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

Digital Disruption with Geoff Nielson

Machine learning expert Eric Siegel joins Digital Disruption to argue that organizations are heavily over-indexed on generative AI while under-investing in predictive

Key takeaways

• Eric Siegel argues organizations should invest at least as much in predictive AI as generative AI due to its proven track record in large-scale operations like fraud detection and marketing targeting.
• Generative AI faces a reliability ceiling for full autonomy, whereas predictive AI's main barrier is the semi-technical complexity required for business stakeholders to understand and act on probabilistic outputs.

Main topics

• Predictive AI vs. Generative AI
• AI Pilot-to-Production Gap

Notable quotes

"We don't have a magic crystal ball. We predict better than guessing."

Conclusion

The episode delivers a grounded, business-centric roadmap for leveraging predictive

Transcript preview

Speaker 2 (0:00) These days, the vast majority of attention is on generative AI and disproportionately so. Speaker 1 (0:07) This is my extraordinarily subversive statement, and I hope everyone's sitting down. Speaker 2 (0:12) Most organizations should be investing at least as much in predictive AI or predictive analytics as they do in generative AI. Speaker 1 (0:20) 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 Jeff Nielsen, 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. Speaker 1 (0:47) 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 I'm one of them, which is using this like umbrella term AI versus actually clarifying what you mean, right? Because there's, you know, different components under there. And so this is something that, you know, 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? Speaker 2 (1:17) Sure. It's a great question, Jevin. 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, when it's not qualified in its 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, buy, lie, or die, commit an act of fraud, any outcome or behavior for which there's value for the organization to improve its existing large-scale operations. 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 that may be 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 predict better than guessing. And that's generally more than sufficient to improve those. So that turns out to be a very different kind of discipline or project than the kinds of large language model type use cases and applications, those types of generative AI projects. They're really still very distinct. And these days, the vast majority of attention is 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. Speaker 1 (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 you know, I want to ask you about it is it seems like more and more LLMs and generative AI are almost masquerading as predictive AI. Like 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? Like it seems, 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? Speaker 2 (3:56) Yeah, let me answer that and also address, I can hear in the back of my head the screams of protest 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, even smaller. neural networks and ensemble models. But there's 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 horizontal. It's an extremely long tail of use cases. And the type of sort of discipline and endeavor when you're putting odds and then you're systematically acting on those probabilities, those predictive scores, the things output by the model. Right. So in sort of a technical point is when you're using a large language model, it's predicting which of the next word be or on that level of detail. Right. The next token, which is incredible what it can do. Whereas for the sort of enterprise machine learning projects that I'm referring to as predictive AI or predictive analytics, you're predicting on the enterprise unit. So, you know, for which customer, healthcare patient, company, transaction, automobile, whatever it is. So it's on that level rather than on a per word level with a language model. Speaker 1 (6:42) And so when we come back to Speaker 2 (6:43) this, so that was a big word salad or, you know, do you think that I helped clarify there? Speaker 1 (6:48) Well, let's, let's keep, it was helpful. Let's keep down that clarification journey because as I mentioned before, people just often say AI and they, they leave it there. Maybe that's deliberate. Maybe it's not. But when, when people say AI is going to cure cancer or these Patriot missiles are using AI. What are they talking about there? Are they talking about generative AI? Are they talking about predictive AI? Or is there something more nuanced than that? Speaker 2 (7:13) You know, it's often the case that they're talking about predictive, but it's in the same conversation that started with generative. On the other hand, the two areas you just described could be either, right? There's ways in which they both can pertain. So it just depends on the very particular project, the problem you're trying to solve, the value proposition, right? Are you making... Speaker 2 (7:37) probabilistic determinations of an unknown, whether this is going to turn out to be fraudulent, whether this healthcare patient is going to turn out to have this diagnosis and, you know, who's going to click buy, lie or die, right. Or the response or cancel their subscription. Right. What are those are predictive use cases. And those tend to be the, I mean, predictions, the Holy grail for improving existing large scale operations. So. I've heard you in other episodes say, hey, look, is it turning out that AI is mostly valuable for really unsexy things? And my answer is mostly yes, kind of. It's a very subjective concept, but certainly on the scale in comparison to the way generative AI is hyped, this stuff is extremely unsexy. But to me, as someone who has been in love with the concept of machine learning for more than 30 years and just as much loves its actual viable operationalization, its usage, deployment, its application, right? Not just this awesome technology. With all technology, including both kinds of AIs, there are two wows. And we almost always get to the first wow. and very rarely, more often not than do, get to the second wow. The first wow is the technology, the core capability, learning from data to predict, or being able to do an amazing demo with a large language model, a pilot, right? Sort of a broad proof of concept. That's the first wow. The second wow comes with deployment, operationalization, right? With the first wow, the technology, you have... created potential value. You haven't necessarily captured or realized that value. And indeed, you definitely have not until you actually deploy it. Speaker 1 (9:28) If you're a technology leader, InfoTech is a name you need to know for getting ahead with AI. InfoTech helps leaders deliver on top technology initiatives. And one of our most popular programs right now is AI Strategy. We've helped hundreds of organizations build out an AI strategy and roadmap using our step-by-step methodology. Infotech's road-tested approach gives you a trusted blueprint to execute faster and avoid costly mistakes. And what you walk away with is a boardroom-ready AI strategy tailored to your needs and ready to execute. What sets Infotech apart is how we deliver. Unlike traditional research or AI tools, we don't