AI, Leadership, and the Future: What We Know (and What We Don't) | Nada Sanders | 629

What happens when the world changes faster than our expertise can keep up? Dr. Nada Sanders, distinguished professor at Northeastern University, believes that in an era of AI and rapid transformation, thought leaders must embrace uncertainty, challenge assumptions, and develop new ways of thinking. In today's conversation, we explore how thought leadership must evolve to remain relevant and why the ability to synthesize ideas across disciplines will be a key differentiator. I'm Bill Sherman, and you're listening to Leveraging Thought Leadership. Ready? Let's begin. Welcome to the show, Nada.

Thank you for having me, Bill. Really excited to be here.

So one of the things about thought leadership is it's a journey quite often into the unknown to try and identify what we could know next. And I know this is part of the work that you've been doing across your career as well as an academic. So my question to you is How do you see that boundary between what's known today and what's unknown, and how is that changing?

Thank you for asking. I'm going to lead with a statement that you might say, "Well, you've kind of given the nugget away," but we can unpack it as we talk. I think one of the most important things is to really be humble and to accept that it's a journey of learning, and we never know everything. In fact, I've been doing my work as an academic, as a scholar, as a consultant, speaker for 25 years. And I can tell you I know what I'm doing, and I can tell you that I don't know everything. And one of the most important things is to really be open and learn and continue to learn and don't have the hubris to assume that what you did last year or what you know is the end of it. Actually, the more I learn, the more I realize how much I don't know. And I would argue that that is even more important today. We are in this sort of post-COVID, and I say post-COVID because we're trailing a little bit perhaps away from it, but it's a very rapidly changing landscape. From geopolitics to obviously Gen AI and technology to the way we work, the way we socialize, play. We just started classes yesterday, Bill, and it was interesting to be on campus and to meet with my students. And one of the things that I've done over the last few years, and I've increased it, using technology to have formal lectures online. I do an intense discussion in the classroom, and I've been really surprised how many students welcome that and say, well, there's still people that are doing traditional PowerPoints in the classroom and lecturing. We need to have face time, whether it's in a business meeting, whether it's any kind of leadership where we discuss and explore and break boundaries on really critical issues as opposed to a one-way monologue. I think that's gone. I think that's in the past because all of us, consume knowledge in a variety of different ways. It is readily available to everyone now. But then it's really about taking it to the next level. And I think the way we teach, certainly I teach very differently today, but I think the way business leaders need to be, the way all of us politicians, everybody, it needs to be very different, open to the concept that our learning never ends. And that, that's very important because we also, if I may just add, live in a time where, you know, categoric statements seem to rule the day and society responds to those, but those are actually incorrect, I would argue.

So there's a lot to unpack there. So let's start digging. I think that The first statement that you made around humility, right, ties also to the opposite of a categorical statement, where—

that's correct—

with that, it's easy to say very confidently, perhaps even with false confidence, I know, or we in an inclusive we sense know that. But it's a much more interesting question to ask What don't we know or what has changed? What will change? What's coming? And that requires humility to be—

Absolutely.

Go ahead.

No, without doubt. And I have to tell you, I think especially now with the, you know, in the era of Gen AI and what I'm seeing in the corporate sector, and I've been, I spent a lot of time with my research and work talking to senior executives, upper-level managers, CEOs. I was just doing it last week. You know, every week I'm gathering data on an ongoing basis. And there's so much pressure at the corporate level to do something with AI, to say we are doing something. CEOs, SVPs are saying to me, you know, we don't even know, but we have to say we're doing something because our boards are saying Well, we have to say it because we need to be in the marketplace with this and lead with it.

If they're publicly held, they have the street saying, well, what are you doing to become an AI business? Right?

