Digital Decisioning: Using Decision Management to Deliver Business Impact from AI

"These two books are very much pioneering in bridging that gap and writing about the technology, not just in an abstract, buzzword way, but in a concrete, specific way, about how value is delivered and how the project needs to be run, in a way that’s understandable to all readers. That’s critical because right now, we have data scientists operating in a vacuum. They come back and they say, ‘Hey! Look, I made a predictive model.’ The executives say, ‘Oh, that’s interesting. That sounds good.’ Then the data scientist says, ‘So, are you going to use it?’ ‘What do you mean, use it? You want me to make a change to the way my company is operating?’ That’s another conversation, and it ends up being a non-starter because that change wasn’t part of the plan from the get-go, but that’s what needs to be put in place. That’s the message of these books—alongside just deepening the understanding of machine learning for people who are not data scientists. In my book, I’ve got a number of examples, including UPS. It’s an established company, more than a hundred years old, that’s very much set in its ways. It improved its delivery in the US of 16 million packages a day using a system that predicts where packages are going to need to be delivered tomorrow, in combination with a system that prescribes driving routes. They save 185 million miles of driving a year, $350 million, 8 million gallons of fuel, and 185,000 metric tons of emissions, only because the leader of the project was really aggressive from the get-go, saying, ‘Hey! Look, this isn’t just a number-crunching project. We’re going to need to change the way shipping centers across the country allocate packages to trucks and then load the trucks overnight for their departures in the morning.’ That change met a lot of resistance from above him, at the executive level, and later, when they went to actually deploy it, from people working on the loading docks, who needed to follow new prescribed assignments of, ‘Put this package here; put this package there. Regardless of where you think this package should go, put it in this other truck.’ There were a lot of trials and tribulations, but relatively speaking, they had really good forethought of what it would take to manage that change. Change management is an established field unto itself. There’s an art to it. Everyone knows it’s hard. The problem here is that with machine-learning projects, people aren’t conceiving it as requiring it. They’re not applying the art of change management, because they see it as a technical project rather than what it should be seen as. It needs to be reframed as an operations improvement project that critically uses machine learning as part of achieving its goal. If an organization has large enough processes—and that applies to all large organizations, even some small ones—it’s hard to think of a place where there’s not at least a certain potential. The important thing is that they focus on a concrete value proposition rather than just thinking, ‘We’ve got to use AI! Everyone else is doing it!’ If you need to improve a large-scale operation, then the way you’re going to do that is by predicting some outcome. Who’s going to turn out to be a bad debtor? Which item rolling off the assembly line is going to need to be inspected as potentially having a fault? Business is a numbers game. Most marketing mail is junk mail; most marketing email is spam. We can’t predict like a magic crystal ball, but we can tip the numbers game by predicting better than guessing, and that’s the value proposition. It doesn’t really hinge so much on the sector or the size of the company, but on the size of the operation. If you’re a really small company, and you’re sending out a marketing catalog once a year during the holiday season to sell candies or gifts or something, but your prospect list is a million, you can learn from the responses you got from last year’s mailing in order to better target this year’s mailing. That’s machine learning. The opportunities abound. Most large companies are using it in certain ways, but its potential is being only partly tapped at this point. Yes, and that goes hand in hand with the way the word AI is generally used. There is a mismanagement of expectations and over-promising. Even when a business stakeholder has a pretty good idea, e.g. ‘Let’s predict which customers are going to cancel, in order to target retention campaigns’ (i.e. giving the right incentives to those customers to try to keep them around), it turns out they need to get a lot more detail than that, in collaboration with the data scientists, in order to make the project successful. It’s not just the general gist of what the project is meant to do, but there are a lot of semi-technical details about exactly what’s predicted, and therefore what those probabilities that the system is going to output mean, and how exactly, mechanically, you’re going to use those probabilities. Because integrating probabilities into operations—which sounds a hell of a lot more boring than machine learning or AI—is really what we’re talking about. It’s very practical—if you’re actually trying to put it into practice."