, UiPath saved nearly 40,000 workdays. That is the stuff of time travel, if you ask us.  

The outrageous savings mean that UiPath achieved a whole lot more in a whole lot less. And you, too, can, if you embrace business intelligence. 

Automation tools will become more intelligent as they integrate with advanced predictive technologies such as machine learning.

Here is how.  

Digital aid for all 

A typical workday comprises a variety of tasks.  

But, roughly, we can group them into two: productive and unproductive.  

Don’t get us wrong, both tasks are necessary. But only one creates true value.  

The first kind of tasks, the productive ones, are knowledge-based. These include tasks such as creating the marketing campaign for next month, outlining the gaps in your current strategy, or defining the values that guide hiring.    

The second kind of tasks, the unproductive ones, as you might have guessed, are necessary but menial. These include tasks such as making reports and notes, scheduling meetings, and ordering coffee (perhaps the most necessary).  

RPA robots or tools automate the latter, so that you have more time to focus on the former.  

For this reason, RPA tools are often called digital assistants.  

And by employing them, not only then do enterprises save time, but by focusing more on productive tasks, they also create more value.  

Here is a deeper implication: employees become liberated of from meaningless work, finding their jobs more value-oriented, meaningful, and satisfying.  

Efficient resource management  

But RPA tools further stretch productivity and value by streamlining the usage of enterprise resources.  

There are two ways RPA improves resource management. 

First, when enterprises say yes to automation or digital transformation, in general, they say yes to streamlined handovers and access to enterprise data across every discipline.  

Second, a data-centric organization makes data-driven decisions. Such organizations do not allot limited resources, like a team’s time or energy, without thought. Instead, they rely on evidence — data-driven insights — to determine a course of action.  

At the heart of a digitally transformed enterprise is a feedback loop.  

When one would underutilize its resources, insights would inform them that they should increase use, and by how much.  

Similarly, when one overuses its resources, data analytics yields insights that inform them of unnecessary consumption, and by how much.  

In both cases, business intelligence solutions enable enterprises to optimize resource management.  

What is next for RPA? 

In other words, what is next for intelligent software?  

Well, primarily, more intelligence.  

As automation tools integrate with advanced predictive technologies like machine learning, their scope of intelligence will expand.  

In the future, RPA robots would be able to, say, recognize natural language, allowing enterprises to perhaps accomplish goals, end-to-end, on voice command.   

The truth is, it is hard to say. The possibilities are endless. 

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