The Secret to Scaling Intelligent Automation?

Gladson Baby, Vice President & Intelligent Automation Director, Fifth Third Bank tells all

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The key to maximizing the ROI of intelligent automation and RPA is scale. The more processes it is applied to, the more people that have access to the technology, the more value it will bring to the enterprise. 

However, successfully scaling business process automation requires more than just a reimagining of enterprise IT systems, but substantial organizational change paired with a robust governance framework as well.  

Few understand this better than Gladson Baby, Vice President & Intelligent Automation Director, Fifth Third Bank. With decades of experience in the technology and digital transformation space, he’s borne witness to both the highs and lows of intelligent automation implementation. 

As a speaker at the upcoming Scalable IA Live FREE virtual event this February 15-16, he’ll be sharing his approach to successfully scaling IA in-depth. To provide you with just a taste of what we have in store for you, below we have a short snippet of what he’ll be talking about at the event. Enjoy and we hope to see you in February.  

 

Seth Adler, Editor-In-Chief, IAN: When people think about keys to scaling RPA and IA, governance is one area that is sometimes overlooked? Could you tell us why governance is so important and what are some key considerations for building such a framework? 

Gladson: From my standpoint, just delivering RPA from the technical standpoint is very simple. If you’re just looking to use RPA as a productivity tool, governance isn’t so necessary. However, if you want to take RPA or IA to an enterprise wide scale, if you want to create a digital solution that can be leveraged by and really transform your workforce, then it needs to have a governance.

Governance should be approached from two different angles: from the security standpoint and from a performance standpoint. In other words, how you will make sure that, once it is in production, it is doing what it is supposed to do. 

When you are scaling it, ask yourself, “What is the audit around it? What is the logging mechanism around it? What are the standards and best practices used in order to make sure that your bots are conforming to your Info Security policies?”

Also, despite the moniker, tools like RPA can’t “think.” So if the process changes at some point, you need to make sure the “bot” is not scrapping the wrong information or malfunctioning in some other way. You have to be able to maintain control.

This is a major challenge because in the agile world, everything is constantly changing. If you have got 200 to 300 bots doing a lot of work on a single IT application and then that IT application changes every two weeks, your bots are done for. At least without governance. 

Having an IT governance around how IT changes are communicated and translated to your bots is critical. And are the bots self-enabled? Or is the team self-enabled to make sure that they can keep up with those changes? 

 

Seth: What are some of the common mistakes organizations make when scaling RPA and IA? In other words, what shouldn’t we do? 

Gladson: There are two things that people should stop: one is that automation teams often don't have the right support from the organizational level. And the reason why they are not having the right support from the organization level is that, one, the outcome they want to deliver, most of the time, just goes against hard dollar savings. 

I would suggest that, instead of focusing on just hard dollar saving, organizations and the experts the work with should expand their vision of ROI to include other business outcomes like growth, increased accuracy, resilience and so on.

The second they should stop doing, and I’ve been guilty of this as well, is that when we start migrating from a technical worker to digital workers, we want to deliver only the features. In the past, when we concentrated only on delivering the features, we delivered bots that were not managed properly. This led to bottlenecks and increased technical debt that hindered our ability to scale. 

In our case, we had to take a step back. We needed to stop delivering our features in order to take care of our technical debts in order to scale it further. So stop just moving forward, make sure you balance the creation of foundational elements with the feature elements. Otherwise, you will run into roadblocks very soon. 

 

Want to learn more from Gladson? Register to attend the Scalable IA Live virtual event this February 15-16 to view his full session on Transformation Through Scaled IA plus so much more!

 


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