Another day, another new technology grabbing the limelight. This year alone, we have seen Machine Learning, Neural Networks, Blockchain and Cryptocurrencies each vie for centre stage. And now, with the new fashion for AI specialists predicting when certain jobs will be replaced by robots (and always predicting that they will be last!), it looks as if all we need to do is strap ourselves to technology and let it pull us to a wonderful future. But is tech really all it takes for success?
In the face of AI, Machine Learning and the like, it helps to remember the plight of laserdisks, Betamax video recorders, minidiscs and jazz drives. All these represented technological advances. Each represented a step change in thinking and an improvement on the competition. All of them ended up on the junkyard of history, right next to PDAs and the portable CD player.
Whatever engineers might say, technology on its own is never enough to bring real change. Pity the poor company with a brand new product, only to see its competitors come out with a cheaper and less reliable version that has the face of a celebrity on the side of it. Few investors will plow cash into a concept that works wonderfully but requires large enterprises to convert wholesale from their existing legacy systems.
For technology to make a difference, it must be adopted. For it to be adopted, people must be convinced of its benefits. For people to be convinced, someone in the company selling the product has to have a good grasp of how people think and how they behave in groups.
Behold the power of social science. Like fine chocolate, social science comes in many flavours: from the heady delights of statistical demographics to the tempting subtlety of autoethnography. What all of social science shares in common is a commitment to study and understand people.
Historically, the biggest war within social science was the split between those who preferred quantitative studies with large data sets and complex statistics and those who preferred qualitative studies, which look more at individual and small group experience. Apart from a few dusty corners, that war is now over and the winner is: both sides. Yes, most social scientists will now tell you that, if you want to understand people you need both the big statistical data and the small group/individual perspective.
There is a lesson there for technology geeks, especially those fascinated by the power of Big Data. Statistics and data are powerful but personal experience and subjective ideas matter too. If your business is going to thrive, you will have to do more than harness big data; you will need to know how to persuade, encourage, serve and delight people.
It’s no wonder then that the highest converting marketing media are still face-to-face contact and word-of-mouth recommendations. Even with the rise of social media, we humans still love to look people in the eyes and spend time with them before we hand over our hard-earned cash. An in-person recommendation from a trusted friend will always carry more weight than a targeted Facebook advert.
The reality is that few new technological products live up to the utopian marketing created by their inventors. Those translation earbuds that function perfectly on that YouTube video will struggle in a crowded bar or when you can no longer be bothered to speak in a mechanical monotone and there are no producers around to ensure everyone behaves predictably. That shiny new tablet will run at breakneck speed … until you actually load your favourite apps onto it.
Technology is wonderful and, if used well, it can make real contributions to productivity and profit margins. But for those gains to be realised, you need social skills like leadership, trustworthiness and persuasion. For your business to succeed, you will need a social scientist’s head for understanding people and their predilections much more than you will need an engineer’s eye for a new piece of kit that could possibly be created.
By all means enjoy technology but put knowing and serving people first and you won’t go far wrong.