#89 Making AI Work for Us

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Hi everyone, and welcome back to the Proteus Leader Show. My guest today is Nada Sanders, an internationally recognized AI thought leader, expert in forecasting and global supply chain intelligence, and distinguished professor at D'Amore-McKim School of Business at Northeastern University in Boston. Ranked by Stanford as one of the world's top 2% of scientists, she's a sought-after speaker and the author of 100+ top-tier scholarly publications and 7 books, including her latest, The HUMachine: AI, Human Virtues, and the Super Intelligent Enterprise. So, Nada, I'm so glad you've agreed to speak with us today. Welcome.

Thank you. Thank you, Erika, for having me. I'm really thrilled to be here.

I'm so thrilled to have you. I just feel like AI is on everybody's mind right now, and most of us want to know more. And so I think your insights and your experience will be really, really valuable to everybody.

I'm really happy to share, and I'm going to share with your listeners that the work that I'm doing and I have been doing is ongoing. And so we have this book that came out in March 2024 just now. It is actually the second edition of the first book, The Human Machine, that came out December 2019. And so this research in terms of how companies use technology and how they use AI Has really evolved. We were doing it for years prior to publishing the book in 2019. And if you— everybody remembers what happened at the end of 2019, we got into 2020 and we know what happened, right? The pandemic and so forth.

Sure.

Well, we've continued with this research and we were asked to do an update of the book. And it's really been interesting to compare 'Cause the research involves interviews with CEOs, senior vice presidents, IT leaders across companies, across all sectors, and to see how they're using it, what they're doing. And so the contrast between what companies were doing right before the pandemic, what they were doing after.

Yeah.

And then I have insights 'cause I've been, you know, continuing this process over the, you know, past months and weeks. And comparing that to even what was being done last year or 6 months ago, I think is really interesting. I'm happy to share.

Wow, that's such great context. So let's just dive in. So the first question I wanna ask is, you've said that though businesses are spending billions, literally billions on AI now, they're missing a crucial piece of the puzzle. So can you tell us what that is and why it's so important?

Yes, and I can actually say a lot. There's actually 3 pieces if I had to distill it down. that I think are really important. And I could even expand those, but I was able to distill it to really 3. So here's the thing, Erika. AI is the latest shiny object. And one of the things that we see in business— and again, I've been doing this for a long time and trained companies on various processes that come out. And companies and leaders are very busy, rightfully so. So we hear about the latest thing. We want to chase it. It is now AI. AI is, however, just another tool, a very important tool, a tool that is going to change the way we do things. But a lot of things have changed businesses. Right now, what we're looking at, we have statistics to show that, believe it or not, 70% of AI initiatives, roughly 70%, don't reach their potential. And when we think about the billions of dollars. I mean, right now, there are multiple estimates. But we're looking at about $60 billion that's going to go into developing AI models over the next coming years, 2026. And think about 70% of those not succeeding or not reaching their potential. We're talking about huge losses. And the reason that is is because, again, There is a knee-jerk reaction. We've got to do what everybody else is doing. Everybody is using AI. I don't want to mention conferences, big names that we all know that have just happened of last month. And everybody that we bump into, all the executives are saying, we have to integrate AI. We have to see or say that we're doing something with AI. Our boards are putting pressure on us to say that we are implementing AI.

Yeah.

But what is the problem? Well, number one is the fact that AI is copyable. So think about Pepsi and Coke. You know, I'm just picking those out. If both are using AI, well, what is going to give a competitive advantage? What makes the difference, right? Now it becomes kind of standardized. Well, what is different is the business model and how you're using it. In one of the interviews, actually it was an interview with Spencer Funk, the CEO of Lee& Funk, and well-known, obviously been around for decades and decades and decades. And one of the things that he said to me that I thought was really insightful was that companies needed to reevaluate, reassess their business plan, their business model, Either change it or at minimum reassess it. And he said what companies were doing was simply tacking on AI to old business models.

Yeah.

That was analogous to, he said, imagine digitizing a horse and buggy when your competition was coming out with a digital car, right? And so I thought it was just a great analogy because—

Rather than integrating AI into your business model, just kind of You know, tacking it.

