"Robotic process automation takes the robot out of the human. Intelligent automation and AI try to put the human into the robot. Digital transformation attempts a whole organization change founded on emerging digital technologies. The difficulties rise exponentially across these endeavours.”
Leslie Willcocks
Professor Emeritus
London School of Economics and Political Science
Introduction
Our new book, Maximizing Value with Automation and Digital Transformation: A Realist’s Guide, aims to provide a realistic and reliable guide to planning and deploying successfully the digital technologies that will improve the performance of businesses. Selecting the technology turns out to be the (relatively) easy part. Putting it to work and gaining full value from it is anything but.
To offer such a guide in a market characterised by contested claims, false starts overstated expectations, and underestimated difficulties seems to us to be a useful and timely activity. We bring to the project expert insight into the ways in which transformative technologies gain traction in the world, and work from a strictly evidence-based perspective.
Over the next few months, we will provide an introduction to the book, but in a relatively novel way. During the last year I have conducted many interviews with journalists, magazines, online think tanks, and academic journal editors, who have asked for summaries of research, perspectives on emerging issues, and predictions of how things are likely to turn out over the year, and the next five to ten years. Generally, the major focus has been automation and digital technologies, with relative neglect of issues like management and organisations, except when the subjects of job loss and skills shortages arise. Business value also seems to escape attention, partly because, as we often find out, few people, including businesses themselves, actually monitor this carefully.
In a series of blogs, I am going to provide a composite of the questions asked, that takes into account the full range of questions—not those just asked most frequently. The result is a strong overview of the book’s on-going findings, making up its first chapter, and a provocative but valid, and hopefully easy-to-read introduction to the other twenty-one chapters of the book
Blog 1 – Misunderstandings
Most people know the headlines. But are they right? What are the top misunderstandings about automation and digital transformation circulating in the media?
There are all too many! But we will limit ourselves to three:
The first is the hyperbole about artificial intelligence. ‘AI’ is such a useful shorthand is it not? But it’s also very misleading. Somebody observed that “if it’s intelligent it’s not artificial, and if it’s artificial it’s not intelligent”. That’s correct, but you could also say that it’s not even artificially intelligent—much depends on how you abuse the word ‘intelligent’!
The area is pervaded by the seductive metaphor that computers are like brains and brains operate like computers. And, of course, technology companies and the media ramp up the rhetoric to suggest that there is a lot more in the technology than there really is, or likely to be any time in the next 15 years. At base what we have is machine learning; algorithms; natural language processing; image processing backed by traditional statistics; and, really, the two key developments—impressive and growing computing power and memory. This can produce hyper-speed and impressive results for very limited applications. But there is no general-purpose intelligence. It is ‘weak, weak AI’. Of 18 sets of skills used at work several studies including our own found only eight fully automatable. Humans have eight distinctive capabilities and composite skills (the automatability of three further sets depend on context and use), and these human skills are increasingly valuable because they are unlikely to be replicable in the next 15 years, if at all.
Secondly, having experienced and worked with information, communication, and now automation and digital technologies since the 1980s, we at Knowledge Capital Partners are still surprised at how people believe that a tsunami of automation will slip easily, seamlessly, and at great speed, into our work organisations—for good or ill. That is not at all how it seems to work. Generally speaking, when technology hits an organisation, strange things happen. The technology is rarely seamless. Even so, in our experience, 25 percent of the challenges are technical and 75 percent are organisational and managerial. The easiest way we have found to communicate this is to talk of the eight-siloed organisation. The silos that inhibit the free flow of data, information and knowledge, and application of technology are: culture; process; legacy technology; data; strategy; skills; structure; and the big one—managerial mind-sets. A major reason these silos exist is where organisations are structured in business divisions and functions that have become self-contained over time. Most organisations are, even today, struggling with going digital. If you ask them how they score on these silos from 1–10 (with 10 being ‘very siloed’), most, even today, will have a significant number against each of these eight areas. And there you have some key reasons why automation and digital transformation are experienced by so many as so challenging.
Interestingly overlooked, but a real trip wire for going digital, is data. Actually, we find 80 percent of organisational data is usually semi-structured or unstructured and not that usable. Usable data for automation technologies may be as little as 15–20 percent. Then you hear about the wonders of ‘big data’; it has been nicely said that the dirty secret of big data is that nearly all business data is dirty. For example, it comes preloaded with biases and it’s frequently not in a form that is usable, or that you can compare with other data. Given the statistical basis of many algorithms that depend on such things, getting a random sample is much easier stated as a principle than delivered on in practice. All in all, the point is that the data challenge has to be faced before the technological and organisational ones, and the data challenge is far from trivial for most working businesses, let alone something like a major government department like tax or social security, or, in the UK, the NHS (National Health Service).
Even assuming that the organisation has the capabilities to manage the technology into the organisation, you can see that these silos create a very big set of challenges to effective deployment.
Allow us one more. The third misunderstanding relates to the idea that technology is no longer a specialism, needing specialist knowledge and experience. In practice people have been discounting the Information Technology (IT) department since the 1980s. A Sloan Management Review paper in 1985 basically said we were all going to become our own technologists, that the technology would become simpler to operate, and our knowledge would be much greater as well. Farewell to the IT function. That has not happened. The systems are more connected, more invisible, more complex than ever before, and we have become much more reliant on technological experts. When deploying automation technologies, very little can be done at scale, strategically, unless it fits very meticulously with the technology platform of the organisation—its governance structure, protocols, security processes, and technical architecture. The risks of not doing this are now so much greater. The era of the ‘citizen technologist’ has not arrived. Pointing at the mobile phone as an exemplar of modern user-operated technology is simplistic because it ignores the enormous amount of technology that has to be in place to make that work so seamlessly.
From Outsourcing to Collaborative Innovation
On our strict definition outsourcing is the handing over of activities, assets and/or people to third party management for required result. This is the specialist focus mentioned above. Relying on third party management is inherently risky, so a key question becomes: how do you mitigate the risks? Over many years Mary Lacity, Sara Cullen and I found that IT and business process outsourcing works most effectively for well understood, stable, commodity type activities, on 3–5 year contracts, following a ‘horses for courses’ multi-vendor approach. This is not currently what many activities involve, and software development has been most frequently an area where outsourcing. Strictly defined, really was not a good model to follow. But there are many ways to leverage the external business services market, including buying-in resources to work under internal management, building long-term partnering relationships with key suppliers, Build-Operate-Transfer models, and business partnering eco-systems as described in this book. At the base of all these is the business/user approach detailed above. This also runs through the contemporary development of global business eco-systems. As CEO of an advisory business in such a technology network, Andy Hilliard writes:
Next month’s blog will look at the impact of automation on the global workforce. What is the evidence saying about the impact of automation and digital technologies on the labour force?