Fads are a constant theme in business. Just as the consumer world gets hooked on tulip bulbs and yo-yos, hacky sacks and Crocs, corporate execs can be suckers for a popular mania. A buzzword gains momentum. Bored journalists ramp up the idea. Pretty soon the concept gains irresistible momentum, and boards fret they are being left behind.
Charlie Munger, Warren Buffett’s investment partner, is a keen observer of the phenomenon. He once noted: “Some years ago one oil company bought a fertiliser company, and every other major oil company practically ran out and bought a fertiliser company. And there was no more damned reason for all these oil companies to buy fertiliser companies, but they didn’t know exactly what to do, and if Exxon was doing it, it was good enough for Mobil and vice versa.”
The result? “It was a total disaster.”
Another case of Fomo?
Much the same thing is happening today in the digital transformation space. The likes of machine learning, Kubernetes and Web3 are generating white-hot interest. According to KPMG’s latest 2022 CEO Outlook survey, 72% of CEOs currently have an “aggressive digital investment strategy, intended to secure first-mover or fast-follower status”.
But the logic is often nothing more than the fear of missing out. “Fomo is totally a thing at an executive level,” says Tom Grogan, founder and CEO of emerging technology strategy consultants MDRxTech. He rattles off examples of poorly thought-through fad-chasing.
“Like so many others, Porsche got excited and launched an NFT project, which went live at the start of the year. It took them so long to launch that they missed the NFT craze by about 18 months. The decision has been widely panned, and the NFTs themselves didn’t even sell well.”
The dafter the fad, the worse the strategy, Grogan observes. “A prestigious Italian fashion house spent tens of millions acquiring land in the ‘Oxford Street shopping experience’ of a metaverse project. In return, they ended up with an Oxford Street shopping experience resembling a post-apocalyptic wasteland with a handful of visitors at any one time.”
Is it wise to be first?
At the heart of these errors is a belief that cutting-edge tech must be superior. New stuff is fun to play with, and there is a social status in being the first. There’s also a sense that innovations will somehow future-proof the enterprise, even if the precise mechanisms are not well understood.
In practice, though, there’s a trade-off between novelty and reliability. New tech will be glitchy, lack a supportive ecosystem, and offer no track record of durability. There’s a reason cash machines still run on Cobol, a language invented in 1959. The code is primitive, but proven.
Will Lion, chief joint strategy officer at BBH London, advocates caution around anything new and shiny. “There’s an inspirational story about the fact you’re the sperm that beat millions of others to get to the egg first. Well done you!” says Lion. “The thing is, it’s rubbish. Any biologist will tell you loads of other sperms died getting through to the egg, desperately trying to be first, only to clear the path for you. There’s a lesson here for digital transformations. We have a fetish for the new. It gets clicks, attention and clients to reply to emails. But new is awful. It’s unproven. It’s complicated. It’s risky.”
Lion says the smarter strategy is to lag behind the early adopters. Let them bear the costs. And capitalise on the lessons learned at their expense. “I’m here to argue that ‘best’ or just ‘working effortlessly’ beats being first by a long shot. Apple has made a whole business out of it. So here’s to being the pioneers behind the pioneers, saving yourself a fortune and turning all their mistakes into gold.”
AI: artificial ignorance?
The explosion of interest in AI is perhaps the biggest fad in digital transformation right now. For all the undoubted promise of the new breed of AI tools, the hype is leading to misapplication.
For instance, judges in the US are now routinely using AI to calculate sentence lengths. An algorithm computes the likelihood of re-offending and makes recommendations to the court. One of the most common algorithms for this purpose, known as Compas, has been criticised for being prone to racial bias. What’s more, it’s not clear whether judges understand the complex algorithms at work. Civil liberties groups such as the ACLU are campaigning to limit the use of AI advice until judges are better able to understand the underlying mechanisms.
Likewise, lots of R&D teams are using ChatGPT to conduct ‘research’, forgetting that the AI engine merely guesses how prompts ought to be answered. ChatGPT is a brilliant inventor – not researcher – hence the creation of ‘hallucitations’, fake academic citations inserted in convincing pseudo-academic bot prose.
And in some sectors, such as recruitment, the hasty application of AI is a potential disaster, says Richard Collins, co-founder of CV Wallet, a CV hosting platform.
“The rapid adoption and use of AI by employers in the hiring process is badly thought out, resulting in bias, inequality and an invasion of privacy,” he explains.
“We see companies using AI referencing at the end of the process. These background checks look more like stalking, as they allow employers to cross reasonable boundaries with a significant invasion of privacy. The AI-powered tools collect vast amounts of data on job applicants without their consent, and often without considering the relevance, accuracy or currency of the information.”
Indeed, our obsession with the latest tech is compounding historic errors, Collins believes. “It has made an already broken system worse. It has resulted in indirect discrimination, which has led to governments around the world moving to regulate the use of AI in hiring. New York City, for example, is banning businesses from screening job applicants with AI-based tools from 5 July.”
How to dodge disaster
Is there a solution? Charles MacKay chronicled mass hysteria in his 1841 work Extraordinary Popular Delusions and the Madness of Crowds. He wrote: “Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, one by one.”
His advice was to be wary of popular obsessions, and stay aloof. Observe, but don’t get sucked in. And if seduced by a fad, be the first to recognise the symptoms.
Digital transformation, however, is a balancing act. The objective is to leap from legacy tech to something more modern. The challenge is to discern the line between what’s a genuinely useful innovation and what’s a trick of the light. Web3, the metaverse or whatever other new tech starts swirling around the hype cycle may capture the imagination of the masses, but this is no guarantee of performance.
Instead, we need to recognise that our judgement is warped by cognitive biases, and that peer pressure can seduce us all. As Charlie Munger himself put it: “I think time and time again, in reality, psychological notions and economic notions interplay, and the man who doesn’t understand both is a damned fool.”