Exactly. And what you're doing in the process is you are simply solidifying old business processes, old business models. Now, so now you have spent all this money, you're solidifying old ways of doing things, but those old ways of doing things may not work. They may not be valid in this new environment where your competition may be doing things very differently. So at minimum, We have to start with reassessing that business model and saying, is it going to be different? Should it be different? Maybe it won't be. Maybe certain parts of it will be different. But you simply can't take what you've been doing, just acquire AI, and integrate it, tack it on like Eeyore's tail, right?

Right.

On what you have been doing. And that's a huge mistake. So reassess the business processes. And one part of that that is important is how that AI is integrated with the human element and the business itself. It's that process that is absolutely critical. You can't just, you know, most businesses want to acquire it, tack it onto what they currently have. and then say, well, we're using AI to do XYZ. That is not going to work. That's not going to work.

Yeah. So now I wonder if this leads into our next question, which is, what are some of the changes that leaders need to make before our norms around AI become hard to reverse?

Exactly. The first one, as I had mentioned, is to reassess the business model. To really look at, you look at the competitive landscape and really look at what are your competitive priorities and where is AI going to make sense that would really enhance the competitive priorities, how you are uniquely competing in the marketplace, bearing in mind a number of things. You know, these things are really expensive, Erika. I mean, we could talk about AI, you know, but, you know, budgets are not finite. So where, Can we get the biggest bang for the buck given? You know, what's the ROI going to be? What do we need to enhance? And then really do a a little bit of you know business model financial metrics and analysis. And the other one is people. One of the mistakes that that I I see companies making is getting rid of a lot of their talented. People that have been around for a long time, that employees that are experienced, maybe their salaries are higher than some of the brand new kids being hired. But I see that as a big mistake. One of the things that companies need to maintain are people with what we call domain knowledge. Domain knowledge is the expertise that somebody has of their business environment, their competitors. And one of the things that we know now is AI machines, they're going to be better than humans 95% of the time. In fact, we had Garry Kasparov at Northeastern last month. And I, you know, he talked to all of us. I had a chance to chat with him privately. You know, he is the first person that was beat by a computer in chess, as it will. But one of the things he was saying to me is, look, machines are going to be better at us than 95% of the time, but that other 5%, we are going to be better. And the key is to know when to intervene and how. And if you don't have experience, you're not going to know when to do it. And that's going to be the difference between good decisions and—

Bad decisions.

Great decisions and great decisions.

Just to interrupt for one minute. That's a really important thing. And I've noticed— I'll give an example from my own life. So, you know, you have ChatGPT or whatever write a paragraph about your business. And so I— we had them do that with our business, Proteus, and then I read it. And then someone who's very pretty new to the company read it. And I could tell when it— where it was off and where it was on. And the new person couldn't. Because the inaccuracies were pretty subtle. You know? So we're completely agreeing with you that somebody has that domain expertise, has been around for a while, they can tell where that 5% is.

I love what you're saying. And I'm actually— I might even quote you at some point because different studies have showed different findings. And I think a lot of it depends on what kind of GenAI you're using, what the model is, and what the task is, right?

Yeah.

What is the actual task? But in our own research, what we found is that GenAI is much more helpful to experienced workers for exactly what you're saying. They know the difference.

Yeah.

They can actually be able to look at the content and be able to pull out the really subtle differences that a novice simply cannot. And you know what's gonna happen? What's going to happen is this is going to become standardized. And so what's going to win in the marketplace? What's going to win in the marketplace are the integration with GenAI with experienced workers that can really put meaning behind it. Because I've used GenAI. And to be honest with you, talk about ChatGPT. It is entirely too chatty, right? They're all very verbose. —extensive, and it's vanilla. And we didn't even touch on hallucinations that become embedded.

Yeah.

Yeah, the errors. And those errors are not obvious. They're actually very— I've run my own name on them where I've said, who is Nada Sanders? And as you know, GenAI models are probabilistic. And every time, you're going to get a different output. And so when I put my name in, I say, who is Nada Sanders, I'll get— A lot, you know, most of the things are very accurate. You know, she's distinguished professor at Northeastern, da da da da. And as you go through, then much, much later, you get lulled in this feeling of, oh, it's doing great.

