Other parts of this series:
Robotics Process Automation (RPA) offers companies significant benefits; however, like every technology there are limitations with its functionality and may not be suitable to all processes.
In our last post, we talked about the “buzz” surrounding RPA and the potential efficiencies that RPA implementation can deliver. In this post, we will talk about the benefits – and the limitations – of RPA.
The benefits of RPA are undeniable. Companies implementing RPA can significantly lower costs, create service excellence, enable greater agility and build scalable capacity into their operations. Through our experience in the implementation of this technology, our clients across the board have seen an 80% reduction in processing costs; average handling times reduced by 40% with a 24/7 resilient operation – free of any human error; and an increased scalability through a 24-hour virtual workforce.
From a workforce perspective, RPA supports higher staff satisfaction by taking over monotonous tasks. allowing individuals to focus on higher value work. In fact, we have seen a 43% increase in FTEs able to re-direct their focus from non-value add activities to customer outcomes. In general, when implemented at scale, RPA can deliver a return on investment in approximately three to six months (Source: Accenture Research 2016).
The Limitations of RPA
It is these benefits that are generating the boom in RPA interest – and a good measure of hype. However, every technology has its limitations, and RPA is best viewed as an additional cost reduction lever and a foundational technology rather than an operational panacea.
Limitations of RPA include:
- First, RPA cannot read any data that is non-electronic with unstructured inputs. An example would be inbound correspondence such as paper customer letters. When a customer sends their energy company or bank a letter is it generally paper-based and unstructured. A company would then receive, scan and reallocate this letter to the correct department for processing. In this case, RPA will only work with a collection of other implemented technologies (such as OCR) required to make it digital and structured. This can become a costly hurdle before RPA can be applied, and companies may want to consider other solutions such as straight through processing, digital capture, process optimization or other intelligent automation technologies.
- Second, companies need to be aware of diverse inputs coming from multiple sources. For example, in a procurement function, supplier invoices may be received in different formats, with fields placed in different areas. For a ‘Bot’ to be able to read an invoice, all supplier invoices must be received in the same format with the same fields. Although robots can be trained by exception to read different fields, they cannot read multiple different formats – unless these are all digital and configured separately. In practical terms, there will always be a volume and cost threshold below which RPA is not an economic solution, and companies should focus first on high volume/high-cost processes for maximum benefit.
- Third, RPA is not a cognitive computing solution. It cannot learn from experience and therefore has a ‘shelf life’. As processes evolve – for example, through the introduction and use of other technologies — they may become redundant and require changes. It is therefore wise for a company to examine the process prior to building a ‘Bot’. Typically, at our clients we see a Bot shelf life to be anywhere from three to five years after implementation. Applied to a process that is inefficient and/or on the way out, that shelf life may be reduced to just a year. The business case may then not stack up.
- Finally, applying RPA to a broken and inefficient process will not fix it. RPA is not a Business Process Management solution and does not bring an end-to-end process view from approaches such as Lean Six Sigma. The same goes for out of date infrastructure – RPA will only mask the underlying issues. This can counteract any sustainable long-term savings by adding complexity which must be addressed down the line. Companies should focus first on addressing the root causes of their process or technology inefficiencies and then apply RPA to maximize the benefits
In the final blog in this series, we will examine whether RPA meets the criteria for being a truly transformative technology and how it relates to other technologies such as artificial intelligence (AI).