There recently has been an increase in the talk — pro and con — about bots, AI and intelligent assistants. A lot of this talk has been percolating around the industry for decades.
Silicon Collar, a book by Vinnie Mirchandani, a friend and truly gifted analyst, accepts that automation might be eliminating jobs, but it optimistically holds out for the silver lining.
Businesses and individuals have been taking advantage of an opportunity to build new human-mediated processes — and jobs — that leverage intelligent systems, according to Mirchandani, who has documented the trend.
10 Years to Overnight Success
Not so fast, says Esteban Kolsky, another friend and gifted analyst. Like many of us, Kolsky has seen this movie before. Adoption has been painfully slow, in his view.
It’s been so slow, in fact, that AI fails what I call the “Gates Test.” You might recall that Bill Gates once said that we overestimate what we can do in two years and underestimate what we can do in 10. Indeed, the gestation period for an overnight success seems to be 10 years these days.
However, a recent survey indicates a shift.
“From the single digits in adoption they enjoyed for the entire 2002-2012 decade we are seeing adoption nearing 15 percent now for automated bots and intelligent assistants,” Kolsky noted in a recent post.
Slow indeed. What’s been holding things back has been a lack of two things: 1) not enough computing power and 2) a clear need.
The World Is Curving
We can come up with plenty of scenarios in which a little help from something with AI embedded might seem good — but on closer inspection, we might realize there are other ways to get the job done.
AI is a heavy lift — or at least it was at one time. Back when the working models of AI were established, computing power was not up to the job — but really fast processors, multiple cores, flash memory, and the cloud have made it possible to concentrate the power needed to drive AI. However, that still leaves us with finding a clear need.
I offer the following analogy: We live in a spreadsheet-dominated world with a linear mindset, but we are moving into a world where the lines are anything but straight. To make sense of curved lines, you need calculus.
It’s calculus — especially the integral variety — that tells us what’s going on in a process that has plenty of funky ups and downs. In the spreadsheet era, which I firmly believe is ending, or at least transitioning, we searched for averages and made straight-line derivatives from them.
This led to some dumb ideas, like calculating what an average deal is and trying to fit all deals into it as if it were a straightjacket. That thinking harkens back to the statistical awakening in the 19th century, when the term “average man” first came into use. The average man is a fiction — but a highly usable one that gives us a basis for modeling.
When you go for an average, though, you have to ignore some profitable outliers or other things that don’t fit your model. In the age of business by transaction, the straight-line model was good enough. Nonetheless, over most of this century so far, we’ve seen that model become less effective as the vendor-customer relationship has moved toward the micro-transactions of subscriptions.
A straight-line model doesn’t work very well in subscriptions, because all transactions look the same at a micro level. It’s only when you expand your view that you can see the micro-transactions that show trends that might be good or bad. As a result, we’ve been left without a model.
A model for the vendor-customer relationship that works involves calculus, at least at the metaphorical level. Calculus gives us the flexibility to model many variables involving customer demographics, purchase history, life-cycle stage, and of course the transaction before us.
I think many people in business have a working appreciation of all this, though they are certainly still in the minority, and this is where AI comes in: I see its algorithms as calculus in a box.
AI gives the average businessperson who has no interest in calculus, or who might have studied it decades ago, the ability to apply more sophisticated modeling to increasingly complex business.
So, this is a long-winded attempt to say that at last we have a clear need for AI, as well as the horsepower to run it. The need is all around us — and if you’ve ever caught yourself wondering at how sophisticated business and our supporting systems have become, you’ll likely be grateful that there’s a new weapon in the arms race.
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