There’s a good chance that if you’re reading this, you either graduated in a STEM subject, or you know people who did. Some of those people may also have masters’ degrees; some of them, who knows, may have gone further still, and hold PhDs
But does that mean that you, or they, would by default be a great practitioner of science, or technology, or engineering, or maths? Well, it might. But it’s by no means guaranteed. We’ve all met bright people who find it difficult to apply their knowledge and, sometimes, even to communicate it.
I’m reminded of this when I think about the application of artificial intelligence (AI) to business operations. The extent to which AI can now master natural language, understand audio, video, and images, on top of learning the logic of processes, is truly remarkable. What’s more, it can take all this new-found knowledge of data and business routines, and analyze and interpret it all at a speed and at a scale that is simply beyond the scope of us mere mortals. With technology like this, who needs people?
AI and people – we need one another
The answer is: artificial intelligence does. It’s the AI itself that needs people (at least for now). It may be able to accrue a huge fund of knowledge and understanding, and in many cases, it may even be able to apply it effectively and independently. But it won’t be able to do this all the time, because its often-remorseless logic isn’t always applicable to the diverse and real-world situations of business operations. It needs people to bring it down to earth – just as those bright academics might find themselves out of their depth when they’re out in the field.
Naturally, all the knowledge and analysis AI can muster is useful to people. It enables them to make better, faster business decisions. But the relationship is symbiotic, because the AI benefits, too. It can, for instance, observe how people interact with applications and automatically generate deterministic robots to deliver the information they need, when they need it. It can watch how people learn, and how they interact with one another, so it can tailor its own inputs accordingly. It can even observe processes of which it has no previous knowledge or experience, and infer the rules for itself – and then, it may be able to go further, and suggest improvements, drawn from its own broad frame of reference.
Everything is simplified, everybody wins
Many years ago, a global telecoms company embarked on a work shadowing exercise. People at all levels of the organization spent time alongside colleagues in other parts of the business. Even the chief executive, who was assigned to shadow a field engineer in the city in which the company was headquartered.
On the first morning, the chief exec met the field technician in the lobby. The engineer picked up his toolkit, and they stepped outside.
“Where are you parked?” asked the CEO. “Where’s your van?”
The engineer was puzzled by the question. “I don’t have one,” he said. “My patch is right here. The financial district. A van would be a liability. I walk everywhere I need to go.”
The chief executive learned something that day about how parts of his business operated – just as AI learns by observing people in action. Everything is simplified; everybody wins.
In a future post in this series, I’ll take a look at practical examples of this symbiosis between people and AI, and at the benefits that can result. In the meantime, you might like to visit the page I’ve contributed to on this subject In Capgemini’s TechnoVision 2020 – see Augmented Me.
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Lee Beardmore has spent over two decades advising clients on the best strategies for technology adoption. More recently, he has been leading the push in AI and intelligent automation for Capgemini’s Business Services. Lee is a computer scientist by education, a technologist at heart, and has a wealth of cross-industry experience.