Innovating Leadership:
Co-Creating Our Future

Resources:

Neil’s book, Own the A.I. Revolution: Unlock Your Artificial Intelligence Strategy to Disrupt Your Competition, can be purchased here.

Learn more about Neil on his website at NeilSahota.com.

Our host, Maureen Metcalf, posts a newsletter every week on LinkedIn. You can follow her there at https://www.linkedin.com/in/maureenmetcalf.

She’s also written several award-winning books on leadership development. You can peruse and purchase them here.

Guest(s):

Guest(s) Bio:

Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) AI Advisor, author of the book Own the A.I. Revolution, and Professor at UC Irvine.  Over his 20+ year career, Neil has worked with enterprises on business and marketing strategies to create next-generation products/solutions powered by emerging technology as well as helping organizations create the culture, community, and ecosystem needed to achieve success such as the UN’s AI for Good initiative. Neil also actively pursues social good and volunteers with nonprofits. He is currently helping the Zero Abuse Project prevent child sexual abuse as well as Planet Home to engage youth culture in sustainability initiatives.

Transcript
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Maureen: [00:00:00] Welcome to Innovating Leadership: Co-creating Our Future. I’m your host, Maureen Metcalf, the founder and CEO of the Innovative Leadership Institute. Today we’re gonna be talking about Own the AI Revolution. With me today is Neil Sahota. He’s an IBM master inventor, United Nations AI advisor, author of the book Own the AI Revolution, and professor at UC Irvine.

Neil, I’m delighted to have you join us today.

Neil: Hey, thanks for having me on, Maureen. I love the show and it’s an honor to be a guest.

Maureen: Thank you. And Neil, I’m looking forward to hearing more about what makes someone actually innovative.

Neil: You know, this is the funny thing, Maureen, is that everyone always thinks that you have to be like Elon Musk or Jeff Bezos, that somehow the light just clicks for you.

And at the end of the day, if you think [00:01:00] about innovation or even disruption, we’re slowly connecting all these dots together. It’s not till we get to that last dot we complete the picture, we go the aha moment. And I think we just gotta get better about how we find those dots. And well, we can do that.

Anyone can do that.

Maureen: That’s the encouraging thing, that anyone can, that it’s not the, quote, “special people.”

Neil: For sure. Oh, I think a lot of us would be in trouble if that was really the case, but we all have the cognitive capability.

Maureen: So let’s jump in then. If we all have the cognitive capability, why do we struggle with invention or innovation?

Neil: Well, I think it starts with the fact we don’t actually teach people how to think critically, let alone creatively. I think the impression is, well, schools will do that, but they don’t actually do that. They teach important things, but they don’t teach us how to think, and we get locked into this mode of you go into work or you go into a restaurant, wherever it is, and this is how things [00:02:00] work, and so you never really question that, right?

Things work good enough.

Maureen: It is interesting, even in teaching leadership, when we start talking about personal vision, the question is just, I never thought I had choices here, and fill in the blank. I never thought I could think about what I wanted. I’m just doing the stuff I’ve gotta do. You know, when I asked my dad if he wanted children, his answer was, “I didn’t know I had a choice.”

The, the… That’s kind of a big assumption.

Neil: That’s like the movie Orange County where that guy wants to go to college, everyone’s like, “Why do you want to go to college so bad?” He’s like, “That’s what you do after high school.”

Maureen: It’s not just the little stuff. It’s significant decisions in our lives that often people assume a path without choosing, and I think that’s really getting to your point, isn’t it?

That we often make a set of assumptions that lead us down a standard path, absent thought.

Neil: You know, a good example of this [00:03:00] is 15 years ago, I was working with the large airline carrier group, and they were trying to integrate their operations and really uncover a lot of the processes to figure out how to do this.

And being the outside guy, I kept asking questions like, “Why do you guys do it this way?” And they’re like, “Well, we’ve always done it that way.” So after 40, 50 years, you don’t think there might be a better way of doing this? And I remember someone saying like, “Well, why do you wanna fix something that’s not broken?”

Right? And I, I think we probably all heard something like that before, and it’s like, we’re not trying to change for change sake. There’s a better way of maybe doing this, that we have new tools, we have new technologies, we have more data. Can we exploit that? And I don’t think that really connected with them until I remember we got to this one part and, you know, fuel was a huge cost even 15 years ago, and I saw this number, 143.

And that was what they were using as the average weight for a person on a plane. I’m [00:04:00] like, “Does the average person weigh 143?” Like, “Well, that’s what it was when we commissioned the study, like 50 years ago.” Pretty sure people weigh more today. They would start thinking about it, just looking around the room, like, “Huh.”

And when they got the new number, which I will, I will not repeat all, they were, they were kind of in sticker shock, so to speak. That’s kind of when they realized, whoa, wait a second, maybe some of these things we’re doing aren’t the best way of doing it anymore.

Maureen: Well, and I’m just thinking as you say that, the implications of being fuel light on a plane, because I’m assuming also people carry more luggage now than they did 50 years ago.

Neil: Yep.

Maureen: So that means when there’s a bad weather pattern and we’re circling, the plane may not be as equipped to circle from a fuel perspective.

Neil: Back then, that’s potentially the case. I’m sure you had some pilots asking for more fuel or even thinking like, oh, I gotta try to use my tribal knowledge to compensate against what’s probably real and what’s calculated.

You know, I’m sure there’s [00:05:00] some fights where, you know, airline companies about, oh, taking more fuel on. That’s the thing we’re actually looking at.

Maureen: Interesting. It sounds then like innovation starts with questioning the norms.

Neil: For sure. So I developed this framework I call TUCBO, which stands for think different, understand different, create different, be different, own different.

So it’s soup to nuts ideation to full rollout. And the think different, the first step is exactly that. There’s techniques you can actually use, and one of them actually is question assumptions. ‘Cause we think assumptions are truths, and they’re not. They’re actually uncertainties where we’re just gonna say, like, “I’m gonna pretend it’s gonna hold true and see what happens.”

Maureen: Those carry forward then as truth over some period of time.

Neil: Think about it. I don’t know if you’re familiar with the story of how Zipcar got started. It was always assumed people needed to own cars. And the guy that, you know, thought of Zipcar, one day he was like, “It’s the most [00:06:00] expensive thing I have, and I don’t really spend that much time in it.”

So he actually researched it and realized the average American spends about one hour per day in their car. It’s 23 hours idle. So he came up with this whole thing like, why do you need to own a car? It’s just sitting there. What if you could just get a car on demand, right? And that’s how he started his Zipcar company and wound up, you know, disrupting the rental car business.

But, you know, we live in a time of rapid change, Maureen, and soon people are thinking like, “Well, why do I even have to go to some place to, like, get a car on demand? There are people driving around all over the place. What if I get carpool on demand?” And thus was the birth of rideshare.

Maureen: Which is amazing.

And for someone like me, cabs were not always reliable in Columbus, Ohio. So I’m, you know, going to the airport and I’ve called a cab and they act indignant that I want them to show up when they promised. So I stopped taking cabs. Uber or Lyft, I can tell on the app where they are, and if they’re not there, [00:07:00] I can make alternate arrangements.

Around the world.

Neil: Around the world. You don’t have to carry money. You know who your driver’s gonna be. It’s a huge convenience, right? And so you look at like the rideshare guys. So you have a good idea, but now how do you know the idea has value? And this is where the U, the understand different comes in, ’cause you actually kind of have to vet and check based on your customer’s needs, is this really gonna be something they’re willing to pay for?

I believe everyone has a billion-dollar idea. It’s how much of it is it really gonna create the value? You gotta check that to get, make sure it’s an idea worth pursuing. I’ve seen too many, especially not just business people, but entrepreneurs, they think of something amazing and then they kind of build the product that they want rather than the customer needs.

Maureen: And that’s from a human costs perspective, heartbreaking, watching a friend, ’cause I think we’ve probably both had friends who’ve started companies. To your point, they were good ideas, in some cases brilliant ideas, and then they use up their [00:08:00] 401money. One of my friends lost her house, lost her 401, and the company didn’t go.

The idea was a good one.

Neil: I know, and unfortunately I’ve seen that too many times. There was a group of engineering students, they were in the I-Corps program and I was assigned as their mentor. So this is a program run through the National Science Foundation, and they wanted to create a STEM toy, like an educational toy.

And they actually had done it. They made the instruction manual a comic book, and they talked to their grad friends that had kids, and it was very cool. But one of the things we do in I-Corps is, well, we use Steve Blank’s business model canvas. So the first thing you do is you go interview 100 customers.

And so they’re coming in like, “We have this cool thing. Our tagline is in inventing inventors,” you know- And the parents are like, “Well, my kid is seven years old. I really am not worried about what kind of job they’re gonna get in like, you know, 15, 16 years, and you’re asking me to spend $50 for a toy.

That’s– My [00:09:00] priority is not education.” And they’re like, “Oh, pfft, those parents, they don’t know what they’re talking about. They don’t love their kids.” You know, they talked to another set, started hearing the same thing, and they’re like, “What? What’s going on here?” And by the third set, they realized that they didn’t have the right value proposition.

In fact, I thought one mom put it very well when she’s like, “If I’m gonna buy this toy, it better keep my son occupied long enough for me to do the laundry uninterrupted.” Right? And that was not what they were thinking. They’re thinking educational, it’s bonding time with the parents and the children, and that’s not what the parents wanted.

I mean, I think they were charging like 50, 60 bucks for this toy. And at the end of the day, I remember we were talking about it. They’re like, “How could we have been so far off base?” And it’s like, well, none of you are actually parents. You’re trying to build something that you want even though you don’t have a kid, so you don’t understand the needs fully.

That’s why you have to go talk to 100 customers. That’s why you gotta understand different.

Maureen: So think different, understand different, and that’s a beautiful example because [00:10:00] it really illustrates the practicality of even good ideas aren’t always marketable. So what next?

Neil: Once we’ve vetted out the idea and we know we’re aligned with the customer needs, then we go to the C, which is create different.

So this is where we actually design and build our product or service, again, aligned with those customers’ needs. I know a lot of people think this is usually the hardest part. This is probably the simplest step in the framework, ’cause we all are more familiar with like actually building something. But once it’s built, it’s not like they will come.

How do we actually get this into market successfully? Well, there’s two things we gotta do. The first is the B, which is be different. So we actually have to drive customer adoption, which means we gotta work with our supply chain partners or our retailers, resellers, whoever it is, so they understand, c-can communicate that value.

We’re using the right channels, the right words, the right incentives to [00:11:00] connect with our customer base. They understand what they’re getting and why this is not just new and improved, but where the value or the problem’s gonna get solved for them.

Maureen: And so that’s the B of Tukbo.

Neil: Yeah. And then the last part is the O, which is unfortunately the one I think a lot of companies trip up on, which is own different, meaning you have to create the infrastructure, other things for people to want to buy the product.

And I always use Tesla ’cause they’re a great example of owning different. They’re not the first electric vehicle company, but why are they the most successful? And you know, some people point to, well, they improved the battery technology. And they did, but not radically. They made some stylish cars, but that’s not the reason everybody really go out and get it.

The biggest fear people had was, “I’m gonna run out of juice. I’m not gonna be able to charge my car up.” And so Tesla owned that and said, “We will build the infrastructure of the charging stations. We’ll go [00:12:00] out, we’ll negotiate, we’ll pay the fees, we’ll build all the stuff,” and took a reason to say no and buy electric vehicle.

They really own that infrastructure.

Maureen: My partner owns a Tesla, and it is at this point the only electric car we would have. And wintertime, Christmas Eve, we were one mile away from a charger and were able to kind of slide in at around midnight after seeing his son for Christmas Eve. The charger we were going to was closed for some reason, and we ended up driving around in the middle of the night.

I don’t ever wanna do that again. The good news is there are good options, ’cause we got to a toll road, found a charger, and it was charging at, like, seven miles an hour. So we were gonna spend the night in the car. Now, why do you have a charger at a toll station that charges so slowly? Back to the infrastructure, the only way I could [00:13:00] imagine owning one of these cars is that I could actually get where I was going away from my neighborhood, or people are required to own two cars still.

Neil: Yeah, and that becomes inconvenient or too costly. That’s why, you know, Tesla has been so successful. They literally built out those whole infrastructure. I mean, if you ever do the trek from Los Angeles to Las Vegas, in the middle of the desert by Joshua Tree, there’s this little area that has, like, 200 charging stations, and you see it all packed with Teslas ’cause Tesla built that.

It’s a 300-mile drive.

Maureen: And not every car is programmed to go 300 miles. Some of them are just shy of that, but just shy is enough.

Neil: Yeah. And

Maureen: interestingly, I don’t know what happens when you run out of juice, but I’ve not seen Teslas hooked up to tow trucks very often.

Neil: Neither have I. Tip my hat to them.

They really thought it through well.

Maureen: That still doesn’t account for drivers who [00:14:00] don’t pay attention. I’m sure there’s somebody.

Neil: I had an Uber driver in San Francisco recently. He had a Tesla, and we were chatting, and he was showing me that he had spent a grand total of $8 charging his car for the month, and I think he’d driven close to 2,500 miles.

And I was like, “How you do this?” He’s like, “You learn things. You learn, like, where the free charging stations are. You know, you’re going to San Francisco Airport, there’s actually a set of free chargers out there that you take advantage of.” And he’s like, “And I always monitor how much charge I have left, how many miles.

So I really plan things out to take advantage of that infrastructure.” And it’s like, that’s amazing. You literally drove enough miles to go cross-country, and it cost you eight bucks.

Maureen: Back to your point of own differently. We charge at home, and I think it costs us $50 a month. And Mike drives every day. I don’t know how many miles he’s driven, uh, probably 1,500 a month or something.[00:15:00]

And our house is, we’re still considered efficient electricity users even though we’re charging an electric car.

Neil: That’s still pretty good. I think most people would be happy if they just spent 50 bucks a month in gas at this point.

Maureen: I filled up my car the other day. I waited for gas prices to come down, and it was $90- Woo

for a tank, and I don’t drive a big car. I drive a… Well, I drive a sports car, but it’s not an Escalade or something. My tank’s 16 gallons. He can drive every day for less than I can fill up my car, half.

Neil: That’s amazing.

Maureen: And I realize the Tesla itself is not cheaper, but over the cost of the life of the vehicle, one could make the argument that it will end up being cheaper.

Neil: That’s the whole thing. It’s cheaper. Hopefully, it’s more environmentally friendly. That’s the goal. That’s a great example of how you can actually innovate [00:16:00] even something as old as the car industry. ‘Cause, you know, electric vehicles, it’s not like we just thought of them, like, 20 years ago or something.

It’s, it’s something that’s been experimented with since the ’70s.

Maureen: Part of what I hear is there were people who have a brilliant idea, but innovation can come in any part of the value chain. So I could be doing something that others have done. So Uber replaces taxi. Moving people around in a shared car isn’t a new concept, but the use of technology, especially with Moore’s Law, that the technology is doubling every 18 months, means that even what makes sense today, next year for me may not make sense.

Neil: That’s right, and you’re actually hitting upon one of the key points I call out to people. When it comes to technology, w- we’re used to it being about automation, doing something faster, cheaper. We’ve got whole new tool sets from artificial intelligence and VR, [00:17:00] blockchain, and, you know, Web.30 that we don’t know how to actually figure out a different way.

This is what I really consider innovation, to find a different way of doing the work. We’re trying to just replicate what we already have rather than find that new path.

Maureen: Yeah, I read an article, I think in the ’80s. It was something about are you automating the cow paths? Are you automating the old out-of-date processes and just doing them quicker?

But back to your luggage tagging, are we using the advent of technology, and I think of cloud-based ERP systems. We now structure the business around the automated processes rather than me changing the ERP to match my old processes. Now, not everyone loves that, I’m sure, but it is an example.

Neil: Yeah, I think that’s actually what is happening.

That’s the problem, that again, we’re familiar with the process of what we’re doing, [00:18:00] and oh, there’s something that will reduce errors, like luggage tagging. We can put an RFID tag as part of the little sticker we put on there, so at least we can scan it But we still have lost luggage, right? And they’ll tell you like, “Well, we’re not sure where your luggage is.”

Okay, so you just lost it faster? I don’t know.

Maureen: Yeah, how can that be?

Neil: That’s the problem. They, they’re not really going back and saying, “Okay, where are the fail points in our luggage cycle,” right? “Can we fix those process issues?” And it’s like, well, we… everyone has a scanner, right? Th- there’s a tag there, it’ll get scanned, but we didn’t actually address any of the fail points.

That’s not innovation, that’s automation.

Maureen: Again, that seems like a crucial distinction for anyone running an organization that wants to give a better client experience, a la the airlines. Or anybody else, frankly, right? We all have places where we automate stuff because we know how it works and it makes sense to us.

And then add in AI. Can you talk a little [00:19:00] bit about artificial intelligence? ‘Cause I realize that is your topic. And how does that start to really upend how we look at the world?

Neil: The thing about AI is it’s not like searching through a giant database to give us an answer we already know. The real power lies in that it actually can do tasks that require some level of cognition.

So it could actually figure out answers to questions we don’t know. It has the ability to learn. It’s not programmed, and so that’s why it can actually solve problems. We’ve used it to retarget proteins for cancer, to like accelerate drug discovery. We’ve used it actually to help depressed and suicidal teenagers.

We’ve used it to help farmers in impoverished areas to be able to improve crop production. If you ever watched the show Black Mirror- You know, there was an episode with Jon Hamm, I think it was called White Christmas, and they basically replicate off your brain an AI, like assistant. And so the AI knows [00:20:00] you as well as you know yourself, and so it can anticipate your needs and do some of these things.

That’s literally the holy grail for most people, I think, in the world. Not just the AI folks, but to have someone that knows all that, can anticipate. So again, not just obviously trying to cure cancer and stuff, but serve our basic needs. That’s the power we’re, we’re trying to tap into. And to give you an example, we’ve seen that AI is actually better at reading the emotional state of a human being than another human being is.

Maureen, think about it. You’re looking at someone, you’re wondering if they’re lying, there’s over 2,000 points on the face that reveal a lie by itself. The best human being can watch, you know, five to seven of those in real time. AI can watch all 2,000.

Maureen: I’m not sure I wanna know when someone’s lying to me.

In some cases. Nice haircut. I just had a mammogram and there was something abnormal, and my question to the person doing the process was, “Do [00:21:00] you have an AI interpreting the films?” And my hope was the answer was yes, because the AI updates regularly where any human being can’t necessarily process all of the new science.

It’s not like the technician, even if they’re brilliant, is going home spending eight hours a night reading stuff. But the updating of the AI and that it itself is learning is a game changer.

Neil: That’s the key thing, right? I can’t read 20 million medical studies, and no human doctor can, but an AI can and has done that.

And it’s not we’re trying to replace the doctor It’s a new tool for the doctors and nurses to use, right? You don’t need to try and remember, oh, what was that journal article I read three months ago? The AI knows it.

Maureen: And they can match whatever is going on in my physiology based on the millions of pictures they’ve seen versus my doc who hasn’t seen presumably all [00:22:00] pictures ever made.

Neil: Yeah. Uh, in back in my IBM Watson days, we had a, a case. There was a woman in Japan, she fell ill, saw her doctor like 15, 20 years. You know, he ran some tests, they tried some things. They were starting to see some specialists, similar thing. After seven months, they didn’t know exactly what was wrong with her, and so they said, “Can we get Watson’s help?”

Watson has read 20 million medical case studies, right? It’s read all these journals and stuff. And so it took all the great work the doctors had done, all their tests, looked at her genomic sequencing, her personal medical history, family medical history, asked a couple of questions, and then said, “I think she has these two rare forms of leukemia.”

The truth is, the doctor’s like, “Well, there’s no way that can actually happen, but could we just test for it?” Tested for it. She tested positive for both forms of leukemia. Now that the doctors knew what was wrong, they were actually able to put her on a proper treatment plan to get some level of recovery for her But it was something so rare and so unbelievable, it was never on anyone’s radar that [00:23:00] this could be the case.

And here’s the machine that says, “I’ve taken all the great work human people have done, and I remember all of it, so I can apply that knowledge to help you.”

Maureen: Presumably that saved her life.

Neil: It did. So, and I don’t think any of us would say that we would’ve figured it out on our own.

Maureen: Many of us, like me, can’t just call up Watson and say, “Hey, computer dude, I need your help.”

So how does what Watson has done, that I’m assuming will be replicated, move into the world? And in that, I also understand there are ethical questions of what should be known, what shouldn’t be known, what should be shared. Curing diseases is certainly, I think nobody argues with that, but that means also Watson can do other things that may be unsettling to people.

How do we move this into the world in a way that doesn’t evoke the bad movie sequences that we’ve all seen at some point?

Neil: That’s a really important [00:24:00] question, Maureen. And so all we have today is what we call ANI, artificial narrow intelligence, meaning that the machines can only do what we’ve taught them to do.

They’re not thinking on their own. They’re not like, “I want to learn how to drive a car, so I’m just gonna do that.” They can only do what they’ve been trained to do, and they only do it when they’re asked to do something. They’re sort of passive systems. But the whole goal here is not like, okay, well, we’ll have Watson or Google Brain or something like that try and do everything.

The whole goal is actually to open up the technology. This is what I, I fought for, like 10 years ago, 11 years ago, in that this is such a game changer, that it’s a new form of computing. It’s triggered what we call the Fourth Industrial Revolution. We have to get the power into the hands of the people that understand the pain points There are lots more technologists, but they don’t know the challenges of a doctor, a nurse, a lawyer, a marketer, a financial analyst.

We have to bring those people into domain knowledge and what they’re trying to do to bear. These are just [00:25:00] tools. So we need people that can make the hammer, the technologists, but we need people that understand how to use the hammer, which are the business and domain experts. And that’s what we’ve really done.

We’ve, we’ve turned these capabilities into, like, APIs that are things that can be used, and that’s what people do. So, you know, Sloan Kettering built an AI tool to read X-rays to try and detect lung cancer. Some of the police departments now using AI as chatbots to take police reports. And they actually ironically feel a little bit safer and non-judgmental, but now you’re also freeing up more police officer time to be on the streets.

So it’s these types of things that are actually going on with AI. And so when it comes to the scary stuff, it’s on us ’cause we as humans are making a choice of how we wield the tool. How do I wield that hammer? ‘Cause I can use it to build a house or rip a house apart.

Maureen: One of our friends was funding research looking at how do you program love or empathy, to use a [00:26:00] more comfortable word for a lot of people, into AI so that the unconscious bias of the programmer doesn’t adversely impact.

Can you say anything about that work?

Neil: That’s a big area right now ’cause we’ve learned that that unconscious or implicit bias, the machine will learn our bad habits or our bad ways of thinking or our skewed ways of thinking. So the United Nations, for example, they’re really big about AI robot judges. If we can get this off the ground, we can improve access to justice, less corruption, more, more court hearings, that kind of stuff.

The challenge is the data we have, the data, you look at the US court system, we have the data to do this. Is it biased? And I think most people say like, yes. Some of the bias are things we’ve not considered. There’s the, the racial element, which for sure we’ve seen, that most people are probably aware of.

We actually studied this. Do you know what the biggest influence is on a judge when they [00:27:00] make a ruling or sentencing? Take a guess, Maureen.

Maureen: Yeah, I wanna say something cheeky like their shoes are too tight that day. Um-

Neil: You’re on the right track with that. What we discovered was how hungry they are has the biggest influence on how they make their rulings and sentences.

The more hungry, the more harsh. How do you factor that in now? Or how do you try and strip that bias out of the data so that the, you know, the AI doesn’t pick that up? It’s about time of day becomes a factor.

Maureen: So 3:00 in the afternoon might be my least empathetic time of the day. Lunch has worn off, I haven’t had my snack, and I’m gonna be harsh, whether I’m a judge or just a parent.

Neil: Yeah, 100%. But the thing is, what if that day that judge actually didn’t have lunch? Then even though we try and adjust for that, it’s like we’re not actually adjusting properly. Or maybe they didn’t have breakfast, right? Or maybe it’s night court. I, I mean, there, there’s so much variation. That’s the challenge.

The more variation-

Maureen: Mm-hmm …

Neil: the more [00:28:00] difficult it becomes to actually strip out that bias and train the AI.

Maureen: Is there some way to pick the, quote, “best judges” and have them overweight their calculations compared to others?

Neil: In theory, you could do that, but then how do you define best judge, right?

Maureen: The ones who aren’t so harsh and mean.

I mean, you s- you just said that you can tell that they’re giving, maybe within the parameters of the law, the harshest sentence.

Neil: It could be, but then there’s the other factors that come into play, right? The, the racial equation. For some cases, gender. Sometimes it’s venue. We’ve seen judges, like in the Ninth Circuit, may be more strict than judges in the Fourth Circuit.

So you can see there, there’s so many different things going on. It’s… In a lot of cases, it’s impossible to strip out all the implicit bias. Data inherently is biased because we’re biased. We could try and get as close as we [00:29:00] can. One of the research projects we got going on is can we teach an AI to look for these things, these implicit biases, and strip them out?

But again, there’s so many variations of unconscious bias.

Maureen: What I hear as I’m listening to you is, as it’s learning and stripping out bias, it’s still gonna be better than most hungry humans.

Neil: That is definitely I think the case. Real simple example is actually mortgage lending So, you know, the data shows that there seems to be some gender and racial bias in loan application approvals and the interest rates that people qualify for.

And as more and more, actually, lenders are now using AI to actually review applications and, and generate the scores, what they found is that these groups, they’re actually getting approved more often and actually at lower interest rates. We know that we can actually strip out some of this bias. Not [00:30:00] all of it, but some of it.

Maureen: So that seems like a really interesting application. My guess is I’m aware a little bit of the medical, and you talked about reading films and diagnosing, and even things like genome sequencing, that now I can go in and have my genome evaluated. Uh, something probably like 23andMe, but infinitely more complex.

What other areas… You’ve talked about judges. What else?

Neil: Marketing is actually– They’ve been whole hog on AI in the past decade. We’ve developed tools now where, you know, AI is not just doing, like, the sentiment analysis. That’s actually become passe. But just with public information like your LinkedIn profile or your, like, Instagram posts, the AI has enough data to generate and figure out your psychographics and your neurolinguistics.

So psychographics, basically your personality. It can actually score you across 56 personality traits and actually decipher your hobbies, your interests [00:31:00] Neurolinguistics is the science of words. Really hard to mask. And so AI’s been able to now figure out and say, “Okay, this is their learning style. This is their level of commitment to something or their level of interest.

If you wanna talk to them, you should use these words, use this channel, and offer these incentives to convert them.”

Maureen: If someone looks at my LinkedIn profile and looks at the… Like my Grammarly always says I use more unique words than whatever percentage of the population, and I’m less efficient in commas.

Looking at my LinkedIn profile, someone who wants to market to me would then be able to do so more effectively?

Neil: That’s correct.

Maureen: Even though I’m not sharing hobbies and I don’t post cat photos or any of that stuff on LinkedIn.

Neil: Yeah, no, language is like a fingerprint. The fact that you use the word supports rather than helps actually reveals things about you.

Maureen: Here’s the thing. I use Grammarly and it always tells me to change my words. Does [00:32:00] that mask what you can tell about me?

Neil: It might, right? The more data we have, the more confident we are in the thing. You might be using Grammarly for your LinkedIn profile, right, your bio. But you’re not be using it all the time, right?

You might be sending out business emails using Grammarly, but other emails where you’re not. Or, you know, maybe you’re, you’re throwing something on Facebook real quick or Instagram or maybe you wrote a quick blog or maybe you were interviewed in a podcast, right? Definitely not using Grammarly there.

Maureen: That’s the only place I don’t use it. All right, all

Neil: you AI marketers know now to

Maureen: get Maureen. It is both encouraging if I’m doing the marketing, and disconcerting if someone is marketing to me that they can tell so much about me. I mean, part of me says it’s nice to be efficient and not get something that’s wasting my time, but I also don’t want someone to be in my head when they’re sending me stuff.

Neil: I’m with you, right? I, I get it’s more [00:33:00] efficient and it helps conversions, but do you really want people to know that much about you?

Maureen: I still want Google Glasses that tell me someone’s name when I walk up to them so I get it right. It’s one of my worst traits, remembering names and pronouncing them properly.

Neil: We’re not that far away from that being a reality at this point.

Maureen: I believe there are regulations that are the issue, not the facial recognition itself.

Neil: There’s things about PI, personal information, and what’s happening with that data, and can you use some people’s likeness. So that Google Glass, actually, the police in China actually have been using that since 2015, I think it is.

So when they look at somebody, they actually know who they are. They get the name, where they live, where they work, where they’ve been the past couple of hours. In China, a lot of people actually like and appreciate that because it helps find lost children faster, you catch criminals faster. But again, I know that if you look here in North America or in Europe, it’s pretty big brother-ish, you know.

People are a little freaked out by [00:34:00] something

Maureen: like that. It certainly does raise the question if you’re going places you don’t want people to know, especially the police.

Neil: I always think about, you know, the worst case, like the stalker scenarios. Somebody sees you, and they’re like, “Huh.” Put the glass on, they’re getting all this data about you.

Maureen: Mm.

Neil: What are they g- what are they gonna wind up doing with that? What if they come and break into your house, for example?

Maureen: Yeah, that would be disconcerting.

Neil: Yeah, that’s the challenge, right? It’s a hammer. We could use it to create or we could use it to destroy.

Maureen: What other applications? Is there something that you’re totally jazzed about, a problem that AI will solve?

Neil: Actually, yes. So kind of bringing this full circle to how we started. The past, like year and a half, I’ve been doing a lot of work with AI virtual reality, or the metaverse really, and cognitive science, and this, this started off saying like, could we tackle big problems? So the metaverse is kind of like the Mirrorverse in the Doctor Strange movies.

It’s a replica of the real world. You can do practice your [00:35:00] magic, or in this case, problem-solving in there without impacting the real world to see what happens. And so looking at things like climate change or could we even like through mining minimize environmental impact, or trying to help people, you know, overcome anxiety or depression or high pressure situations.

And so, you know, that’s the metaverse. We use AI to kind of develop their cognitive abilities. It’s for the experimentation. Can memorize your way through, for example, all these random things that might occur. What we found is it’s actually very effective at actually encouraging people to find more creative solutions.

So they’re willing to take more risk in the metaverse, and that’s led to better outcomes in the real world. What we didn’t realize was this enhanced creative thinking was actually sustained outside of our work. So it wasn’t just you did this problem-solving. As you went back to your normal work and stuff, that level of creative thinking actually was retained.

So we’re now realizing that we could actually [00:36:00] probably teach people to be better creative thinkers and critical thinkers through this combination of technology and science.

Maureen: We used to play SimCity, and I remember staying up all night doing this thing, and I’m not a gamer, so it was curious that I saw the sun come up.

But we had done all kinds of things like take out all the streets and see what happened. Put parks more frequently than would be economically reasonable if you’re a developer. Where did the people get happier? Where did they get less happy? We learned a ton. Assuming that this was programmed with some level of accuracy, you could go back and find a point in time where a natural disaster happened, solve it differently, and see what you learned.

So it sounds like the metaverse is a much more complex version of that.

Neil: It is, and that’s the thing in that we call it a digital twin. It’s like a replica of your office, your farm, the, the people, the [00:37:00] controls, the furniture. So it has that feeling to it, but you’re put into these situations that you may face one day or you’re maybe trying to solve, but you can do things in there that you probably would never do in the real world.

Like, there’s a financial company we worked with that they were short on cash, and normally what they would do is probably sell off assets. As they went through this exercise, they were able to experiment with things. They were doing things that they thought were completely contrary or contradictive, like, “Well, let’s create a different product offering,” or, “Let’s jump into a different market.”

The AI was using real world data to say like, “Okay, if this is a step you take, this is the outcome, this is the reaction to it.” And it’s throwing in events that sometimes we might consider them even black swan, but they’re actually very realistically could happen. And so by going through this for a week, they actually realized is, we shouldn’t go off selling these assets.

It’s just gonna handcuff us later on and sort of our company being measured in months will be measured in a couple of years. They actually come up with a totally unique solution [00:38:00] that worked. They wound up doubling their stock price in a year as a result So this is the potential we can tap into just by throwing in not just the real-world circumstances, but things that we think may not happen or things that we don’t understand what would actually happen from these actions.

And the freedom to take these risks, well, that’s what gets to that state of flow.

Maureen: It seems like, uh, because my work is with mostly leaders, this has a ton of positive implications for executives as they’re making strategic decisions.

Neil: Yeah. That’s actually how this got started. We were working with the C-suite and boards of big companies.

And what we’ve learned is that we can actually take it down the chain as well. Like, one of the things we’re now working on is, you know, obviously, you know mental health is a big issue these days. You can actually put people into some of these situations before they experience it, so they can actually try different things in a safe space, and that’s how they’re actually [00:39:00] now building coping skills and building resiliency so that if this were to happen in the real world, they’ve actually already dealt with it to a degree and know what to do and how to better manage that.

Maureen: That could be everything from I can practice interviewing for a job, I can practice somebody gave me negative feedback, the things that cause normal… especially young folks who are new in these situations-

Neil: Yep …

Maureen: cause them anxiety.

Neil: 100%. You can go through and realize that you failed a class or someone breaks up with you or your best friend is in a car accident or you lose a parent, right?

Maureen: Yeah, the things that will happen in most of our lives if we live long enough.

Neil: Yep.

Maureen: And we hope we all live that long. So let’s connect it a little more to leadership. How can we help leaders be more effective in everything from demonstrating more empathy to being more judicious in their decisions? I don’t know anyone who says, “That’s a really dumb [00:40:00] decision.

I’m gonna do that.” So most people, I assume, believe they’re making wise choices. I think we all have times that we would look back in retrospect and say, “W- I was not stoned when I did that- … but boy, the outcome looks like I must have been.”

Neil: Well, that’s where I think the convergence of all these things come into play.

The, the TUCBO framework, using some of these things like the AI and the metaverse together. I- and I’ve seen this. The leaders, they’ve invested the time. They think they have a great idea, and often they do. But then they think, like, “Here’s the solution. Everyone will run out and go do it.” Uh-uh. You’re forgetting the B and the O of TUCBO.

You gotta go out and drive the adoption. You have to buy in from your employees. You gotta build the infrastructure so your employees will be successful. It’s not that, “Here’s the vision. Good luck.” Right? “I got smart people. They’ll get it and go.” I, I rarely see that happen. You gotta invest in the, what I always call the people problem.

Most people [00:41:00] even working on a project, they don’t know why they’re doing the project, what the value of the company is. Here’s an opportunity by applying Tukba as a leader, every time you’ll cover that. You’ll make sure you get the buy-in, get the infrastructure. You know, you tap into things like the metaverse and the AI that, you know, I’m working on right now, where you can actually experiment, try these things, and it’s not just coming up with a great solution.

We’ve also done it that as you get this and you try and pitch it to your employees or you have your employees try to execute on that, what happens? So imagine actually being able to see the pushback from, you know, against leadership, like, “Whoa, wait, what are we doing here? We’ve never done that before,” or, “I don’t think that’s gonna work,” right?

So they’re actually forced to confront it and realize that they have to deal with it. I don’t think it’s that they don’t know half the time, it’s that they forget about that, right?

Maureen: And back to we make assumptions. We assume other people see the world the way we do.

Neil: Right.

Maureen: And especially for those of us sitting in the privileged seats of [00:42:00] leadership, we forget what it was like to be afraid of fill in the blank, because we have more autonomy than many of the people we work with.

Neil: I call it the getting cut off example. You know, you’re driving, somebody cuts you off, you’re like, “Ah, that jerk,” right? You have no idea why they cut you off. When you cut someone else off, you rationalize it like, “Well, I have to do this because I gotta go pick up my kid from, you know, soccer or something, and they’re already waiting by themselves.”

So in your mind it’s like, I’m perfectly justified in doing this, but the other driver you cut off has no clue.

Maureen: Or they’re hungry. Um, clearly worse judgment when we’re hungry.

Neil: For

Maureen: those of us perpetual dieters, I wonder if that correlates with poor driving.

Neil: Might be an interesting study.

Maureen: So what do you want our listeners to be thinking about with regard to AI?

My sense is it’s such a game changer we don’t even yet realize [00:43:00] how everything you’ve described makes our world more efficient, right? We’re better at diagnosing diseases. We’re better at implementing solutions that will serve a larger population, and we don’t wanna give stalkers Google Glasses. What do you want people to be thinking about as they listen to this conversation?

Neil: Well, I want them to be thinking about how they can be a driver, not the passenger. I think there’s too many people thinking like, the great technologists with the big tech companies will build stuff and we’ll use it. They don’t know your problems. Mm-hmm. And, you know, in my book on the AI revolution, there’s several interviews, and the most successful, like, AI companies are ones that were started by the domain experts, just people like the rest of us.

And that’s because they, they’re the ones that understand the problems, they know what the needs are. And like I said, you don’t have to know how the hammer gets built, you just have to know how to use that hammer. And so everyone can learn that capability, and we all know how to use a hammer. It’s the same thing with [00:44:00] AI.

How can I just apply that capability? There’s smart technologies you can partner with. They’re probably in your company already that’ll help you help build this. You gotta figure out where you’re gonna go first and what you wanna do. And if you sit around waiting for someone else to figure it out, you’re missing a huge opportunity.

Everyone has the opportunity to be that driver right now, to be that innovator. Grab it, ’cause you can shape the next 10 years yourself rather than worry about what changes are coming.

Maureen: Say I wanted to do something with AI on helping build leadership How would I grab a technologist? There’s gotta be a more precise way for me to do that, ’cause I know some that don’t wanna be grabbed.

How do I find someone that has the AI expertise to align with my subject matter expertise?

Neil: Well, you gotta come up with a opportunity, Maureen, so it’s gotta be something a little bit more prescriptive. So you’re, you’re expert in leadership. What is it that you wanna try and address? Maybe it’s like, how can I, we more effectively [00:45:00] teach young people leadership skills as they grow in their careers?

Or maybe it’s like, how do I help existing leaders like the C-suite address their blind spots, right?

Maureen: We talked about the scenario planning because again, lots of people have really good ideas in a specific context, but the world is changing quicker than they get the idea fleshed out and implemented, and by that time it’s not a good idea.

How do we help them make better decisions? That would be, I think, for any company, a valuable resource.

Neil: I would think everybody would want that, and so when you- if you’re going, like, down this path, it’s not so much you have to worry about finding this great data scientist that understands the leadership and stuff.

You’re the expert, you know the data scientists, the machine learning programmers are gonna be looking to you and say, “Okay, you’ve explained to us what you wanna do.” What data do we have, right? And that’s how things will start then coalescing. Once you kind of [00:46:00] set that Moore path, like you hopped in the car and it’s like we gotta find a way to get to the Rocky Mountains or something like that, right?

Then you guys will all roadmap that out, but you gotta have that ultimate destination.

Maureen: Interesting, ’cause I am thinking of data scientists I know and what could we automate that would produce the best outcome for society, assuming that leaders have a bigger leverage point than others.

Neil: I’ll give you a real quick example.

So I help out a nonprofit called the Zero Abuse Project where they unfortunately are in the realm of child sexual abuse, so they do a lot of work to help survivors, they have some programs. One of the things I’ve been trying to do is look for and say, “Who might be at-risk kids? Are there certain indicators, flags?”

And so we started developing an AI system to do that, and again, the data scientists we worked with, they know nothing about child sexual abuse, and thankfully didn’t have to. When we built the system, you know, there, there was this belief that there’s these [00:47:00] seven indicators, and as the AI was getting trained and learning these things, we didn’t teach it these seven indicators.

We just… We taught it other things. The AI actually came back and identified 12 indicators.

Maureen: Hmm.

Neil: And of those 12 indicators, three of the indicators that experts thought were indicators actually happened to be coincidental, and there was like one indicator that like it seemed so obvious, and they were looking at it like, “We totally missed that.”

Maureen: Hmm. ”

Neil: That actually makes total sense. I can’t believe we totally missed that one.” That’s the power of AI, and that’s the power of, well, you know, I don’t need to everyone to be an expert in everything. I just need to have people that know their space so we can-

Maureen: Mm-hmm … you

Neil: know, partner together to create that synergy.

Maureen: As we’re moving toward closing, one of the other questions I assume people are asking is how do I make sure my job’s safe? If an AI can be a court judge and read hospital films, what of my job will the AI do and how do I stay [00:48:00] relevant irrespective of what my role is?

Neil: So that’s a really important question to ask, Maureen, and the goal is not to automate people out of jobs.

I am sure everyone who hears that says, “Yeah, right.” The truth is, is that while machines or AI is good at some things better than people, there’s still a lot of things that people are better than AI. The creativity, the first-of-a-kind type of thinking, all these things come into play. So it’s not that the AI’s gonna come in and take over everything you do.

There are some jobs, like if you’re a taxi driver, a truck driver, yeah, right? We’re not gonna need as many. Doesn’t mean it goes to zero, we’re not gonna need as many. But AI is gonna take on more of the, I call it kind of the grunt, low-level cognition work. But that frees up our time to do more complex value add work.

So, you know, we talked about, like, the robot judges. Estonia actually is using robot judges now in traffic court. So speeding tickets, moving violations, 90% of those cases are exactly [00:49:00] the same, and so it’s perfect for AI. But those judges that were doing those cases now have been moved into more complex, like, traffic cases or have been moved into other areas like family law, where there’s a shortage of judges.

