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294: Neil Sahota – Artificial Intelligence for Good

Neil Sahota shares how A.I. can impact our lives for the better

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Neil Sahota Show Notes Page

After the IBM Watson team won the Jeopardy challenge, Neil Sahota was fighting for the ecosystem model to try to open up the platform. A lot of the people was trying to engage the tech people, but Neil was trying to target the businesspeople. According to Neil, the businesspeople understood the problems more, they were on the ground, and if they were trying to build solutions, they were the people that need to be at the table with the technologists. Neil initially lost that fight, but he never gave up.

Neil convinced them that if they had five of the best technologists in the world then they would be thinking about self-driving cars and missions to Mars, but if they had five of the best doctors in the world then they would be thinking about curing cancer. What if they were to put them together?

During that epiphany moment, people realized the amazing impact that technology can bring across every sector if they were to join together business and technology. This amalgam between business and technology became one of Neil’s biggest humps that he was able to overcome.

Neil Sahota was born in New York and grew up there until his father took a job in California. Then, this 7-year-old boy moved to Southern California. After an initial culture shock, Neil learned to adopt the best of both New York and California cultures.

Neil was the black sheep of the family. However, being so different gave Neil the early recognition that people have different perspectives, thought processes, and motivations. This would fuel Neil’s future career as the person who find and connect those “hidden dots” and build collaborative coalitions, ecosystems if you will, to maximize the value from these hidden dots.

Originally, Neil had a strong career path into politics through helping people get elected to local, state, and national offices since he was in junior high school. However, by his second year of college, he realized he wasn’t “evil” enough to be a politician. So, he pivoted into management consulting and helped Global Fortune 500 companies with their business strategy.

Never content to think “inside the box,” Neil solved problems at an industry/sector level. Through this work, he developed a lot of intellectual capital. Becoming a recognized thought leader, he was sought after to join the IBM Watson team. Here, Neil helped pioneer the current artificial intelligence (AI) wave that we are currently in. Today, enterprises and government agencies like the United Nations actively seek his advice, and he sits on the Board of several companies.

Neil is an IBM Master Inventor, United Nations Artificial Intelligence Advisor, Faculty at UC Irvine, and author of Own the A.I. Revolution. With 20+ years of business experience, he works with organizations to create next generation products/solutions powered by emerging technology.

Beyond his career, Neil’s mission in life is to leave the world at least as good as, if not better, than he found. That is why he volunteers his time to non-profits and champions social good. Currently, he is helping the Zero Abuse Project prevent child sexual abuse as well as Planet Home to engage youth culture in sustainability initiatives.

Neil currently “lives” in Southern California, but the whole world is his office.

Tweetable Quotes and Mentions

Listen to @neil_sahota get over the hump on the @FastLeaderShowClick to Tweet

“Artificial intelligence is an evolution, and it is a tool for us to use. It’s not here to kill us or replace us.”  – Click to Tweet

“A.I. is helping unlock our own humanity.” – Click to Tweet

“Diversity and inclusion is incredibly important. We all see things very differently and we all have different experiences and ideas, and we have to bring that to the table.” – Click to Tweet

“So much of A.I. is about how we actually train the machine.” – Click to Tweet

“Diversity inclusion is so critical to training the A.I. systems.” – Click to Tweet

“A.I. is not a magic bullet. It doesn’t just come out knowing things. You actually have to teach it.” – Click to Tweet

“A.I. is a machine, a system that can actually do a task that requires some level of cognition.” – Click to Tweet

“A lot of people are afraid of the A.I. revolution we’re currently in and they’re wondering what’s going to happen. You actually have control and power to shape how we use A.I.” – Click to Tweet

“Good judgment comes from experience and experience comes from bad judgment, so don’t be afraid to try and take risks.” – Click to Tweet

“You’re never going to go anywhere if you’re just hoping that all this stuff will go by you.” – Click to Tweet

“If you’re not trying to disrupt yourself, someone is going to disrupt you.” – Click to Tweet

“The pessimist looks at opportunities and finds problems. The optimist looks at problems and finds opportunities.” – Click to Tweet

“There’s going to be so many new jobs out in the future that we can’t even conceive of yet today, but you have an opportunity to get ready.” – Click to Tweet

“A.I. has given us as people a chance to be more human.” – Click to Tweet

Hump to Get Over

After the IBM Watson team won the Jeopardy challenge, Neil Sahota was fighting for the ecosystem model to try to open up the platform. A lot of the people was trying to engage the tech people, but Neil was trying to target the businesspeople. According to Neil, the businesspeople understood the problems more, they were on the ground, and if they were trying to build solutions, they were the people that need to be at the table with the technologists. Neil initially lost that fight, but he never gave up.