Exactly. We're seeing all these talking heads that are talking about the future of work and then, you know, what AI is going to do. And I know a lot about this stuff and I can tell you nobody knows. Nobody knows the future of work, and these people are being paid huge sums of money to go on stage and tell everybody what the future of work is going to look like. And I'm sitting there going, you don't know. So what is needed is this in-depth dialogue with humility, a much better way to have us explore that boundary in terms of how do we shape that future and approaching it again, coming full circle with humility and acceptance that it is rapidly changing. And we truly don't know because we have many factors that are changing at the same time. We saw post-COVID what is going on with the workforce. We are continuing to see that in terms of what, what workers want from their work environment, what leaders are offering to this. And then what's happening with the AI capability for better, but also for worse. And I would argue we are definitely not any place where we can comfortably say AI is perfect at doing X. And so I would also add that all the GenAI has up to this point worked off of human-generated data that has scraped everything from the internet to what's available. What is going to happen over time is more and more of that available data is going to be AI-generated. My area of expertise, among others, is forecasting and the way prediction models work. And I can tell you that the most critical element is the data. That we use and how clean and reliable it is. And the biggest mistake that happens with models is if you use data that is generated by the model itself. That is what's going to happen over time with AI. There's going to be more and more data that is AI-generated, not human-generated. So I would argue that it's very likely that we are going to see more and more hallucinations, errors, and a variety— drift and a variety of problems. And today, where you and I are sitting and talking, for us to comfortably say the future will look exactly like this in a year or two, we don't know. But we can— what we can do is assign probabilities or likelihoods for possible outcomes. And I think for leaders, the answer is going to be to be humble, but to create resilient and agile enterprises that can adapt to what the changing landscape is going to be.

So if we start building a simple 2x2 matrix, which always is probably a challenge to describe over and a podcast, but I think is worthwhile. There's the question of what do I know as an individual, or what do we know within my organization or within my profession, etc., and what data can we rely on for that knowledge. But then the other axis is a question of what does the AI interacting with us know, and what does it not know? And even in— you said in the box of what does it know, it could know a whole lot of things that are false. So we're quickly getting into a realm of both philosophy and ontology here that really gets to, I think, the core of thought leadership in the sense that if thought leadership is the act of peering around a corner into the future and distinguishing between signal and noise and then bringing an insight back to people who need that information today, you can't rely on just the algorithm to be authentic.

You cannot. And part of the problem with the algorithm is that, you know, like the game two truths and a lie, you have to know where the lie is buried. So for example, and I've done this, I've put my name in ChatGPT multiple times because it's probabilistic, so every output will be different, right? So who is Nada Sanders? And it will go through and everything is correct. You know, distinguished professor, da da da da da, first paragraph, second paragraph. You're like, okay, this is great. Then you come to the very bottom in there and it'll say she has worked with numerous companies.

True.

And then it'll, it says, this happened the first time, she gave a significant talk in front of the United Nations. It's absolutely not true. I would love to give a talk in front of the UN, but I have never had or been asked or had the honor. But if you don't know, you see, I've had to go very deep into that response to see where the false comment is. I could have easily been lulled into a false sense of security and confidence that everything was correct. Next time around, I asked the same question, same thing. Okay, we're going through multiple paragraphs. Correct. Correct. Correct. And then it says, she is a CEO of a company she founded. Absolutely untrue. So again, so there, there are a couple of things here. I think that over time, I think leaders are going to learn to use AI as a tool, getting the information that they need, especially information that is less significant as far as risk and consequences. So it could be road information, basic information, but ultimately making their own decisions on top of that, not allowing the algorithm to make that final decision and knowing when to interact with it, when to override it. But that takes knowledge, expertise. And one of the— so that's the first aspect is knowing how to override it, when to override it. And that means that a leader has to have domain knowledge, domain expertise, whatever that is. If you're, you know, a cancer or you're a pathologist, you've got to know your stuff. If you're a politician or, you know, you've got to know your stuff. So you really have to have the expertise. The second part of that is for businesses in particular and organizations is to create a pathway for people that are entering the organization to develop that knowledge because you see a bifurcation of the workforce of those people that have the experience. And then those that are coming in that know how to code and really know that have that digital literacy but do not have the experience. And where companies are now is many of them unfortunately are laying off their senior talent because they're more expensive. I would argue that is an incredibly foolish thing to do for multiple reasons. And I would argue that the future of enterprises that are going to thrive is to have these teams where you have senior people that are very experienced and those that are young, and then you create a pathway of training for the younger ones to have, you know, to truly develop these skills that are going to take time.

