It’s also important to consider RPA in the context of a rapidly shifting business and technology ecosystem. “System upgrades, underlying architectures, evolving business process—all have to be considered so that effort put into automating processes is done in conjunction with others things around it,” Mazboudi says. “Anytime you’re overlaying automation onto existing systems, you have to be careful of underlying changes and be aware of what changes may or may not break the automated processes. Once [automation] is in place, you have to manage it as core systems evolve.”
Another challenge in a massive organization like Deutsche Bank is aligning the objectives of various business functions. “If you centralize the automation effort, you can benefit from that scale, but it’s hard to take that approach because priorities and incentives can differ,” says Mazboudi.
His approach instead has been to start with small, foundational RPA projects to create clear uses cases for automation. “Otherwise,” Mazboudi warns, “you’ll end up with a cottage industry of automation whereby you don’t have economies of scale. It’s very challenging.” CIOs looking for the best place to start with RPA should “follow the money,” Mazboudi says. “Ensure the use case has high return potential and commitment from the business area represented.”
RPA plus cognitive computing plus advanced analytics plus workforce orchestration
Mazboudi also warns against pursuing RPA as a standalone solution. “When we talk about RPA, it’s never RPA by itself. That’s a dead end,” he says. “Rules-based automation is short lived; that’s not where the value proposition is. It’s in RPA plus cognitive computing plus advanced analytics plus workforce orchestration.” If digital transformation is the end goal, RPA alone won’t get any company there. But it’s an enabler to achieve the level of efficiency in operations, says Mazboudi.
Deutsche Bank is particularly interested in RPA tools that incorporate cognitive capabilities and has been working with vendors like WorkFusion in this area. RPA can automate tasks based on rules, but machine learning can tackle more nuanced work like recognizing the type of document that comes in or scanning unstructured data for specific information. RPA alone can make lower-level employees more efficient. RPA plus cognitive computing can free up subject matter experts to focus on higher value activities. The next step is taking the knowledge encoded in those algorithms and making it available to employees or customers via intelligent assistants. “That’s powerful,” he says. “That’s higher in value than just RPA.”
Deutsche Bank has moved beyond experimentation and analysis to the early stages of RPA implementation. “It took a while to get our heads around the value proposition and put that in context of what need to achieve,” Mazboudi says. “It’s a new concept that, at first glance, appears to be disruptive. It takes some time to understand how best to leverage the technology.” Education and demos— part of the charter of the lab—have been critical in moving forward.