Expert systems, neural networks, and predictions of generalised intelligence are everywhere. Venture capitalists are rushing to fund start-ups with the magic letters “AI” grafted on to their PowerPoint presentations. Consultants and chief executives endlessly proclaim the need to embrace artificial intelligence and its sibling “ big data”.

We have seen this movie before. Fifty years ago, “chatterbots” such as Eliza and Parry were heralded as victories on the march to generalised intelligence. Complex neural networks were considered primitive but reasonable models of human brain functions. And on the back of intense interest in AI, Ridley Scott’s 1982 film Blade Runner projected a dystopian future in which artificial lifeforms exceeded human intelligence and strength. Surely humanity was about to give birth to a digital Adam and Eve?

Of course, it did not happen.

We have not moved a byte forward in understanding human intelligence. We have much faster computers, thanks to Moore’s law, but the underlying algorithms are mostly identical to those that powered machines 40 years ago.

Instead, we have creatively rebranded those algorithms. Good old-fashioned “data” has suddenly become “big”. And 1970s-vintage neural networks have started to provide the mysterious phenomenon of “deep learning”.

But didn’t IBM’s Watson beat human contestants on the television show Jeopardy? Surely that’s revolutionary?

More to the point, however, is Watson’s commercial success. It was reported by Stat that IBM internal documents indicated that Watson had recommended “unsafe and incorrect” cancer treatments. It turns out that going from Jeopardy to cancer diagnosis is a bit harder than IBM thought.

Another contender for the AI crown is Google with DeepMind. In a salute to IBM’s strategy of throwing hundreds of engineers at flashy victories in deterministic games, DeepMind trumpeted the triumph of its AlphaGo program over the world’s top player of the ancient strategy game Go. This was somehow supposed to be an even greater victory than those of Deep Blue, the chess-playing computer that beat Garry Kasparov in 1997, or Watson.

But let’s consider a case where DeepMind has attempted to extract commercial value. With much fanfare, the UK’s National Health Service employed DeepMind in 2015 to improve patient outcomes and reduce costs. Three years later, an audit of the DeepMind project for acute kidney injury, known as Streams, conducted by the law firm Linklaters said: “Without intending any disrespect to DeepMind, we do not think the concepts underpinning Streams are particularly groundbreaking.” In short, DeepMind is providing nifty graphical interfaces to existing NHS algorithms.

The fact is that the latest incarnations of AI are not much better at solving real world complex problems than their ancestors were three decades ago. Heavily-tuned systems can win at narrow challenges such as Go, chess, or Jeopardy. But diagnosing a disease from the myriad and conflicting symptoms a human being can exhibit is currently beyond them.

So what is AI good for? What it has always been good for: the identification of patterns in complex data. Medical image anomaly detection, hydrocarbon detection, consumer behavioural prediction and fraud detection have all benefited from advances in computational capacity. These all share two things: large volumes of well-structured input data and well-defined endpoints.

How could companies benefit?

First, we should stop investing in generic AI and big data projects. Consultants love these, but the vast majority of such investment is a waste. The question we ask ourselves at my technology company, Afiniti, is this: “Can the CEO understand in five minutes or less how we add value?” If there is not an exceptionally clear business case for the pitch, then it is almost certainly not worth pursuing.