Self-selecting employees then joined small, focused sessions to work on the opportunities they had prioritized – tackling them with an open, problem-solving mindset. As a wider group, they were able to consider common core solution elements that are required across the various identified opportunities. We chose to build out those we felt were repeatable with the fastest time-to-market and reasonable ROI.
Keep It Small and Regional at the Start
The key with RPA, in particular, is not to look for large scale wins, but to go for more minor but better defined successes. Our back office innovation to date has been prioritized on a local basis and focused on simplistic automation and narrow scope. It was also important for us to execute on an agile basis and celebrate small successes along the way to build team confidence.
These smaller pieces of work require less of an investment in terms of money and resources but also deliver a significant impact in a short timeframe. As an example, in 2019, our US team used RPA to automate approximately 60,000 repetitive payment tasks that had previously been handled manually. As our teams became comfortable executing small wins and the art of the possible, more opportunities came forward.
Add Complexity and Scale Over Time
Still on our journey, we have built on our initial learnings, best practices and, importantly, the confidence created in the first transformation wave.
Solving more basic problems with a narrow focus first has provided our staff with the knowledge and skill sets of “The How” to go about transformation. We’re now tackling more complex opportunities across multiple areas and functions. Whether liquidity and cash management, trade or credit – we are now able to leverage the front-end graphical user interface (GUI) or underlying code of previous solutions.
The team has expanded into machine learning and AI wrappers to add functionality such as segmenting customers or analyzing text or voice conversations. In our case, we plan to build on what we accomplished in 2019 by adding AI to RPA to free up more resources and direct them back toward our clients. Further down the road, we can also add third-party, packaged APIs where it makes sense to continue expanding on the value and scope of solutions. Eventually, what starts out with automating a mundane process can turn into a complete redesign – resulting in a more dynamic process.
An Outcome: BOLT
One outcome of our more advanced work is the development of our inbound customer service query system, BOLT. Instead of having people interrogate inquiries, we are using machine learning and RPA software to track a query as it moves around the bank. This way, when a similar issue comes in again, the software will recognize that queries like it often move in a certain pattern. Then, instead of following the entire pattern, BOLT will take it straight to the end-point – getting the customer to the right place the first time and improving performance.
While our commitment to this transformation journey continues and our expertise, understanding and comfort with RPA grows, it is clearer than ever that people are the most critical resource in such change programs.