As Malcolm Gladwell reminded us at the World Business Forum

There is a time to innovate and a time to reap success. History, both recent and less recent, is full of examples of great innovations that had to wait years, if not centuries, to become successful. Why was this, though? Malcolm Gladwell, journalist, sociologist and storyteller, has a theory: because the culture and maturity of society and organisations do not always coincide temporally with innovations.

For example, let’s consider the phone. It was not clear initially that it would become a means of mass communication. In the early days, phones were used solely to report emergencies or perhaps to check the presence of a product in a particular shop or warehouse before travelling. Back then, no one considered that telephones could be used to chat.

How about the ATM? Those have been around since 1967, but it was at least 20 years before most of us started to use them. Most people did not see the necessity of getting cash while out and about. The bank was the place to get cash, and going to the bank was part of life.

In other cases, however, there are those who had the right idea at the right time.

Examples of such entrepreneurs include Ingvar Kamprad, who founded a furniture empire with a sales model requiring customers to assemble their own products, making them both cheaper and easier to transport. And what about Malcolm McLean, who in the 1930s developed multimodal logistics based on containers, a revolutionary model of transport that we now take almost completely for granted. 

Malcolm Gladwell’s stories and ideas led me to think about current innovations, and particularly artificial intelligence. This is definitely an innovation with a long history, and currently experiencing a strong moment, if not a moment of hype. There is no doubt that technology is moving forward in that field and AI can be disruptive. We can, however, see at least in part how AI could be useful to us as individuals and to organisations and companies. But – only in part. We seem to be talking about AI more than we are actually using it. Why?

Maturity

Here’s where Malcolm Gladwell comes in. Perhaps it is because we are not yet ready. We are not yet mature enough to capitalise on the full potential of AI. After all, biological, cultural and social systems evolve at a much slower speed than technology. In the case of AI, the cultural transformation requires epochal paradigm shifts. Deep learning requires that systems learn from information, modify their state and their neuronal layers, and give answers that are not necessarily the same for every use, precisely because learning has potentially different paths. It is not replicable and difficult to reconstruct. It also extends into the field of probabilities; the answers we get from artificial intelligence are not unique and absolute, but they provide levels of accuracy. This is difficult to accept for people and organisations that for decades have relied on spreadsheets, relational databases and ERP.

SAS, Intel and Accenture, working with Forbes Insights, surveyed business leaders and interviewed thought leaders around the world to identify early adopters and uncover their emerging best practices for AI. Download the report here.

The change is epochal, a bit like the transition from classical to quantum physics. It is a move from clear formulas and unequivocal results to probability and indeterminacy. Einstein himself struggled to accept quantum physics at first. The good news is that just as classical mechanics and quantum physics coexist, so too can traditional systems and AI. What’s more, AI can actually augment traditional systems.

So how do you change the paradigm and accept the challenges of the AI transformation?

You have to address enablement paths, or paths that aim to mature the different dimensions of change at the same time. In particular:

• Awareness, learning and commitment, where people and organisations, at all levels of decision making and operations, become aware of and determined to face the challenge.
• Technology enablement, to allow organisations and individuals to exploit the potential of AI ​​and analytics in a broad way through the adoption of a unifying analytical platform.
• Co-creation, because it is only with open innovation and multidisciplinary collaboration that we can fully grasp the benefits of AI and customise it to obtain a real competitive advantage.

Turning AI into a successful innovation will require all these, but I think particularly co-creation: working with our customers to make it a reality.