While it seems like every second conversation over the past year has been about technology enabling remote working, this has not slowed companies’ interest – and investment – into tech like the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI).
Unfortunately, too many businesses are not realising the value from these investments, says Richard Firth, CEO of MIP Holdings.
“This is partly because these emerging technologies are part of a long-term strategy and are only expected to start impacting on the financials further down the line, and partly because a lot more work needs to happen before they can start delivering on their promises. AI is a perfect example. In the rush to AI, many companies have neglected to put the right foundation in place,” he points out.
“The phrase artificial intelligence actually offers a false promise. The term misleads decision-makers into investing in advanced analytics before their organisation has reached sufficient data maturity. You simply can’t have AI unless you have data, and the right analytics in place, because the AI feeds off the data that informs its decisions.”
He adds that few companies have built mechanisms to store the right data and to store that data in real-time. The intention of AI is to use an up-to-date snapshot of data to potentially add value to a “level” rather than the transaction end state. Thus, companies are not doing real-time, much less achieving real-time AI capabilities.
“Google, Facebook and Amazon have tens of billions of dollars to invest in AI, and even they are still in the very early stages. They are using the data willingly surrendered by their customers to fuel these engines – which is more data than any average business could ever hope to see – and they still don’t have the ability to use AI for more than chatbots, advertising and a few other specific applications.”
Firth is not alone in his opinion. Writing in The Gradient, an online magazine devoted to tech, AI entrepreneur and writer Gary Marcus accuses AI leaders as well as the media of exaggerating the field’s progress. AI-based autonomous cars, fake news detectors, diagnostic programs and chatbots have all been oversold, Marcus contends. He warns that “if and when the public, governments, and investment community recognise that they have been sold an unrealistic picture of AI’s strengths and weaknesses that doesn’t match reality, a new AI winter may commence.”
Another AI veteran and writer, Erik Larson, questions the “myth” that one day AI will inevitably equal or surpass human intelligence. In The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do, Larson argues that “success with narrow applications gets us not one step closer to general intelligence.”
The large multinationals are working on this, but there is still a long way to go, Firth says. Google researchers, for example, have created software that borrows concepts from Darwinian evolution, including survival of the fittest, to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.
“However, this is still all theoretical. Bearing in mind that these organisations have access to most of the world’s consumer data and billions of dollars to keep pumping into research, we have to ask if AI is practical for the average organisation right now. With everything being experimental, what value can a company gain from trying to jump on the AI bandwagon?”
Firth adds that it’s also very difficult to see the value offered by AI in data-sensitive industries. “In banking, for example, it is already challenging to ensure regulatory requirements are met. Financial services institutions already have to look carefully at what data can and can’t be moved across borders, and if AI engines requiring even more data come into play, I don’t see how these types of industries can make use of them unless the AI engines are in-country or on-premise.”
With embedded AI still being far from reality, and with investments into the technology still far from paying off, Firth says that advances in AI are unlikely to be nearly as disruptive–for companies, for workers, or for the economy as a whole–as many observers have been arguing, for a quite long time to come. The key is for every company considering AI to begin the data storage journey or strategy.