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By intelligent automation I mean enhancing business operations with automated, end-to-end processes and a digitally augmented workforce at scale, underpinned and infused with RPA, AI, and smart process analytics.
Let’s examine this from three perspectives – people, processes, and technology.
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Mention the word “automation” to a workforce, and many of them will immediately worry about their job prospects. In fact, intelligent automation is far more likely to help people do more things, better. Years ago, when offices were ruled by fax machines, sending and receiving business documents meant re-sending the same paper over and over again – each time lowering the quality and spending more time for manual processing. Who misses having to do all that now? How much time would that waste if it were still with us? It’s a minor but clear example of unnecessary friction.
What business needs is people who are equipped to do their best work, and who are motivated to do it. What people need is a job that gives them both an income and a sense of self-worth. Intelligent automation can meet these twin needs, by removing unnecessary distractions, and by improving both the tools and the information available to get things done.
This is why organizations need to start training the workforce of the future now for the roles they are likely to be filling in two- or three-years’ time. If you’d asked people using typewriters to find better things to do than to center a heading, they would have found plenty. Similarly, the modern workforce, supported by automated routines, will be able to develop ideas and resolve problems to a degree that isn’t currently possible.
People will be more efficient because processes will be able to handle more of the repetitive, time-consuming tasks that currently occupy their working day. In fact, as these processes become more intelligent, and more automated, they will grow to become, in a sense, team members in their own right. We might call them a virtual workforce, or an AI workforce, working alongside people. Effectively, they’ll be doing all the non value-adding processing that no one wants to do, but they’ll be doing it automatically and effortlessly.
We’re already seeing this happen. Individual pinch-points are being addressed by robotic process automation (RPA) routines, enriched by artificial intelligence (AI) driven by data. To quickly identify process improvement and automation opportunities, industry leaders employ smart process analytics and management tools such as process and task mining, as well as creating a digital twin for operations – as described by my colleague Lee Beardmore in his previous article.
In addition, as we have seen in the articles on the supply chain and on F&A, process silos are being taken down. In other words, it’s not just discrete activities that are being improved, but entire value chain. The result? Even less friction.
My third perspective is technology, and in particular, of the data on which it acts. In order to create a frictionless enterprise, we first need to define our data estate. In particular, we need to identify the data upon which a digitally augmented workforce can act.
If we can map this data, as well as the processes from which it is derived, we can develop a digital twin of the enterprise – a comprehensive model of operations that we can use offline to notice bottlenecks or, conversely, to spot performance gaps. Are they the result of insufficient human resources? Is the workflow remiss? For instance, it may become clear that processes are incompatible – “the website isn’t in synch with the order management system” – and an automation artefact or a bespoke fix is needed to smooth out the bump. The data on which the digital twin is based will help in the understanding and resolution of issues such as these (read Lee Beardmore’s “Digital twins for business operations” for an in-depth look at the digital twin).
What’s especially useful about the digital twin approach is that the safety it provides can generate the confidence to be creative, and to take risks that wouldn’t be possible with live systems. Companies can create prototypes to solve problems or improve performance, and if those prototypes fail, well, no problem: they fail rapidly, and without consequences.
In fact, companies can even deliberately use digital twins to disrupt the existing set up. For example, adding a new, automated step to a process that already works may seem unwise – but it may deliver benefits to operations as a whole. Could this have been expected? Possibly not. Would anyone have risked finding out on a shop-floor system? Certainly not.
In short, sometimes it’s worth introducing a disruptive friction if there’s a chance it will prevent a larger value generation issue. It’s the tiny piece of grit in the oyster that produces the pearl.
I’d encourage you to read the other articles in this series. In the meantime, you might be interested to know that Capgemini has been named a leader in the intelligent automation of business processes in Everest Group’s PEAK Matrix™.
Marek Sowa helps clients to transform their business operations leveraging the combined power of AI and RPA to create working solutions that deliver real business value.
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