Right, yeah.

It's all right, it's doing fantastic. And then later it's embedded. And there was one much, much later where it said, and she has addressed the United Nations. And I was like, whoa. I mean, I've worked with hundreds of companies. I have never addressed the United Nations. That would be very cool, but—

Right.

And if somebody doesn't know, they're very lulled into all these truths, and then you don't know when the lie is. The second time I ran it, same kind of a thing, all these truths, and then she was founder of, and something completely fabricated, some organization I don't even know. If someone doesn't know, and they just follow it through, that's really significant. And then you think about the legal liability and the kinds of professions that aren't just casual. They're really just looking at content. So—

That's a great example.

Yeah, so we have to be extremely careful. And so kind of coming back to what you were asking me, I think it's extremely shortsighted to eliminate talent that has been around for a long time, that has experience, because that experience right now is going to be the X factor. It's going to be the delta that is going to give the difference in terms of good versus excellent. Yes.

So then, you know, we talked about this a little bit before we started, that you're, you're all about applied AI and we're all about applications. So what are some key practical things that you would suggest to leaders that they can do right now to make sure that they and their teams are taking best advantage of the current state of AI?

Multiple things. One of the things that, and it's been said, we found this with our first book, is to make sure that the culture itself is inclusive and human-first. And I'll tell you why. And I have quite a bit to say, quite a bit of layers here. But we don't want talent that is afraid of losing their jobs. What you're going to find if your companies that do that, we're going to see incomplete integration. We're going to see sabotage. We're going to see disingenuous integration with AI, and it's going to be suboptimal. So you want to have a culture where the humans are first and they're not afraid of AI, that they are integrating AI to the best advantage, not from a point of being afraid of it. And number 2, keeping humans in the loop, humans that have the skills, that have domain knowledge, and have authority to be able to interact and override the system. So if I— one of the comments that was made to me, one of the interviews was John Siccard, who is CEO of Kinaxis, said to me, Blind obedience to the model is dead, and he said during COVID we learned that all math-based models collapsed. Why? Because these models are all based on historical data. When something happens that the model doesn't know about, that is not embedded in the historical data, will have a massive disruption. Think about all the various things from the pandemic to 9/11 to. all kinds of events. The model doesn't know about those. So we need to give authority to people with domain knowledge to override the system.

Yeah.

It's really important to be able to do that because they know— they will know that the rumor is the CEO is just about to step down of their competitor. And what does that mean for the business? The algorithm doesn't know that. And the last thing that was pointed out to me that is really important is that when— as AI becomes standardized, what's going to differentiate are human relationships. One of the CEOs I interviewed said to me, personal relationships then will be the thing that will override or be the determining factor. And the longer that relationship, the better. So again, we're talking about AI that is now standardized, that You know, competitors are using it, but what's going to be the, you know, who's going to get the sale? Who's going to get the deal? Where, Erika, if you and I are friends, we've been friends for a really long time, you know, everything is standardized when it comes to technology, but you and I know each other. We have trust. That's going to be the winning formula.

Yes. Yes. Oh, this is so great. Wow. Thank you so much, Nada. I know we just, we're just scratching the surface, you know. But this is so helpful. And listeners, to find out more about Nada and her work and to buy the Hue Machine, you can go to nadasanders.com. And to find out more about how Proteus supports leaders and their organizations to succeed in these wild, high-change times, just go to proteus-international.com. So thank you so much, Nada. This has been just— I feel like we've been drinking from the fire hose for the last 15 minutes. This is great.

Thank you for having me on. This is really important for leaders. It's extremely important because right now there's a panic, a hyper state to chase this shiny object. And as we both know, many of these are going to fail if leaders don't do this in a smart way.

Don't do it in a smart way. Oh, you're so welcome. So thank you everyone for listening. And until next time, here's to creating the life you truly want.

We hope you're feeling better equipped to create the career, the business, and the life you want. To find out more about how we support our clients in leadership, learning, and change, visit us at proteus-international.com. Have an excellent day, and thanks for listening.

Thank you.