Neil convinced them that if they had five of the best technologists in the world then they would be thinking about self-driving cars and missions to Mars, but if they had five of the best doctors in the world then they would be thinking about curing cancer. What if they were to put them together?

During that epiphany moment, people realized the amazing impact that technology can bring across every sector if they were to join together business and technology. This amalgam between business and technology became one of Neil’s biggest humps that he was able to overcome.

Advice for others

Make the right bets.

Holding him back from being an even better leader

It’s the people problem. It’s tough for people to believe what machines can do and to create the buy-in.

Best Leadership Advice

Don’t give up, but know when to fold when focusing on winning the war, not the battle.

Secret to Success

I’m able to see the perspective of other people.

Best tools in business or life

Emotional Intelligence.

Recommended Reading

Own the A.I. Revolution: Unlock Your Artificial Intelligence Strategy to Disrupt Your Competition

Made to Stick: Why Some Ideas Survive and Others Die

Contacting Neil Sahota

Neil’s website: https://www.neilsahota.com/

Neil’s LinkedIn: https://www.linkedin.com/in/neil-sahota-%E8%90%A8%E5%86%A0%E5%86%9B-028143/

Neil’s Twitter: https://twitter.com/neil_sahota

Resources

 

Show Transcript

Click to access unedited transcript

Unedited Transcript

Jim Rembach (00:00):

Okay, fast leader Legion today. I’m excited because I have somebody on the show today. Who’s going to give us some really unique insights into AI and some historical background into all this that you may have not received before. Neil Sahota was born in New York and grew up there until his father took a job in California. Then the seven year old boy moved to Southern California after an initial culture shock, Neil learned to adopt the best of both New York and California cultures. He was the black sheep of the family. However, being so different gave Neil the early recognition that people have, different perspectives, thought processes and motivations. This would be the fuel of Neil’s future career as the person who would find a connect, those hidden dots and build collaborative coalitions ecosystems. And if you will, to maximize the value of those hidden data dots. Originally, Neil had a strong career path into politics through helping people get elected to local state and national offices.

Jim Rembach (01:03):

Since he was a junior in high school. However, by his second year of college, he realized he wasn’t evil enough to be a politician. So he pivoted into management consulting and helped global for a fortune 500 companies with their business strategy, never content to think inside the box, Neil solved problems at an industry and sector level through his work, he developed a lot of intellectual capital becoming a recognized thought leader. He was sought after to join IBM Watson’s team here. Neil helped pioneer the current artificial intelligence wave that we are currently in today. Today enterprises and government agencies like the United nations actively seek his advice and he sits on the board of several companies. Neil is an IBM master inventor, United nations, artificial intelligence advisor, and faculty at UC Irvine and author of own the AI revolution. With 20 plus years of business experience, he works with organizations to create next generation products, solutions powered by emerging technology beyond his career. Neil’s mission in life is to leave the world at least as good as if not better than he found it. That is why he volunteers his time to nonprofits and champions social good. Currently he is helping the zero abuse project to prevent child sexual abuse as well as a planet home, which is engages a youth culture in sustainability initiatives. Neil currently lives in Southern California, but the whole world is his office. Neil Sahota. Are you ready to help us?

Neil Sahota (02:41):

I am Jim lead for the conversation.

Jim Rembach (02:44):

No same here. Now I’ve given my Legion a little bit about you, but can you share what your current passion is so that we can get to know you even better?

Neil Sahota (02:51):

My current passion is AI for good. I’m very big about social impact and I look nothing wrong with making, but we’re always taught well, either make money or you help people, but we can do both. So I’m really trying to help champion the mindset of social enterprise and social entrepreneurship with AI.