That layers into several things I want to talk about. One, the death of expertise has long been forecast but doesn't seem to ever arrive. And in fact, the way you're describing the use of generative AI, whether you're an expert or practicing thought leadership, not only the ability to distinguish falsehoods— and you gave some very easy, relatively factual examples— but if you're going and using it as a tool professionally, you have to know what is false, what will not work. And someone less experienced may take an assumption as a given, which isn't true. And that can lead to significant or even catastrophic mistakes.

Absolutely. In fact, Garry Kasparov, the famous chess master, was on campus here in Boston. And I had the opportunity to chat with him, and I was really honored. He was the first person that was beat out by the IBM Deep Blue at chess match. And he's done a lot of work in this area, human-machine interface. And there's really, I think, a lot of very important lessons. But I was chatting with him, and I said, tell me what you see as the future. What is this going to look like? And he said, really, It's going to require two things: our acceptance that probably 90% of the time— and obviously, is it 90, 95, 85? It depends— we let the algorithm do what it does and it will be better than us. The rest of the time, we have to know when and how to intervene. And he said, and I'm just quoting him, but I believe this to be true as well, The rest of the time is overriding it. And he said that will be, that will be the difference between good companies, good decisions, and excellent decisions. And I would argue that, you know, companies, Bill, that survived COVID, that got through, we saw so many businesses go out of business, collapse, and so forth. They were doing something right. But now we're in a very different, you know, really different universe, if you will. And who is going to be left standing in 3 years, in 5 years? And that difference in terms of good decisions versus excellent, I think, is going to be one of the factors. And that's going to take thought leadership. And right now, so many leaders are mesmerized and under pressure again to rely on AI. And knowing how to balance that, I think, is really the secret ingredient. How to balance that and how to work in teams. One of the CEOs that I had interviewed for my work said to me, you know, in this new age, it's really, you know, one of the factors is the ability to quickly work with strangers, were the words that he used. And what he means by that is that companies need very agile teams that you can be thrown in a team with, quote, strangers, as he put it, people you don't know, and have those human skills of being able to connect, to be able to dialogue, negotiate with people that you really don't know, you don't have a long history with. Those are very human skills. And I would also argue that for many people, those are skills that have atrophied as we have become so mesmerized with technology, technological capability. And, you know, in the process of working with GenAI, you know, if you remember even last year, the whole term prompt engineering, we're all getting on the bandwagon with prompt engineering and how do we communicate with the AI. But what I've noticed in, in the process of doing that, what one is doing is they are changing their thinking and their dialogue to suit the machine. And, right? So I am morphing myself so I can speak to it. No, it needs to talk to me. And in that process, if one spends a tremendous amount of time doing that, they're losing the skills to connect with other humans in a very different way.

Again, so much there, but I want to pull out this thread of human-machine interaction and explore it within the concept of practice of thought leadership. Whether you're an academic or keynote speaker, or you're a senior-level consultant leading a practice or have a body of expertise, I'd be interested because you've been doing research and also deep thinking around that human-machine interface. How's the practice of thought leadership going to change, and what skills will a thought leadership practitioner need to excel? Any thoughts?

Yes. So to be clear, I am continuing to think about this, and I would be delighted to come back on the show in 6 months or a year because this is going to change. And there are many individuals that we have seen out there that we thought of as thought leaders. And suddenly, yeah, I'm thinking of some names that I'm trying, I'm holding back not to mention in the both tech space and political front. And then you see their ideas become stagnant and morph. And then if they do morph, then you hear the political backlash of, well, you're flip-flopping, which is sort of this term that I don't think is, is correct. And the reason for that is that as a thought leader evolves in this era, your ideas are going to somewhat change. Your moral stance, your beliefs can stay the same. But if you're truly a thought leader, you're going to evolve with the evolving technology, with evolving businesses, and it has to evolve. You and I cannot be having the same exact locked-in thought process that we did pre-COVID. There is one of the many people in corporate world that I work with, and she and I have been working in this space together, writing papers for a while. And recently she said to me, you know, a few years ago I said AI is not going to take our jobs. I think differently today. Today I see that it will take over jobs.

