Exploiting RPA to Cultivate Operational Innovation
Abid Mustafa
Director of Strategy and Customer Experience

Professional Background: Abid Mustafa is a seasoned ICT professional with over 24 years’ experience in Digital Transformation, IT, Operations, Project Management, Strategy, and Customer Experience, with special expertise in RPA, robotics and AI. He is well versed in different aspects of the telecommunication industry and has worked for a number of operators in Europe and the MENA region. Mustafa is also the author of In the Age of Turbulence: How to make Executive PMOs Successful (ISBN: 1482740621), which is available both in paperback and Kindle format.

Education: BSc, Electronic Engineering, King’s College, London University

This is the third in a series of five articles on adopting RPA in the enterprise.

The advent of RPA technology has got various industries excited and organizations who are keen to exploit it in the workplace. Interestingly enough, the adoption of RPA technology by organizations has also spawned a hotly contested debate about measuring the success of the technology. Some advocate metrics such as the reduction in average handling times and the hours saved, while other espouse the number of processes robotized and the reduction in FTEs as effective metrics.

No doubt these are effective measures; however, for organizations at the commencement of the RPA journey the prime focus should be the number of use cases identified and their conversion into the number of robots.   This is because the generation of viable use cases for RPA implementation is the basis of all metrics and KPIs. Without use cases there will be no robots, and without robots metrics such as reduction in average handling time, hours saved and FTE reduction become meaningless.

Identifying RPA use cases is a completely different process to the one employed for the discovery of use cases for system automation. System automation use cases have much longer cycle times, and they require more effort in terms of requirements elucidation, governance, and management, from IT assessment to deployment. RPA use cases—from inception to deployment—have a much shorter life cycle of typically 4 to 6 weeks. The criteria used to assess the suitability of RPA use cases is also different to the one used for the evaluation of system automation use cases.  Characteristics such as large volume of transaction (VoT), long handling times (LHT) and dumb processes form the essential basis for RPA use case selection.  

The combination of shorter time to deployment and relatively simple assessment criteria means that target setting is a continuously evolving picture. Many organizations discover that in the beginning the number of software robots are few, but after a while the expansion in RPA deployment assumes exponential growth.  This is unlike system automation, where the correlation between use case identification and solution deployment is linear.

Therefore, at the commencement of the RPA journey, it is important for organizations to set aspirational targets that challenge business users to identify a huge number of use cases. This is important because not all use cases will translate into a software robot. In fact, one has to trawl through a large number of use cases to produce a small number of robots. As business users grow accustomed to RPA technology, the ratio between use cases and robots deployed will invariably improve.  Obviously, a mature process environment driven by structured data will greatly assist in RPA use case generation and software robot deployment. Business ecosystems that are contingent on unstructured data such pdfs, jpeg files, etc., will struggle to achieve targets.

Given the exponential growth of RPA deployments, organizations should expect to meet aspirational targets, such as the number of use cases and the number of robots deployed, quickly. At this juncture, the aspirational targets should be revised upwards and other metrics such as the number of transactions performed by robots, FTE reduction, LHT reduction, hours saved, and so forth can be introduced to drive the next wave of RPA expansion.

By approaching target setting in this manner organizations learn vital skills in identifying suitable RPA use cases and translating them into effective software robots. Hence, keeping target setting restricted to a few simple metrics will pay dividends in meeting the organization’s RPA ambitions.

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