By John O’Neill
What triggers buyer’s remorse? They say some people feel it within seconds of driving their brand new car off a lot. For me, it sets in as soon as the honeymoon period with my new flat-screen television is over, and I realize I shouldn’t have fallen in love with the first one I laid eyes on. We’ve all felt it, but that doesn’t make it less painful.
Fifteen years ago, I invested over $20K in new office software, only to watch a completely revamped -and cheaper – release render it totally obsolete just two months later. That still sticks in my memory and makes me cagey about software purchases, especially in a professional setting where these decisions can have career impacts and judgments.
The same dynamics are at play when I talk to my customers about artificial intelligence (AI). There is plenty of research out there to tell them buying is the right decision. That AI is on the cutting edge of banking technology and billions are being poured into research annually ($37.5 billion in 2019 alone, according to the International Data Corporation).
But none of that hype reassures most buyers. In fact, it makes them more apprehensive. Most can’t help but wonder, “Is this AI system going to be obsolete in a year?” Many put off a purchase, figuring a delay will work to their advantage. After all, next year’s version is bound to be better, right?
But that is the funny thing about AI – waiting is almost always a bad idea. That is because, unlike your new automobile or flatscreen television, AI actually gets more valuable as it ages.
Why the inverse dynamic?
With most software, its capabilities are locked in the moment the code is complete. Try doing something the programmers didn’t anticipate (like opening an unfamiliar file type), and the results can be ugly – anything from an error message to an application crash. Applications mature, of course, and each new version (usually) works better than the previous one, but you have to constantly play the update game.
AI works differently. It can learn just about anything with few, if any, programmed rules. It can even learn from trial and error, the same way us humans do.
Let’s suppose you’re part of the ‘know your customer’ team at a bank, solving name screening alerts generated by onboarding clients, and you work closely with an AI to do it. That AI investigates and makes decisions just like your best analysts (except a lot faster of course, solving over a million alerts per day). But no analyst is perfect right out of the gate, and smart as it is, the AI is no different.
The day it started, the AI was solving only around 40% of alerts without help, and after working at your side for a year, it’s up to 80%. How? The AI learns from every single case it sees. The longer it’s on the job, the more it learns – and the better it gets.
To use a non-banking example, think about the recommendations social media makes for you. When I first signed up for Facebook, the paid content it suggested for me was laughably off-base (like you, I’ve gotten political ads for politicians I’d never vote for). But as time went by the recommendations became more focused, eventually narrowing in on things I actually did find interesting. Sure, there are still some weird items that come up in my social media feed, but now they’re the exception rather than the rule.
So why buy an AI system now, rather than waiting two years? Because a system with two years learning under its belt will do the job far better than a new one that has to learn everything from scratch. It’s the same reason that someone who has been with your institution for two years works more efficiently than a newly hired employee. Experience matters.
The fact is, it’s only the banks that have learned not to think of AI like an old school software product, and regard it instead as an extremely skilled and eager-to-learn employee, that are truly tapping the enormous potential of this powerful new technology.
The two big investments that a bank makes in AI are money and time. You might be tempted to wait to make a purchase, because you believe it will save money. But really, you’re just losing time.
The writer is currently SVP, US and Canada, at Silent Eight. He received a Ph.D. in Chemical Engineering from the University of Illinois, modeling big data sets on supercomputers. He has worked in the tech industry in Chicago for the last 25 years, primarily in the field of machine learning and artificial intelligence. His first novel, The Robots of Gotham, was released in 2017 by Houghton Mifflin under the name Todd McAulty. Follow him on LinkedIn.