moving into a new era where algorithms are developed for each task; what kind of influence it will have on how we will run our organizations in the future; and how we should best approach such radical transformation.
The time machine is waiting, but this time with the aim to inform us and make us smarter about the ways in which we can design technology to improve humanity.
Chapter 1: Entering a New Era
In 1985, Mark Knopfler and his band Dire Straits released a song about a boy who got the action, got the motion and did the walk of life. This boy became the hero in many a young kid’s fantasy. In the 21st century, we have another kind of hero, something that is not human. Now, we admire the use of algorithms in all walks of life.
However, it is also important to note that AI is not some new phenomenon that has only arrived in the last few years. In fact, the notion of AI was used for the first time in 1956. At that time, the eight-week long Dartmouth Summer Research project on AI at Dartmouth College in New Hampshire was organized. The project included names like Marvin Minsky, John McCarthy and Nathaniel Rochester, who would later become known as the founding fathers of AI.
So, early on in the second half of last century, the belief in the super power of AI was already very much present. Consider, for example, the quote of Herbert A. Simon, Nobel laureate in economics, who wrote in 1965: “machines will be capable, within 20 years, of doing any work a man can do.” However, researchers failed to deliver on these lofty promises. Since the 1970s, AI projects have been heavily criticized for being too expensive and using too formalized, top-down approaches which fail to replicate human intelligence. And as a result, AI research was partly frozen, with no real progress being made. Until now!
AI witnessed a comeback in the last decade, primarily because the world woke up to the realization that deep learning by machines is possible to the level where they can actually perform many tasks better than humans. Where did this wake-up call come from? From a simple game called Go.
In 2016, AlphaGo, a program developed by Google DeepMind, beat the human world champion in the Chinese board game, Go. This was a surprise to many, as Go – because of its complexity – was considered the territory of human, not AI, victors. In a decade where our human desire to connect globally, execute tasks faster, and accumulate massive amounts of data, was omnipresent, such deep learning capabilities were, of course, quickly embraced.
As a result, we are now witnessing an almost obsessive focus on AI and the benefits it can bring to our society, organizations and people. This obsessive focus, combined with an exponential increase in AI applications, has resulted in a certain fear that human intelligence may well be on the verge of being challenged in all facets of our lives. Or, to be more precise, a fear has emerged in society that we, as humans, may have entered an era where we will be replaced by machines (for real, this time!).
However, before we address the challenge (some may even call it a threat) to our authentic sense of the human self and intelligence, we need to make clear what we are talking about when we talk about AI. Although the purpose of this book is not to present a technical manual to work with AI, or to teach you how to become a coder, I do feel that we first need to familiarize ourselves with a brief definition of AI.
In its simplest form, AI can be seen as a system that employs techniques to make external data – available everywhere in our organizations and society – as a whole more transparent. Making data more transparent allows for interpreting data more accurately. This allows us to learn from these interpretations and subsequently act upon them to promote more optimal ways of achieving our goals.
The technique that is known to all and drives our learning from data is called machine learning. It is machine learning that creates algorithms that are applied to data with the aim of promoting our understanding of what the data is actually saying. Algorithms are learned scripts for mathematical calculations that are applied to data to arrive at new insights and conclusions that we may not directly see. Specifically, they allow us to arrive at insights that can help us to develop more comprehensive and more accurate predictions and models. Algorithms act in autonomous ways to identify patterns in data that signal underlying principles and rules.
As you can easily see, algorithms are not only useful but powerful tools in a society interested in continuously improving and enhancing knowledge. Indeed, algorithms are en route to serve such an important function to how we act and live in society that they will be as much part of our social and work lives as other human beings. In other words, the ability of algorithms to analyze, work with and learn from external data, means that algorithms today have reached a level where they can interact and partner with the outside (human) world.
The rise of algorithms in organizations
When you look around today and see what excites people about the future, it quickly becomes clear that the influence of our new hero (the algorithm in action) is rapidly growing, especially in domains where the potential for realizing significant cost savings is high. One such domain concerns our work life, where algorithms are increasingly becoming part of how organizations are managed.1 Although it may be a scary development for some of us, there are good reasons why algorithms are applied to a wide variety of problem-solving operations.2
Let us first look at the economic benefits. Current estimates show that the application of AI in business will add at least $13trn to the global economy in the next ten years. In a recent report by PwC, it was predicted that using AI at a larger scale – across industries and society – could boost the global economy by $15.7trn by 2030.3,4
Why do we expect AI to contribute in such enormous ways to the global economy? Mainly because algorithms are expected to have an impact on how businesses will be managed and controlled (as indicated by 56% of interviewed managers by Accenture) and therefore will facilitate the creation of a more interesting and effective work context (as indicated by 84% of managers interviewed by Accenture).5,6 This enhancement in effectiveness will ensure economic growth. Indeed, surveys worldwide indicate that the adoption of algorithms in the work context will help businesses to promote the fulfilment of their potential and create larger market shares.7,8
For some, these numbers have been used to suggest that algorithms represent steroids for companies wanting to perform better and faster. It is nevertheless a reality that companies today are developing new partnerships between machines and AI on one hand, and humans on the other hand. Developing and promoting this kind of partnership also has an important implication for humankind. It is likely that the new technology, available to push companies’ productivity and performance to a higher level, is bound to steadily take more autonomous forms that will enable humans to offload parts of their jobs. Importantly, this development is not something that is likely to happen tomorrow. In fact, it has arrived already. AI is developing so fast that an increasing number of machines are already capable of autonomous learning. In reality, AI has achieved a level of development that makes it capable of taking actions and making decisions that previously were only considered possible under the discretion of humans.
If this is the case, then it is no surprise that the availability and possibility of implementing intelligent machines and their learning algorithms will have a significant impact on how work will be executed and experienced. This reality is hard to deny because the facts seem to be there. As mentioned earlier, Google’s DeepMind autonomous AI beat the world’s best Go-player, and recently Alibaba’s algorithms have been shown to be superior to humans in the basic skills of reading and comprehension.9
If such basic human skills can be left to machines and those machines possess the ability to learn, what then will the future look like? This predicted (and feared?) change in the nature of work will be seen across a broad range of jobs and professions. It is already widely accepted that automation of jobs in the business world is happening. For example, algorithms are being employed to recruit new staff, decide which employees to promote, and