Jim Rembach (03:09):

Well, and as you’re saying that and that because of the industry that I’m in with customer care and contact centers and the whole global nature of that particular industry and AI coming into the world at an increasing rate, some of the AI being forced into play, uh, right now going through this, you know, global pandemic that maybe shouldn’t be, we’ll get, we don’t, we need to get into that. Um, but there is a lot of whole lot of fear associated with AI. And you actually really don’t even introduce artificial intelligence as a concept and define it in the book until chapter six. Why did you wait so long?

Neil Sahota (03:49):

You know, Jim, it was actually because of all the fear concerns. Well, the reason I wrote the book was the stuff out. There was either way too technical for, you know, for business books or it was a lot of fear-mongering right? They dropped a whole host of issues at people’s doorsteps with no hope, no outlet, nothing to be able to do. And so I wanted to tee up the conversation, get people into the right mindset, understand that, look, this whole thing is an evolution, and this is a tool for us to use. It’s not here to kill us off or to replace us, but we, as the people actually hold the power,

Jim Rembach (04:26):

But you do talk about something that is evolutionary in nature, talking about society 5.0, what is that?

Neil Sahota (04:33):

Well, it’s actually started with [inaudible] in Japan, but this whole idea that our societies have woven. So we’ve gone through different revolutions, the formation of civilization, but you think about what they actually bring to the table. It’s going to change the way we do financial services, the way we do healthcare, we do education, you know, even the way we pick up trash that where are we now focusing our time and the things that we actually do and where we add value is going to transform and want everyone talks about like the automation side we’re finding as AI is actually helping to unlock our own humanity, right? That we’re actually taking more time to explore ourselves, be more creative, more imaginative. And that’s an interesting shift where more of the tedious work or will fall onto the machines, more of the creative work before the us.

Jim Rembach (05:26):

Well, but even those a as you’re saying that, and you and I had a discussion off Mike that I think we need to bring on board. Cause then you’re in, you’re in the introduction of your book, Steven Baraki talks about a separation or a split in humanity is what he refers to it as. And you and I talked about this a little bit and I think it’s important. And you had mentioned something about some work that you’re doing called or referenced, you know, to being the equality gap. What is the equality gap and tell us about this, you know, humanity possibility splitting.

Neil Sahota (06:01):

Well, I think we have a big challenge on a global scale that we’re seeing the people who have things and the people that don’t have things is, is growing that divide. And unfortunately technology seems to be an accelerator for it. And so when it comes to AI and there’s a huge concern that it’ll widen that gap and you’ll have a bunch of people that they can afford, the high speed internet, they can afford the tools and the iPads and stuff like that, where they can actually learn and use the technology and get great jobs, wherever the love and group of people that don’t have that infrastructure or access to equipment that they won’t actually learn these things, or be able to even use it. We’re already seeing now that, you know, there’s experimental surgery where they can actually restore vision with digital cameras that can transmit it into the brain and things like where if you lose a hand, they can replace it with a robotic arm where the eye can read your muscle and tendon motions. That’s great restoring mobility, but is that gonna be available for everybody will only be the people that can afford to get it. So it’s not just a question of jobs and wealth, but even things like access to healthcare and even maybe food.

Jim Rembach (07:18):

So as I start thinking about that, and you start talking about the whole political aspects of, you know, Hey, I w let’s advance the economy of tomorrow, for example. Uh, but then we all have all of these social issues because of this gap and this separation. So, I mean, to me, it’s like a catch 22 scenario. So how are you seeing a way forward and all this

Neil Sahota (07:45):

It’s it’s, it’s not, not easy. We have to try and ensure that, um, the infrastructure is there for everybody. The United nations is actually going to launch in the general assembly meeting, coming up in September, an initiative called connectivity, 2030. And the goal with that is to try and get everyone, especially the underserved populations, the world access to high speed, internet, stable, high speed, internet devices, simple devices that begin at least use the technology. Cause we’ve seen it even with kids, kids as young as two, they grew up in a household with like a tablet. They get really good at it. They learn things. They may not fully understand how to open apps, do things, play videos. I think the leg up that child’s going to have as they continue their education and develop their career versus the child that doesn’t have it. I mean, not see a tablet onto their tab.

