The Other AI Revolution: Why Predictive AI will Overtake Generative AI in 2027

Eric Siegel (0:01)

These days, the vast majority of attentions on Generative AI and disproportionately so. This is my extraordinarily subversive statement, and I hope everyone's sitting down. Most organizations should be investing at least as much in Predictive AI or Predictive Analytics as they do in Generative AI.

Geoff Nielson (0:21)

Generative AI is where the attention and most of the investment is going. So why does Eric Siegel think companies may be overlooking an equally important opportunity? I'm Geoff Nielson, and this is Digital Disruption. Today, we get past the incredibly vague label of AI, and ask what underlying technology is actually doing something valuable. Let's find out.

Eric, super excited to have you. Thanks for being here. Maybe just to jump into it, you know, I was reflecting. There's this like bad habit that a lot of people in the space are guilty of, and one of them, which is using this like umbrella term AI, versus actually clarifying what you mean, right? Because there's different components under there. And so this is something that I've heard you talk about before. So under AI, what are kind of the different macro types of AI? And can you unpack those a little bit?

Sure.

Eric Siegel (1:18)

It's a great question, Jeven. Thanks for having me on the show, by the way.

Yeah, AI is not a monolith. It never has been. It's always sort of been an umbrella term for collection of things. But these days, you know, without when it's not qualified and it's general usage, it usually refers to a use of large language models or generative AI.

But indeed, there is at least one other main kind of AI, which is very much still in operation, which is predictive AI. Predictive AI learns from data to predict for each individual who's going to click by, lie or die, commit an act of fraud, any outcome or behavior for which there's value to the organization to improve its existing large-scale operation. So that's the technology, unlike generative AI that you turn to for improving existing largest scale operations targeting marketing fraud detection, which satellite should I inspect if maybe running out of a battery? Where should I drill for oil? Any and all the largest scale existing operations where you want to tip the balance in the numbers game that is business, we don't have a magic crystal ball, we could predict better than guessing, and that's generally more than sufficient to improve those. That turns out to be a very different kind of discipline or project than the kinds of large language models type use cases and applications, those types of generative AI projects. They're really still very distinct. These days, the vast majority of attentions on generative AI and disproportionately so. So this is my extraordinarily subversive statement, and I hope everyone's sitting down. Most organizations should be investing at least as much in predictive AI or predictive analytics as they do in generative AI.

Geoff Nielson (3:01)

So I want to dive right into that, but I also want to clarify something as well. So predictive AI, to me, as I understand it, is almost sort of like the OG AI.

There was some version of this around for a lot longer than LLMs. But one of the things that I think is maybe confusing some people as well, and I want to ask you about it, is it seems like more and more LLMs and generative AI are almost masquerading as predictive AI. They try and plug in somewhere to something that feels like it's predictive, even if it's not necessarily predictive itself. Do you know what I mean by that? It seems like LLMs are not just positioned in the game of, hey, here's a block of text. They're trying to get into the autonomous business. They're trying to get into the, hey, I'm giving you data or an answer business. And they're not necessarily doing that on their own. Do I have that right?

Eric Siegel (3:56)

Yeah, let me answer that and also address, I can hear in the back of my head the screams of protests from a good portion of the listeners. All machine learning predicts, including large language models predicting the next word, that's what machine learning essentially does. These are different ways of using it. You can use a large language model for a predictive AI project. You can use it to predict. It's typically overkill. Maybe not though, it can help. There's diminishing returns as you increase the complexity of the model, whether it's a large language model or one of the standard classical machine learning methods, like decision trees, log linear regression, and even smaller neural networks and ensemble models.

But it is diminishing returns because we can't expect to have highly confident prediction about who's going to click, buy, lie, or die. Often we're predicting for humans or enterprise customers, or transactions, which ones are going to turn out to be fraudulent. We don't have a magic crystal ball, so we don't have high confidence, but we have the next best thing, which is probabilities, which is putting the odds on those numbers so we can tip the odds as we play these numbers games and treat each case accordingly. So the difference between Generative AI and Predictive AI used to be known as Predictive Analytics. It's older, but not old school. Most of its value is still largely untapped. The difference isn't so much about the core technology, but about the way it's being used. So these are two main categories of use cases of machine learning, and they're destined to remain distinct because there's an inherently different thing for those types of projects where you're trying to target large-scale operations more effectively, whether it's marketing or any kind of risk management, insurance decisions, pricing and selection, fraud detection. It's embarrassingly long. It's embarrassingly horizontal. It's an extremely long tail of use cases.