The term hyperautomation began circulating widely through the financial services industry about two years ago. Banks, of course, have been focused on automating processes to improve efficiency and customer service for well over a decade. But hyperautomation that connects disjointed systems and transforms bank processes from end to end with technologies such as machine learning, artificial intelligence, robotic process optimization and low-code application development is still relatively new.

Worries about a recession and the need to drive costs down and accelerate risk has many banks thinking hard about how to seize the benefits of hyperautomation as soon as possible. “If you look at what’s driving the need for hyperautomation at banks, it’s three big things,” said Keith Pearson, a global head of financial services at ServiceNow. “It’s growth, it’s the reduction of costs and it’s the mitigation and reduction of risk. Those are the three things that bank executives get up every day and think about.”

This renewed focus on hyperautomation comes after halting initial implementation attempts. Indeed, Gartner found that 56% or organizations have an average of four or more hyperautomation initiatives underway. “Lots of companies jumped on the bandwagon of hyperautomation two years ago,” Pearson said. “But many have now recognized how hard it is to do and have stopped talking about it so much. I don’t think that’s right. Just because something is hard doesn’t mean we shouldn’t continue trying to achieve it.”

Indeed, not pursuing hyperautomation isn’t really an option for banks that have invested so much time and resources into digital transformation designed to vastly improve everything from internal operations and employee productivity to customer satisfaction. Increased competition from digital-first fintech companies and customer expectations that demand ever-more seamless, swift and personalized experiences also underscore the need to pursue hyperautomation.

The question for bank executives, however, is this: What is the most realistic pathway to implement hyperautomation? While it will inevitably look different at banks of different sizes and customer bases, hyperautomation success has three essential ingredients. They are:

Begin with customer outcomes in mind. It can be tempting to think of hyperautomation as primarily deploying advanced technologies. That is a mistake. Technology is a tool to deliver better experiences and value to customers — be it by onboarding new customers easier and quicker, bringing innovative products to market faster, or processing loan applications in a way that meets customers’ needs.

Hyperautomation needs to be approached through the lens of how technology and data can replace cumbersome and inefficient processes that frustrate both customers and the employees serving them. These are not hard for banks to find and focusing on one is a good starting point for a hyperautomation journey. Indeed, Lloyds Banking Group in the United Kingdom did just that when it fundamentally transformed its payment operations.

Before COVID-19 made it impossible for customers to visit physical branches, Lloyds relied on hundreds of staff and manual processes to handle issues such as payment requests and errors. As is the case in many banks, Lloyds also had data siloed in multiple systems across the bank. Over just 12 weeks, Lloyds worked with ServiceNow to bring together siloed data and begin applying machine learning and sentiment analysis to bolster efficiency and automation to payments. The result: 82% of direct debit refunds are now issued in less than 30 seconds, over 90% of batch payment exceptions are automatically resolved and more than 70% of the payment in the error process is automated.

Build the right digital foundation. One of the most persistent challenges banks have faced in moving to hyperautomation is siloed data. “Machine learning and other technologies help with the hyperautomation challenge, but only once you’ve got the data in a place where that technology can be applied to it,” Pearson said. “Machine learning is all about training machines to look for repeatable patterns in data. But they can only do that effectively if data is consolidated into one place.”

That’s what it means to build a solid digital foundation, which can happen when banks move away from on-premises data warehouses to cloud-based platforms that connect traditionally siloed data. Minnesota-based Bridgewater Bank experienced significant benefits serving its small-business customers during COVID-19 when it implemented ServiceNow’s Financial Services Operation solution. Instead of relying on manual processes and workflows, the centralized solution provided loan visibility to the bank’s leaders, credit analysts, loan administrators and business services team. A centralized digital foundation resulted in a 48% reduction in the workload required for each file and three hours of time saved thanks to automation.

Improve operational agility, while minimizing risks. One of the main drivers of bank automation and hyperautomation is a simple recognition of human fallibility. “Every time you introduce a human into a workflow or a process, it slows things down and introduces an opportunity for errors and data-quality issues,” Pearson said.

At the same time, though, human involvement in workflows is both necessary and desirable. Compliance requires human oversight and customers often want to engage directly with another person, even when digital solutions are effective. Done right, hyperautomation delivers the efficiency and insights that advanced tools can provide with a human touch when it provides important oversight and customer value. The result is greater operational agility and reduced risk.

To Pearson, one of the big inhibitors of banks securing the benefits of hyperautomation has been the idea that it all happens at once. Any moonshot promises that a bank can be fundamentally transformed via hyperautomation should be viewed skeptically. Instead, Pearson said, bank leaders should ask for specific milestones on the road toward hyperautomation. “What does the next six months look like? How do you incrementally build up your expertise to get there?” Pearson said. “You don’t have to throw a bazillion dollars at something. Instead, quickly build increments and demonstrate value. That gives you the confidence to continue.”