It will.

So then it becomes a question of what are the new jobs created and how do we position ourselves for those new jobs? That is very much a very different stance in terms of viewing the role and the capability of AI. But I think what leaders are doing and the kinds of tools that they are using are evolving and are changing. So I spend time with many CEOs. One of them, actually, he's a CEO of a company called Aleene Candles in New Hampshire, and they've actually implemented, and he's kind of pushing this, even meditation in the company, just however one does it. But the idea that you pause and that you think for a little bit in terms of what are you doing, what is happening, how has the world changed, how will you have an impact, how one does that. How that takes place is going to vary. But I will say from my end, I think it is important to take time to think. Whether one does it when they're at the gym or have other outlets or other ways, I think for leaders to take time to pause and to think on a regular basis is something that I think isn't talked about enough. And you could call it doodling. You could call it blue sky. You know, I call it blue sky, but you have to take time to actually synthesize and think. As humans, what are we good at? What do we do that machines cannot do? And one of those things is that we are able to bring together very disparate ideas, ideas that are, that don't seem to come together and then all of a sudden have those aha moments of ingenuity, innovation. You know, so many examples I can give, Bill. A great example is, you know, I hate to use it because it's been used, but it is true, is the, you know, the yellow sticky notes. Oh my gosh, the yellow sticky notes are a, if I can call it technology or the glue that was innovated that you know, 3M, that nobody knew what to do with, right? I mean, what do I do with this? And then, you know, 3 years later they go, hey, this would be really useful. And now none of us can live without it. And it has made a heck of a lot of money. Okay. So it's those kinds of things that we bring together. And that is not going to happen if we as thought leaders don't take the time to think. I mean, Jeff Bezos has said this very, very well. Leaders are paid to make a smaller number of really important decisions. And that is a very important thing to remember and not to get caught up in those rote, multiple decisions that can be automated, but leave the time and the bandwidth to actually process and to create a system of how that takes place. And that will vary from leader to leader, but creating a process and a mechanism, I think, is going to be increasingly important for thought leaders to do that.

If you're enjoying this episode of Leveraging Thought Leadership, please make sure to subscribe. If you'd like to help spread the word about our podcast, please leave a 5-star review at ratethispodcast.com/podcast. /ltl and share it with your friends. We're available on Apple Podcasts and on all major listening apps, as well as thoughtleadershipleverage.com/podcast. I love that, that space for thinking, that space for cross-domain connections to either produce the aha or a hypothesis or question worth investigating. Because I think one of the problems that GenAI faces is even though it has a massive dataset, it is constrained by its dataset, right?

Absolutely.

And so the ability to expand beyond what is already in an AI's dataset and ask questions that truly are being asked for the first time on a human level, I think is one of the distinctions. And I think also I heard in there a comfort with uncertainty and complexity and recognizing, and this goes back to humility, you don't know a lot, right? And so I'm thinking about your career path a little bit. From fluid mechanics, neural net, logistics— those are all very complex systems where often, certainly through history, what was known was only a fraction of what was actually happening. And so you have seemed, at least from an outsider's view, to be drawn to that complexity and uncertainty. Am I reading that right?

Yes, you're correct. And I think it's important, you know, as, as you have mentioned and as we've talked about thought leadership, human skills, I'd like to— I always like to remind people that I come from a very, you know, STEM, traditional STEM background. And the reason that is important is that I did not approach my research and my work from the lens of a behavioral scientist, a psychologist, somebody that is, you know, rooted and I'm going in and I'm searching for behavioral answers. I am an undergraduate in mechanical engineering with specialty in fluid mechanics and very, you know, neural nets, very, very, very quantitative. And I think from the very beginning working with companies, I came in, and there were many, many times when I thought in the very early part of my career where I was going to come in and I was going to offer these dazzling solutions that are quantitative, and I was going to impress everybody, only to completely fail. And what I really have to say when I look back at the younger me, I just picked myself up. I went back and I said, OK, that was kind of foolish. So what do we learn from that? How, you know, just me talking to myself and saying, What can I learn from this and how do I do it better? And then picking myself up and doing, learning from that. And this path has taken me to where I am today. And I'm continuing to learn that I don't have the answers. The world is truly complex. And I think understanding that, I think, is really where the answers lie because nobody has the answers. I, I'm at a point in my career, and I have been for some time, that the moment I see a categorical answer, I know that the individual saying that does not really understand what they're talking about because it's about probabilities. It's about risks, probabilities, and there is always the highest likelihood that we could say, well, the highest likelihood is X, but there's also the likelihoods that It could be instead of A, it could be B or C. Probabilities are da da da. And then understanding that it's what we call a rolling time horizon. It's going to be different as more information becomes available. This is the way thought leadership at governments, politicians, and businesses, every enterprise needs to be today is to understand that We are continuously updating probabilities and doing scenario planning. Forecasting today needs to really be about scenario planning and continually updating that because the environment is changing, as opposed to saying it is X, because the moment— and I know enough to know the moment a thought leader says it's X, okay, I know enough to know that is not so, but give me scenarios and give me risks. And now I know that there's a reality.

So what I love about that is that really creates a framework for what I would describe as a Bayesian approach to follow—

Correct.

—leadership, right? And so you're constantly reevaluating what do you know and what do you believe to be true based on new information that either confirms or disproves the way that you've been thinking or adds a new perspective. Absolutely.

Without question. And I've often used the term, so I'm kind of excited, Bill, about, uh, you, you said the Bayesian approach, because indeed it is very much the Bayesian approach. And when one understands that, they begin to— because I actually visualize this, I sort of visualize this Bayesian model And it's changing. So as an entity, as a person, as an organization, if I were leading, if I was a CEO or, you know, leading a company, senior VP, I would organize my team to be very agile. There's a lot of things that I would do with regard to training. And, you know, I'm happy to describe that if you, if you like, but where everybody is agile, we are continually updating the outcomes based on information. A lot of the information that is again going to differentiate excellent leaders and companies from good enough is not going to exist in AI. And the reason for that is that that information is not in the historical data. It's going to be information that I gather in other ways. It could be through gossip. It could be through rumors. It could be through informal channels, back channels, water cooler, whatever it is. But that is not existing in the AI algorithm. And if you're Pepsi and if you're Coke, we're all going to have pretty much the same access to the same AI LLM models. But what's going to be the difference? The difference is going to be that thought leader or leaders that are out there gathering a lot of informal cues, a lot of informal information, and knowing how to layer that on top of the AI output and updating those probabilities on a continual basis. Now that is the winning formula, and that is the part that I'm astounded how many businesses don't get it.

Well, and I think you could layer that to several different things, right? It is easier naturally to accept a premise, hold on to that premise, and continue to assume it be true. It is much harder and take some of that reflective work, that self-awareness to ask questions of this has been true in the past, or I assumed it to be true. Is it still true? Right. And so we're almost standing at an intersection between STEM, the humanities, as well as sort of meditation, like you talked about, the ability to reflect and have it take that expansive view becomes so critical.

Exactly. And, and again, just to underscore, I come from a very STEM background. So that is very important to note that I'm not coming in as, you know, this meditation guru that's been doing this or behavioral scientist. I come from very traditional— differential equations was probably one of my favorite classes. So seriously. So no, I'm being honest. I loved, I love that. But so, but I had to change and I had to learn. One of the things that you just touched on that I think is extremely important is to have breadth, breadth of knowledge. And I think one of the, one of the really big significant changes that I'm seeing that I think is important And this needs to be coupled with training and all of it. When we're talking about thought leadership, is not to be siloed. I think historically in the past, we had leaders that were experts in their domain. Could be finance, could be marketing, could be purchasing. It could be, you know, you name it. But what we are seeing now is the need to triangulate between different, different areas and to understand the bigger strategic vision of the enterprise. So AI then can be relegated to the rote tasks, the things that are automated. And then as a leader, one needs to have not only the time to reflect and to have that be built in, and I think being a little bit trained in terms of the ability to be able to do this. Is in addition to have breadth, to be able to be such that they, he or she or they can be, they can triangulate between disciplines and areas and be able to speak many different languages within the corporate world. This is, I also think, a very important wake-up call for, I think, boards, because I think boards really need to hold their leaders accountable and not push so much the very quick ROI that we've seen in the past when it comes to technology. Because what happens is CEOs and leaders will stay maybe in their position, say, 3 years. Board members tend to be in their position longer. What we've seen historically, oftentimes senior vice presidents, CEOs might say, well, I'm going to maximize my time very quickly where I am. And I'm going to get a return that showcases, displays, showcases my, my talent. But what happens afterwards? What are the consequences when I leave? Well, I really don't care because I'm moving.

