Today, there is no denying that when it comes to increasing efficiency, reducing cost and offering a better customer experience, intelligent process automation (IPA) is a go-to strategy for businesses, and pharma is no exception. IPA promises that it can free up people to do more value-added work and play more strategic roles within their organizations. At a fundamental level, IPA is an emerging set of new technologies that include robotic process automation (RPA), conversational AI and machine learning to mimic activities carried out by humans. With the evolution in IPA technologies and the broad applicability of such automation, IPA continues to be a buzzword in every industry. Given the many benefits and possibilities, it’s understandable why organizations are jumping onto the IPA bandwagon.


However, most organizations aren’t succeeding at scaling intelligent process automation. A recent HFS survey found that “barely more than one-in-ten enterprises has reached a place of industrialized scale" with IPA.  


From our experience in running intelligent process automation programs for our clients and our own business, we have found that some aspects of automation are easy, such as creating automation POCs and pilots, working with early adopters and building top-down estimates. But scaling automation is a much more challenging endeavor.


ZS has identified four key elements that are necessary to realize the benefits of IPA at a larger scale:

  1. Obtain top-down strategic alignment. While the process discovery and assessment of an automation project is a bottom-up exercise, it's essential that an IPA program has buy-in at the executive level and is strategically aligned from the top down, with a clear mandate. The program needs to have a clear and consistent change story tailored to the uniqueness of the company, communicated throughout the organization, starting at the executive level. Top-down IPA governance needs to ensure that various pilots across the enterprise are connected and synchronized in a meaningful way.
  2. Think portfolio returns. For any IPA initiative, there are going to be some projects with high ROI and others with smaller ROI. While initially, one may prioritize high ROI processes, it is essential to understand that the benefits of smaller IPA use-cases are additive in nature, and the whole business case is going to be much higher than the sum of individual use cases. Instead of focusing on the ROI of each project, organizations should focus on portfolio-level returns for the whole IPA program. In addition to real dollar savings it is important also to consider intangible benefits like overall quality, timeliness and end customer experience, which are equally critical in the current business environment.
  3. Establish an automation platform. Instead of bolting all new automation onto old platforms, organizations today need to create automation as a platform. Succeeding at building an automation platform means starting with capabilities and problem-solving, rather than with a specific technology. The next generation operating model needs a far broader set of IPA technologies (smart workflow, RPA, OCR, NLP, computer vision) and a lean, design-thinking mindset.
  4. Enable internal IPA capabilities development or continuous improvement. For scaling intelligent process automation across any organization, it is crucial that the execution of IPA shifts from being a center of excellence activity to an operational line activity. As IPA technologies evolve, continuous improvement becomes the single determining factor for scaled automation. Future operating models will bring different kinds of jobs, which will need automation-specific roles in the team. Organizations need to reskill people and provide custom, role-based training across the enterprise for building an in-house IPA capability.

Intelligent process automation is here to stay. IPA technologies and use-cases continue to evolve. Achieving IPA impact at scale in this automation- and AI-first world means challenging a “more of the same” mindset and shifting to a “design for automation” mindset.