Jim Rembach (08:42):

Well, as I started thinking about that, I also start looking at the, you know, you talk about the generational aspects. You talk about the improving of technologies is that there are different and qualities of technologies that exist today, especially when you start talking about AI and you talk a little bit about the democratization of AI, but we’re, we’re a long way off from that. So how long do you think we’re going to have to struggle with a lot of bad AI out there before it gets really good in a general sense?

Neil Sahota (09:13):

Um, I hope not that long, but I think it’s gonna take, it’s gonna take five to seven years to kind of work through the system that way. Unfortunately, you know, there is a lot of hype around AI. You have a lot of people that are riding the coattails and not really using it. Um, you know, trying to raise money, so products, but there’s actually a lot of good work out there. But a lot of that, the real AI stuff is like underneath the covers, right? It’s suppose people don’t see it directly. And so, because they don’t experience it, they don’t know it, but I think that’s changing. I, you know, my, my experience with lot of these AI ecosystems, right, the really innovative and even the most disruptive ideas are actually coming from the startups. It’s not, it’s not so much to be companies it’s this, the startups are coming in and say like, Hey, I have this whole new way of doing this as more and more of these things come to the forefront or cover, you know, in the consumer space. I think that’s when you’ll start seeing this, the shift happened and all the bad stuff and all the hype stuff get pushed out.

Jim Rembach (10:18):

Even as you’re saying that, I start thinking about some of the things that I’ve read in regards to the people who are out there and, and, and having the startups and doing the program and programming and the coding and creating of all these different products. And there’s a very clear separation between, for example, guys, you know, it is very gender split. You know, guys do certain things and they want to create certain things. Uh, and, and the ladies do something that is different and more women are creating technologies that do help humanity, they aligned with what you were talking about and what’s important AI for good. Uh, so do you see that type of separation when you start looking at all these startups and companies that are bringing some of these, you know, societal impacting solutions to the forefront?

Neil Sahota (11:11):

I, I do. I mean, we’re all kind of shaped by our experience and our interests, but you’re absolutely right. I mean, women tend to be way more nurturing. You know, we saw this explosion of like AI chatbots and a lot of it was for like customer service and these other things, but some of the more human element ones are the ones that were like targeted things. And that people weren’t thinking about rushing to vote by women, like there’s one called rainbow. And it was actually started as an outlet for, uh, abused women. So women that were suffering under domestic violence and it was like, it had no outlet and no one to talk to that they could at least get some information, feel like they’re being heard and hopefully encourage them to seek help. You know, I hate to say it if, if you’re in a room with five well adjusted man smart technologists, they would probably never think of that. Right. It’s just not something that would naturally come to us.

Jim Rembach (12:07):

So if we are looking at also the leadership that is currently transforming in, um, you know, the entire global scene, even before kids couldn’t go on campus anymore, even looked at the graduation rates, you know, being more women than males. And you look at all this societal shift occurring in, you know, more mature countries. And, uh, do you see that heading in impact and how we’re going to be leveraging some of these tools? And for example, this is going back to the whole democratization piece. I mean, do you see that making an impact?

Neil Sahota (12:39):

I think it’s going to make a tremendous impact. Um, there’s a lot just that, you know, we’re getting the, the women’s perspective and some of the more human elements of diversity and inclusion is actually incredibly important, right? And we all see things very differently and we all have different experiences, different ideas. And we have to bring that to the table, you know, with right now the black lives matter. And the, some of the, you know, the peaceful protests around racial equality and racial justice, a lot of people actually been asking me like, can we use AI to do anything about this? And it’s like, well, here’s the interesting thing. And people have tried this in the past, right? Like not to pick on Google, but they created a hate speech detector, but it turned out to be racist, right? It’s so much of AI is about how we actually train the machine.

Neil Sahota (13:28):

If we don’t have those diverse and inclusive perspectives at the table, it’s really hard to do. The UN is really gung ho about having AI robot judges, you know, improve access to justice, reduce corruption, very, very great, very much needed. You have a wealth of data in the U S court systems, but we teach the AI bad thing with the AI learn the implicit bias of the court system answers. Yes. Right. If you just look at some of these cases where you got to two people, they’re the same age, same type of occupation, or maybe they’re both students, it’s going to see all these things. Let’s say like, this guy got six months of jail and this guy got 10 years. There’s something different about them. