Data driven inspiration to boost returns on intelligent automation
Many enterprises are not getting the levels of returns that are possible with RPA. The returns can be significant, particularly when RPA is combined with other intelligent automation (IA) technologies, yet a lot of enterprises are achieving less than is possible. There are a number of factors that contribute to the problem. I discuss two of them in this post: using new technology in old ways and missing out on process data that can inspire them to do more in new ways. This is what I call data driven inspiration.
Many enterprises are not getting the levels of returns that are possible with RPA. The returns can be significant, particularly when RPA is combined with other intelligent automation (IA) technologies....
The past decade saw the emergence of RPA as a major lever for modernizing business processes. However, it soon transpired that many enterprises were using it largely to digitize processes that were the most obvious candidates for automation e.g. high volume transactions. Some were also automating the processes as they were without first optimizing them for robots. Some have not achieved scale either with deployments remaining small in terms of the numbers of robots and how much of each process they have automated.
With IA return on investment (ROI) of 100% or more is certainly achievable as shown by studies conducted by independent analyst firms. Forrester, for example, has published a series of RPA-focused Total Economic Impact (TEI) assessments based on in-depth interviews with RPA users that show ROIs of upward of 110% over three years, with some achieving many times more. In contrast, as Head of Technology Immersion at Emergence Partners, earlier this year I conducted the Technology Impact Pulse Survey, a study that showed enterprises aimed and achieved relatively modest returns from IA. As part of the survey we interviewed senior managers in 177 large enterprises in Europe and North America. We asked them what returns they were aiming to achieve from their priority technology investments in 2020 and how satisfied they were with the results to date. Firstly, we found that process automation had been the top priority. Secondly, that the majority of the enterprises were satisfied that they would meet their initial ROI targets, but they were aiming for relatively modest returns of between 24% and 29%. Those rates are good but small for IA. Even allowing for accumulation of savings over time, the companies are unlikely to meet the high returns that others (e.g. those in the Forrester TEI studies), have been able to achieve over three years.
With IA return on investment (ROI) of 100% or more is certainly achievable as shown by studies conducted by independent analyst firms.
The question is why aren’t more enterprises achieving higher levels of returns? I believe this is related to the approach and maturity of adoption. The approach can be a significant contributor to the lower than expected returns from IA and using new technology the right way is central to that. Let’s face it, we humans find it difficult to change old habits and this reflects in our approach to adoption of new technology. For example, the first iron bridge in the world was built in 1779 in Shropshire, England. This was a huge technological advance over wooden bridge structures but it was built as if it was made of wood using mortise and tenons, dovetails and wedges (source: https://www.bbc.co.uk/history/british/victorians/iron_bridge_01.shtml). We had used rivets since medieval times to make armour but we did not join the dots and instead made the first ever iron bridge using carpentry techniques. Nonetheless, the Iron Bridge was a major development in the first industrial revolution.
The first iron bridge in the world was built in 1779 in Shropshire, England. This was a huge technological advance over wooden bridge structures but it was built as if it was made of wood using mortise and tenons, dovetails and wedges.
Today in the fourth industrial revolution, the automation of office-based knowledge work is a significant development, and just like the technique used in constructing the Iron Bridge, some RPA adopters continue to do things the old way except for the switch from humans to robots. They automate processes as they are without first optimising them for automation. Yet, robots can do things a lot faster and undertake many steps in parallel in near real time.
Enterprises also need help to identify potential opportunities for process optimization and this is where mining processes for information can contribute significantly to modernizing and updating business processes.
Another problem is using a subjective approach to finding candidate processes for automation when we can do it scientifically using actual insights mined from processes. Our software, KYP, even tells you what the best IA technology is to use to digitize the identified processes, and it calculates the business case for automation and presents it to you.
The optimizations and improvements can be incremental but the benefits add up. There’s the case of a contact centre operator, that improved its chat operations by identifying the parts of the chat process that was frequently repeated with customers asking the same types of simple questions. These could easily be handled by an Intelligent Virtual Agent (IVA). Without the process data you can only guess at this. The company looked further into the most popular questions and expressions used by customers when interacting through the chat channel. It then trained its IVA to take over the channel but with more complicated questions directed to the contact centre staff. Subsequently, the time that staff take to answer questions on the chat channel has reduced to only ~4% of their daily work. The rest is taken care of by the IVA.
This is what I call data driven inspiration when process data shows us the potential for improvement, to do things differently and to innovate.
Achieving only modest returns from RPA and IA means that we are losing out on their true potential.
Achieving only modest returns from RPA and IA means that we are losing out on their true potential. We need to develop our ability to take advantage of technology the best way possible – old approaches simply reduce the potential returns as they limit what we can do. We also need to get more scientific about how we scale automation in the enterprise and let process intelligence inspire us to innovate, to come up with better ways of doing things. We have the sources of information – we just need to collect the data and leverage it to improve outcomes.