Information technology that performs tasks that would ordinarily require biological brainpower to accomplish, such as making sense of spoken language, learning behaviours, or solving problems.
The European Commission (2018, para.6), on the other hand, (having gone through some minor revisions in the past few years) has a somewhat lengthier definition:
Artificial intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions â with some degree of autonomy â to achieve specific goals.
AI-based systems can be purely software-based, acting in the virtual world (e.g. voice assistants, image analysis software, search engines, speech and face recognition systems) or AI can be embedded in hardware devices (e.g. advanced robots, autonomous cars, drones or Internet of Things applications).
Naturally, other variations may occur in different countries around the world. Interestingly, while the Canadian definition stresses the complexity of the system in assisting humans with chores, the European definition places more emphasis on the intelligent and autonomous design and behavior of the system. In this way, the European definition of AI accounts for the possibility of an evolution of the behavior of AI, in a way that follows technological advancement. That is to say, AI is not a âfixed constructâ, and to that end, this definition better encompasses the relevance of AI in the scope of this book.
It is, in this context, appropriate to also mention the terms âdigitizationâ, âdigitalizationâ and âdigital transformationâ, as they have become frequently used âbuzzwordsâ in many different businesses. However, the terminologies are sometimes erroneously used interchangeably. The first term, âdigitization, entails the conversion of analogue material (such as images, video and/or text etc.) into a digital format (Larsson and Viitaoja, 2017; Feldman, 1997; Brynjolfsson and McAfee, 2014). The second term, âdigitalizationâ, refers to a process wherein the use of digital/computer technology (also mobile applications) is adopted, alternatively, increased by an actor (Wachal, 1971; Castells, 2010). More often than not, the digital technology implemented with the intent of establishing a communication infrastructure that connects various activities of the actorâs various processes (Van Dijk, 2012; Larsson and Viitaoja, 2017). âDigital transformationâ, on the other hand, is a considerably broader term that signifies customer-driven strategic business transformation requiring far-reaching and cross-cutting organizational change in addition to the implementation of digital technologies (Bloomberg, 2018; Cochoy et al., 2017). Due to its scope, digital transformation is in reality not a matter of implementing one project, but rather a whole series of different projects, effectively necessitating the organization to deal better with change overall. In this way, digital transformation in and by itself essentially makes organizational change a core competency inasmuch that the venture seeks to become customer-driven end-to-end (Bloomberg, 2018).
For this reason, âdigitalizationâ and âdigital transformationâ are the two most useful/significant terms when explaining the changes and impact that digital technology has had on society at large. That is to say intelligent algorithms make our day-to-day tasks easier, and it is in many cases near impossible to imagine how we could manage without them. The use of AI and robotics continues to gain momentum at a rapid pace, which prompts the question as to what the future of labor will look like once fully evolved. Extant literature suggests that digitalization has opposing effects on labor markets and that as such, it is still not clear what effects a digitalized society will ultimately have on the labor market (BĂŒhrer and Hagist, 2017). Will mass unemployment, poverty and social distortions be a given consequence of this development or may there be a different outcome?
This book will seek to explore these issues and many more through a series of different studies by scientists and industry professionals from Europe and the United States, with deep insight into their respective areas. It is true that the chapters in this volume are to a large extent inherently based on a speculative and/or predictive premise, given the fact that much of the digital transformation is still happening and is nowhere near completed and/or optimized. However, while the authors have sought to interpret near- and far-future developments, they have availed themselves to uphold scientific rigor by following proper academic protocol. This means using citations and basing their point of departure in extant issues/problems and undertaking due analytical procedure and research rather than conveying conjecture or personal opinions. As such, the chapters offer an array of methodological and thematic studies, with some studies presenting original, empirical material while others are more theoretically rooted, with some additional chapters basing their foundation on various forms of literature reviews or departing from the authorsâ personal, âbest practiceâ experiences. To this end, the studies covered throughout the different chapters have based their assumptions in referenced facts, but while doing so, the studies may at times also transcend the conventional academic comfort zone by offering some foresight in how their subject area could transpire based on the current and expected developments due to digitalization and digital transformation.
The overall premise of this book takes its point of departure from a 2016 OECD report that targets the rapid structural transformations that have followed the digitalization process throughout the OECD countries (Berger and Frey, 2016). Specifically, this report lends support to the aforementioned academic notion that the impact digitalization has on the future of labor is ambiguous. That is to say that there is accumulating anecdotal evidence suggesting that the potential scope of automation has expanded beyond mere routine work, which would make technological change potentially increasingly labor-saving. On the other hand, there is evidence suggesting that digital technologies have not created new jobs on a scale that it replaces old ones.
Adding to this, an additional 2018 OECD report indicated that digitalization and automation as such does not pose a real risk of destroying any significant number of jobs for the foreseeable future (Nedelkoska and Quintini, 2018). Nevertheless, the report did contend that tasks by and large would change significantly, which in turn affects welfare, as most of its revenue stems from taxation, and particularly so from the taxation on labor (be it directly or indirectly). Taking its point of departure from the findings uncovered in these reports, the structure of this book seeks to explore some overarching themes in which digitalization and digital transformation can be expected to impact the labor conditions to some degree or another. The themes investigated are as follows:
