We’re in the midst of an exciting confluence. Disruptive technological trends in robotic process automation (RPA) and artificial intelligence (AI) are rapidly transforming business models, enhancing productivity, and enabling innovation in the way organizations operate and conduct business.

Robotic process automation (RPA) and artificial intelligence (AI) are being leveraged by banking, insurance, and financial institutions to provide a key competitive advantage. Transformation in back-office operations and offhand customer interactions is now what is considered the new mainstream.

Organizations that are currently opting to sit on the sidelines to watch how it pans out will see that if they don’t join in this bandwagon, they will be left behind with outdated procedures and business models relying on service firms that no longer exist.

While many have dipped a toe with trials and tests – few have experience building large-scale, organization-wide RPA capabilities. Implementing a few pilot processes is often critical in satisfying the demands of board members, investors, and key decision-makers. But it also places the RPA program on the right track by increasing the knowledge and expertise needed to scale and industrialize RPA. However, this straightforward ability to prove the concept leads many to run before they can walk. Implementing one robot is relatively easy. But meeting the aggressive automation goals post proving the concept and implementing hundreds in the live environment across diverse processes across departments – is a herculean task.

To realize the full potential of RPA, it is important for organizations to put enough diligence into building their robotic operating model right at the outset of the program — which defines the interaction between processes, organizations, roles, and technology to generate specific business outcomes. While RPA operating models are not “one-size-fits-all”, the bottom line is that a company’s RPA transformation is likely to fail if it tries to keep the same operating model after automating human tasks.

Scaling up from piloting then requires a formal structure and operating model, centralized control, strong governance, approved business cases and a long-term road-map that is laid by the robotic operating model. It involves systematically rolling smaller projects in a wider program and delivering implementations as per the roadmap in waves and benefits in parallel. This formal, robust, holistic approach is the only way to build a sustainable automation capability that realizes the benefits of larger-scale RPA.

In our experience, RPA at scale is best achieved within a common environment – using common security, risk and quality standards – under centralized control and governance procedures; minimizing risk and maximizing learning. However, selecting the most optimal operating model depends on the organization’s vision, strategy and culture.

A well-functioning RPA CoE is like a well-oiled machine that not only delivers business benefits but also prepares the organization for more advanced automation concepts such as machine learning and artificial intelligence.

There is certainly plenty of excitement around robotic process automation right now, but to scale this capability companies need to adopt a CoE approach and the right operating model to get maximum return on investment.