Maureen: So what does someone see when they go to court? They see a screen or do they see a physical robot, R2-D2 sitting in the judge chair with a gown?

Neil: Yeah, they see a physical robot.

Maureen: Okay.

Neil: Doesn’t look like R2-D2. It’s a little more human-like. But yeah, and I know it, ironically, it freaks people out for a different reason, ’cause now it’s like, well, the AI is gonna rule on facts.

I can’t make an emotional plea. So they’re like, okay, so you have a lot more people say, “I’m not fighting the ticket anymore,” so they’re like, “Okay, I’ll pay the fine or I’ll go to school.”

Maureen: Yeah, ’cause crying won’t work for the, for the robot.

Neil: Yeah. Right, the, the emotional pleas are not gonna be effective.

That’s the biggest concern now. Not that the robot is biased or anything like that, but it f- freed up judges to go elsewhere. It’s the same thing [00:50:00] where in, like, mental health, there’s a shortage of therapists, and so some of these tools are not meant to replace the therapist, but do some of the basic tasks the therapist would have to do, like the admin stuff, the transcription, the information collection, so the therapists actually have more time to see patients.

Maureen: There’s actually AI coaching tools where you can tell it your goals and it reaches out several times a day and prompts you to do something, which executive coaches aren’t gonna reach out to you several times a day and ask you if you’re, you know, doing your meditation or if you’ve eaten this afternoon.

Uh, so it does seem like there’s value, and again, even I have a bit of a twinge of, how do I make sure I’m relevant?

Neil: That’s the thing, is I think there’s more complex work, more value-added work we can all do, and I think we’ll have more freedom for creative thinking And if you look at past industrial revolutions, it’s the same exact thing, right?

People thought tractors would be the end of farmers, right? It just [00:51:00] allowed now one farmer can do the work of 10 farmers. I think that’s the real thing, and there’s gonna be whole new jobs that are, are getting created right now, right? They’re being incubated today that will exist in five, 10 years. So I think that’s the path we’re going.

Ironically, Maureen, the thing that worries me the most is not so much will we have these opportunities, is will we choose to take them? Because we’ve also never lived in a time with so much infotainment So are we gonna rise to the challenge and take on the more complex opportunities, or are we gonna go binge watch Stranger Things?

I don’t know.

Maureen: It is an interesting challenge, thinking of like I would issue a challenge to our listeners. How do you contribute to building something, and in what proportion? ‘Cause we do need downtime, but in what measure? ‘Cause the complexity is just exhausting.

Neil: It is, right? This, this are the things that [00:52:00] I worry about.

I mean, like I look at the kids today and, you know, I, I get everyone needs a break, but like summertime and like all the other parents like us are complaining everyone’s playing video games like all day. And you like check the screen time, you know, on the Nintendo Switch, and it’s like 60 hours this week.

Wow. And I’m like, that’s, that’s a lot. That’s more than the average adult even works. You know, if you even took one tenth of that, six hours, and did something else, build a skill, what would that mean, right? And that’s, that’s the challenge I really am more concerned about. The opportunities for us to do things, have work, we have meaning, are gonna be there.

It’s a question are, are we willing to grab them or not?

Maureen: Yeah, that to me links to also the mental health piece. ‘Cause I see people overwhelmed who will opt for entertainment rather than addressing some of the issues.

Neil: I know, and that’s unfortunate. I’m not saying everyone is like that, but people tend to choose the [00:53:00] path of least resistance.

Maureen: And some of these things are hard. You know, you talked about child abuse and sexual abuse. Those kids grow up and they, they face a uphill battle, and the challenges are interconnected.

Neil: Yeah. But it reminds me though what JFK said back in his speech about trying to go to the moon, and he’s like, “You know, we choose to do these things because they are hard.

You know, we wanna rise to that challenge.” And I still believe most people deep down want to do that. I, I’m an optimist, for sure. Just I know the challenge with infotainment today, I can see this going either way. That’s the thing that worries me.

Maureen: It’s interesting that that is more of a concern than all of the negative things we hear about AI.

Neil: Yeah, and I get that, right? AI is a tool. It’s all about how we as people wield it, right? We can use it for good, we can use it for evil, but it’s on us and our mindset, and that’s the problem, right? It’s that mindset. [00:54:00] If our mindset is, “I wanna be entertained,” or, “I don’t wanna use my brain,” well, guess what happens, right?

Maureen: Yeah. Less brains.

Neil: Yep.

Maureen: So Neil, as we wrap up, where would people find more about you and your work? Because it’s fascinating and so crucial for everyone, and especially leaders who are trying to make decisions about how they run their enterprises. AI can change the game.

Neil: Yeah. And, you know, and I’d love to hear from your audience if they have ideas or if some of these things we’ve talked about would be useful for their organization.

So my book is Own the AI Revolution. It was named best business book. It’s available Amazon, Barnes & Noble, all major retailers have it. If you’re looking to keep up with my work and some things that I am doing, I am posting often and sharing my website, which is just my name, neilsahota.com. Very active on LinkedIn, Twitter, Instagram.

Feel free to follow me and check out what’s going on. [00:55:00] And of course, a lot of the work also is through the UN, through the AI for Good initiative, which is, you know, you just Google United Nations AI for Good and you’ll see the portal.

Maureen: Thank you. What a fascinating conversation. And I love that you’re engaged with AI for Good, that AI has the potential to solve some really big problems that we’re facing right now and create better lives for a lot of people.

Neil: We have the tools, we have the means. We should take advantage of it. That’s why I want everyone to realize you are an innovator and you can be the driver, not the passenger.

Maureen: To our listeners, how do you drive? What do you drive? We, we do encourage our listeners, one, subscribe, like us, follow us, share us, share us.

Share this conversation with Neil, with anyone you think will benefit from it. Connect with him on LinkedIn, read his book, and most of all, in your sphere, lead [00:56:00] effectively. Do what you can do to make your world better, whatever that role is. Join us again soon. Thank you.