How Fast Is Digital Transformation Proceeding? Slow emergence of new technologies says the pace is slow.

jeffrey lee funk
5 min readJun 21, 2021


Digital transformation was supposed to give us higher productivity, a better quality of life, and perhaps higher unemployment. Yet, productivity growth in most Western countries was slower in the 2010s than in any previous decade, and unemployment was at record lows before the pandemic.

Optimists will of course say differently. From The Second Machine Age to The Rise of the Robots, and World Without Work, all award-winning books, optimists cite academic papers from Google and others, supplier announcements, and consulting reports as evidence that an increase in productivity growth and unemployment will soon emerge.

Another way to measure progress in digital transformation is to look at the technologies that purportedly make up this transformation. From the Internet to e-commerce, Big Data, AI, commercial drones, robots, and new platforms such as virtual and augmented reality, the Internet of Things, and blockchain, there are many technologies that are purportedly part of this digital transformation. Rapid growth in their adoption, particularly those just emerging, might suggest the optimists are right.

The current market size for some of these technologies is quite large. E-commerce, smart phones, online advertising, cloud computing, video streaming, and tablet computers had global market sizes of $4.29 trillion, $714 billion, $376 billion, $327 billion, $70 billion, and $40 billion respectively in 2020. The number of active Facebook users had reached 2.7 billion and there are about 2.7 million installed robots of which 293,000 are in the U.S. These are enormous figures and still growing strong, evidence that digital transformation is proceeding rapidly at the global level.

But if we look at the new technologies that have emerged in each decade, a different story emerges. Most observers would consider computing and their electronic components, transistors, and integrated circuits, to represent the beginning of the digital transformation in the 1950s. Mainframe computers began to diffuse in the 1950s and 1960s, mini-computers and robots in the 1960s and 1970s, and personal computers in the 1970s and 1980s, with packaged software for them not far beyond. Bigger changes began to occur in the 1990s as networking equipment enabled these computers to be connected both within and between companies. The Internet was born.

The 1990s was the decade in which construction of the Internet accelerated, giving us e-commerce and enterprise software such as customer relationship management and manufacturing resource planning. E-commerce, Internet hardware, and software, revenues had reached $446, $315, and $282 billion respectively by 2000, all in 2020 dollars to simplify comparisons to subsequent decades. Other new technologies that emerged in the 1990s include web browsers for content browsing and set-top boxes for cable television.

The 2000s were the beginning of rapid growth for smart phones, cloud computing, online advertising, social networking, and e-books. Also in 2020 dollars, smart phones had reached $293 billion in global revenues by 2012, cloud computing $127 billion by 2010, and online advertising $81 billion by 2010. Facebook had 550 million users by the end of 2010 and 23% of books sold online were e-books in 2012. Although tablet computers were first introduced in 2010, they had a market of $70 billion by 2014. For television, America had ended its analog services by 2009 and most programming was compatible with high-definition TV (HDTV) by 2014.

The 2010s by contrast were a decade of growing markets for old technologies, but not for new ones. Although revenues for e-commerce, cloud computing, smart phones, and online advertising continued to grow, only one single category of new technology had achieved $50 billion in sales by 2020, and that was video streaming (See Table 1), which is more applicable to consumers than to corporate digital transformation efforts. The next closest was Big Data/Algorithms with $46 billion and OLED Displays with $32 billion in revenues; the latter is also not usually considered part of corporate digital transformation efforts. Artificial Intelligence, virtual reality, augmented reality, commercial drones, and blockchain have even much smaller markets. Even the Internet of Things had only reached 1/5 the number of connected devices projected in 2012 for 2020 and most of those devices were smart phones.

Big Data and its successor, AI, are the technologies that were supposed to bring us the productivity improvements for digital transformation. Not only are their market sizes still small, they have been heavily criticized, thus suggesting their impact on productivity growth might be even smaller than their market sizes suggest. One of the first criticisms of Big Data’s algorithms came from Cathy O’Neil’s 2016 book Weapons of Math Destruction in which she described the impact of algorithms on people’s lives. The algorithms help criminal justice systems determine bail and sentencing based on a person’s associates and neighbors, companies determine employee schedules whose weekly hours are just short of giving them health coverage, and universities game rankings. Most of these applications involve racial and gender bias, worker exploitation, and advertisements that target low-income people with little self-confidence. In a book with hundreds of examples in more than 10 industries, I could find few examples of a productivity enhancing solution that was appealing.

More recent research is also critical of Big Data and AI. It has found that algorithms decide which children enter foster care, which patients receive medical care, and which families get access to stable housing, and most of the decisions appear to be less effective than the ones previously made by humans. Criticisms of AI are even stronger: algorithms are opinions embedded with code, the foundations of AI are riddled with errors, and achieving higher accuracies requires exponentially rising costs for training. These criticisms suggest that AI and Big Data are not yet good examples of new technologies successfully emerging in the 2010s.

Why is the slow growth in new technologies in the 2010s a problem? Because their future growth depends on the base that was established for them in the 2010s and this base is still not large. E-commerce, enterprise software, online advertising, social networking, and cloud computing now have huge markets because large bases for them were established decades ago in the 1990s and the 2000s, and as a result, they have experienced exponential growth for decades. But without a strong base of growth in the 2010s, it is unlikely the newest technologies will achieve high market sizes by 2030, and thus they cannot have a big impact on productivity by then. This is simply how exponential growth works.

Explaining why the growth in new technologies was slow during the 2010s is a more complex and challenging question, one that must be left to other articles. The point that we wish to make here is that digital transformation has not achieved what many had expected by 2020 and thus productivity improvements will likely take much longer to emerge than technology optimists have thought they would take. If we want higher productivity growth, we need a new plan.