Yeah, I'm a CMO who has an 18-month tenure. I'm in, I'm out. I've got time for one strategic initiative.

Exactly. And I think the challenge now is for companies, we want to stay in business for a longer period of time. We want to make sure that we've got the structure and the infrastructure and the system in place to be here in 5 years. And that is not going to happen if we are blindly just acquiring technology and AI. So it is really up to boards to put this kind of direction on their leadership as opposed to just very quick pressure. Give us something in AI. I think that's going to— that is very short-sighted.

So we have covered a lot of ground in this conversation, Nada, and I want to thank you for such a rich conversation. Before we go, I want to ask you one last question, and that is, in terms of the craft of thought leadership, is there a book on your shelf or one that you recommend to others that has helped you become a better thought leader? And if so, what and why?

That is a great question, and I can't answer that only because there are multiple books and many of them come out of philosophy, actually. And I—

Feel free to name a few. Yeah. Yeah.

So I am a big fan of Nietzsche, and there are, I think that anything in the Nietzsche space, I think anything in the Marcus Aurelius space, I think that if we look at some of, it's really amazing to me if we look at, if we read somebody that was in a very different decade or century than us, how many lessons we have. But all those writings give us pause in terms of what the world looks like. And I'll just say one of my favorite pieces that isn't really related so much to technology or to business is a short story by James Joyce that I love. It's called "The Dead," but I love it because it juxtaposes— it's in his book called "The Dubliners." And it juxtaposes a scene of partying, merriment with really death. And it puts in perspective what life is. And it makes me think. And I think, I think that the ability to think and the time to think are probably one of the most important things that we could be doing now.

So you hit on an answer with Joyce, which brings delight to me. Because in my PhD program, I thought for a long while I was going to be a James Dean scholar. Wow.

I'm a huge Joyce fan. Okay. I am. Yeah, I'm, I'm a huge Joyce fan. It is such beautiful— his writing is so beautiful. It's artwork, really. And I admire it so very much. And those are the things that give me that quiet, that ability to quiet down. And to really take a bigger view of life, which I think is really important as we are caught up in massive speed of Instagram and X, Twitter, and all the social media. And what we're doing is, Bill, we're reacting, we're reacting, we're reacting, and we're not taking time to think and to pause. And there are so many consequences for us as humans. I think more than ever, and I think we've touched on this, we didn't have a chance to delve in, but the ability and the need to further develop our human nature, our human skills, is something that I think is going to be the winning formula. And a lot of that is going to go back to, it's going to go back to original writings of humans, people like Joyce, the slowing down, actually reading books, as opposed to flat-screen TVs.

Diving in and taking more time than you can allow with an 18-minute TED Talk. So, for example, with Joyce, to go one step— because I can't pass up the opportunity— for me, I would turn to Ulysses as an example of how better to practice thought leadership. Each chapter written in a different framework, style, and structure with a different perspective, telling a continuous story, but on multiple layers. And so you have to be as aware of the lens and the framing that the storyteller is using, as well as there are layers within words, and often there are multiple things going on in the same sentence in terms of allusion. And that richness and complexity leads to the ultimate question of, what do I know and what is going on around here? Exactly.

And that is what AI cannot do. And what you notice, what you just said, the multiple layers, even in a sentence of richness and complexity, multiple meanings. Multiple messaging is really— these are very human kinds of skills. And there's so much that we as humans, I think again, we've atrophied so much of this, and that is what we need to go back to. And I think that is the key to thought leaders.

We're gonna have to leave it there. Nada, thank you. And I will look for you next time around Bloomsday. Thank you.

Thank you so much for having me, Bill.

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