stop at guidance. Infotech works with your team to build the strategy. You can do this through a focused one-week workshop, ongoing analyst support, or by using our full methodology and toolkit to get it done yourself. The outcome is an AI strategy ready for the boardroom and a team that's ready to deliver. To learn more about our AI strategy approach, check out the link below and don't forget to like and subscribe. I want to come back to something you said earlier, because I think we're very close to your thesis here, which is, you know, basically what I heard you say is that most organizations are... over-indexed right now on generative AI and under-indexed on predictive AI. And so I wanted to come back to what you were just saying and ask, why is that? What is it about predictive AI that has more opportunity or potential for you? Speaker 2 (11:01) You know, around the time this episode is releasing, I'm releasing an article on CDO Magazine, which I make the case that, yeah, it's over-indexed, at least in terms of venture investments and R &D. But in terms of actual enterprise deployment and projects, maybe on the surface, maybe in terms of the headlines, maybe in terms of the way executives talk to the board and such, it's very under-indexed. But predictive AI is still, it turns out, very much alive. And I give a bunch of evidence to that effect. For example, when I did a broad call for speakers for the conference series I've been running for a long time called Machine Learning Week. And I didn't specify, hey, we prefer predictive versus generative. And it ended up being about 50-50. I mean, there's lots of predictive data. And also just informally, like this isn't legit data, but the buzz behind the scenes, so often we hear people saying, you know. There's a lot of pressure to use generative AI, but really the thing that's still delivering value is predictive. So even though the bad news might be that, hey, look, it almost looks like we're throwing out the baby with the bathwater, much to the detriment of the organization and sacrificing all this potential value because the overwhelming spotlight and attention is being given on generative AI. The good news is it turns out that hasn't fully taken hold and there's still, I mean, there is something sexy about value. Speaker 2 (12:27) For Speaker 1 (12:28) sure. And I'm curious, like to use your words, where does that second wow live? Like, where are we seeing that right now versus where are people, you know, all talking about it, but it's kind of hot air. Speaker 2 (12:41) Well, to answer that question, I kind of have to answer separately for generative versus predictive. They both have this very parallel story where pilots don't go to production and most don't, but some do. So if it's 5 % to 15%, depending, and this is a moving target and there's a lot sort of conflicting data about it, it's hard to put an exact number on. But I was involved with industry surveys for predictive and that looked like it was around 15%. Speaker 2 (13:12) What happens is with predictive AI, so if it's, you know, if it's five to 15 % of many, many projects, that's still a lot of successes. And predictive AI has been around for decades, right? I mean, in the 60s, they started targeting marketing and doing credit risk prediction with regression and like sort of primordial machine learning models. Speaker 2 (13:34) So there's been a long track record. And even if, despite its age, predictive AI still hasn't fully matured and reached that level of professionalization, there's still a lot of success. However, the potential is generally untapped and we could do a lot better. And it's not, on both accounts, generative and predictive. It's not a tech problem so much as an organizational one. In the case of generative, the problem is... unrealistic expectations, infeasibility, and that is to say reliability. So it's very, very easy to conceive of a project, a scope, an intended use of what people often use the word agent. I feel that. word isn't helping avoid the overpromising, but whatever you want to call it, right? The system can't be deployed because even though its error rate is only 5 % and a 95 % success rate is incredible, right? Unprecedented. Couldn't even have imagined it five years ago. The error rates too frequent. So it's not viable, at least with its intended current, currently intended usage of being. fully autonomous and therefore it's going to collect dust. Nobody can operationalize it, therefore it doesn't provide any value. That's generative. In the case of predictive, it's not that it's got a reliability problem. Nobody who's familiar enough with this area or anybody who listens to the following sentence is going to expect too much because it's not about high... Well, the word accuracy is already often thrown around, but what that... usually means is that it's really confident about both positive and negative predictions. That is to say, really confident about every prediction. It's not a magic crystal ball. It's simply a lot better than guessing. That tends to be valuable. So it's not that it needs more technical reliability, but there's an understandability or complexity issue. It's not a rocket science level type of understanding. It's sort of a semi-technical accessible amount. But stakeholders need to have an understanding and apply just the right particular arithmetic to understand, hey, here are my operations. Here's how often we fall victim to fraud in the transactions or whatever problem you're trying to solve. And here's the math that shows, hey, if we take this action in these high-risk cases, we'll be wrong in the other direction. We'll have false positives. Let's do the arithmetic. Let's figure out what the bottom line is. That level of complexity often eludes. business stakeholders. So there's sort of a cultural catch up. Yeah, it's already been 40 or 50 years, but we're so close and yet so far. So just to summarize, generative AI has a reliability issue. Predictive AI has a complexity issue. Speaker 1 (16:24) I'm there's an angle here that I want to talk about around the hype there. And tell me if you disagree, because I don't even know that I agree with myself right now. One of the things that comes to mind hearing about both of those is it feels to me like predictive AI is probably a really effective optimization tool of for any business that can help you squeeze more juice from the same fruit. The promise that we're starting to hear about from generative AI, you know, more so seems to be like, this is going to transform the way you do business. Forget the fruit you've got right now. Like this is going to take you from selling oranges to, you know, a smoothie stand. I've, I think I've stretched this metaphor a bit about as far as it can go, but, but it seems to be a very different narrative around it right now. Do you buy my framing and do you buy that that's actually true? Speaker 2 (17:16) You mean that that's. That it's true that that's the way in which it's over-promised and over-hyped? Speaker 1 (17:22) Yeah. Are those the conversations that people are having? And is that actually true about the technology? Speaker 2 (17:30) Yeah. I mean, look, I've been annoyed by hype for a long time. And in the last few years, two things have happened at once. Large language models came out and knocked my socks off. Right. In fact, so to be specific, like during my six years of