There’s a new wave of work automation now starting to impact businesses. Yes, partly this is enabled by Machine Learning technologies and techniques: but there’s more to it. The new wave of automation brings advances in interaction, insights and integration. This report lays out the landscape.
Written by MWD Advisors’ lead analysts Angela Ashenden, Neil Ward-Dutton and Craig Wentworth, this report is the first of a two-part series.
The new wave of automation in business is about more than machine learning. The ‘mainstreaming’ of learning systems techniques is enabling lots of experimentation and many new tools and applications; however there are three distinct layers in the new wave of automation that’s now washing over businesses, and learning systems are only the major factor in one of these layers.
You need an automation strategy. The new wave of automation is bringing many new low-cost, self-service tools into the picture for your business, as well as more complex tools and platforms that may have the potential to radically affect your business processes and operating models; without a clear strategy for how you’re going to engage with new automation tools and approaches, you’ll come unstuck very quickly.
When creating your strategy, your unit of analysis and change has to be tasks (not jobs). It’s easy and tempting to assume that automated systems will substitute for human jobs but the much more accurate way to think about the impacts of the new wave of automation is to think about how different kinds of automation tool and technology have the potential to impact particular tasks in your business.
Prioritise for the biggest impact. Go for either the high-volume tasks or high-value tasks. The other areas will take care of themselves as innovation ‘trickles down’.
Think beyond the technology. Even learning systems need to be trained, and in many cases you’ll need to develop and apply some specialist skills to really get the most out of the technology. You have to thoroughly understand the business domain you’re working in, the nature of individual tasks, and have a very intentional approach to how you insert the technology into work. Domain knowledge in particular is crucial because – for the time being, at least – even the most sophisticated AI needs boundaries within which it can do specialised work.
A new wave of automation
In the past two years there’s been a major uptick in announcements of new ‘intelligent’ services and systems available to both businesses and individual consumers. If you’ve not been exposed to discussion of the capabilities of Apple’s Siri or IBM’s Watson in the media, for example, you’re in a very small minority. These are just two examples at the forefront of a wave of technology and product advances.
It might seem like automation in the workplace is a relatively new phenomenon, but in reality it’s been going on for over 200 years. In 1785, the first automated process, in flour milling, was implemented; 1892 saw the first automated telephone switchboard being used.
Until relatively recently, the waves of automation that have washed over businesses since the late 1700s have shared an important common feature of compromise: we’ve had to deliver and operate automated systems within very tightly-configured and controlled environments. This is obviously true in the context of physical process environments like assembly lines, product packaging lines, automated warehouse picking systems and so on; but it’s also true in the context of software-driven processes that are heavily automated – like trade settlement in banking, service activation in telecoms and bill production in utilities, for example.
So in a sense, the history of automation design is as much about the design of the environments in which automation occur as it is about the design of automations themselves. Think about automation in manufacturing processes and production lines; think about early business systems with crude ‘operator interfaces’. These systems required us to shape our work around the needs of the automation.
In 1959, though, a new course began to be set with the introduction of the first learning program (a chess-playing program invented at IBM). Now, helped by advances in learning systems techniques, a huge shift in the economics of computing and digital storage and changing expectations around the ease-of-use of software systems and services, we’re finally seeing the transformation of this relationship: technology systems are starting to be able to mould themselves around us and our work, and the interfaces they present to us are becoming more and more flexible, natural and intuitive.
What’s being created here is a new wave of automation that will have profound impacts on how people accomplish tasks in the workplace, and on how businesses arrange, distribute and manage work.
The new ‘smart components’ that are beginning to impact business today – chatbots, ‘intelligent’ assistants, and so on – are not really about automation of work or processes in the traditional sense that we’re used to. Yes, the new technology capabilities now entering workplaces are delivered by software that ‘automates’ certain activities; but the endgame and experience is not one of a completely self-contained automated system that works in isolation on an end-to-end business process; but rather of software agents of various kinds working alongside people to augment particular work tasks and processes.
What’s behind it? Machine Learning and more
Advances in Machine Learning (ML) software techniques are the big headline-grabbers that are contributing to the creation of this new wave of automation, but that’s not all that is happening in the technology space – for example, innovation and interest in ML is being accelerated by, and in turn is further feeding, rapid development of big data management and predictive analytics technologies, and the use of these to drive activities and business processes.
What’s also important to recognise is that many of the underlying technologies powering the new wave of automation are not new. Pioneers like Nuance (speech recognition and transcription, speech generation), SAS Institute and SPSS (predictive analytics) have been developing and delivering key component technologies for 20 years.
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