The title of this chapter may be provocative to some as it suggests radical change driven by data and AI are leading a revolution in how organisations will operate in the near future. Caution over analysts and pundits calling technological advances ‘revolutions’ is wise (as we shall see in Chapter 5) and there is no shortage of examples where irrational exuberance has got the better of writers and forecasters. As the management guru, Peter Drucker, is quoted as saying, ‘Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window’ (Guber, 2014). This is particularly true with trying to forecast how new technologies will evolve and whether they will succeed in the marketplace. Established technology vendors may have a vested interest in playing down the significance of a new innovation developed outside their own company, while businesses launching new products may go in the opposite direction.

Over-confidence in the existing order was demonstrated in 1876 when the president of the telegraph giant, Western Union, was offered the patent on the telephone for US$100,000 and stated, ‘This “telephone” has too many shortcomings to be seriously considered as a means of communication’ (Yeuh, 2019). Less than 30 years later, his company was bought by telephony ‘upstart’ AT&T. On the other side, we have WeWork, once valued at US$65 billion by Goldman Sachs for its innovative use of technology to revolutionise officeletting but sold for approximately one hundredth of that figure a few years later (Neate, 2019; Sherman, 2024). Hubris and a confused business model were to blame there.

While acknowledging the difficulties of predicting technology adoption curves, the contention of this book is that we are at the start of a revolution in business practices driven by data and AI. Chapter 5 will explore how this may take shape over the coming five to ten years. This chapter examines a number of current developments and how organisations are already experimenting with and deploying data-driven AI services.