Efficiency is a common driver for investing in process intelligence in shared services but, as with any other technology, there are different stages of maturity of adopting it that can progressively deliver greater returns. It is what you tap the technology for and how you act upon the findings that make the difference. A simple way of looking at it is the three-stage model highlighted in Exhibit 1. This shows an entry, middle and advanced levels of maturity, each achieving more than the previous stage with a broader set of outcomes and greater benefits.
Entry level adoption maturity
At the entry level is process intelligence solutions adopted with a narrow focus. This could be to find and address a particular issue e.g., shortage of capacity, or as is often the case, to find more opportunities to automate processes with RPA (Robotic Process Automation). In this approach with KYP.ai you can get insights on causes of problems or new opportunities for robotic automation very quickly, but you leave much potential for digital transformation and productivity mining, untapped.
Middle level – focused on digital transformation
With experience comes the next level of adoption maturity but of course you can bypass the previous level and jump right into the middle-level if you already want more out of your process intelligence tool. At this level you are collecting more data and mining it for further insights. You are looking for opportunities for transformation digitally or otherwise: which processes or parts of them can be more standardised and digitalised? Are the teams’ shifts set up correctly to match demand patterns? Are there underlying system or infrastructure response time issues? Another benefit of a broader scope of process intelligence projects is finding openings for innovation. More on this in my blogs titled “The Opportunities Hidden in your Shared Services Data” and “Driving Excellence in Shared Services with Process Intelligence”.
Advanced level – productivity mining
This is the level that you want to get to in order to maximise the benefits of process intelligence. At this level you are looking for even more opportunities than the previous two stages, extending the focus to improving end-to-end employee productivity and experience. Process intelligence opens new windows into human and machine interfaces giving rise to the new concept of productivity mining – when process intelligence not only uncovers process- or system-related problems and bottlenecks but issues that reduce productivity and employee job satisfaction. The data can be mined for actionable insights to help employees reach the state of flow* to increase productivity.
To recap on flow, it is when work is the right mix of interesting, challenging, and enjoyable. Various studies have shown that people who achieve flow at work can be five times more productive than others. You can read more about it in my previous post titled “Achieving Flow with Process Intelligence to Beat the Great Resignation”.
Over the decades shared services have pulled a mix of levers to increase efficiency and productivity but today they can tap process intelligence to get a clearer picture of what can be improved, where and how with insights ranging from processes to systems to productivity.
* Flow: The Psychology of Optimal Experience by Mihaly Csikszentmihalyi, published by Harper and Law, 1990.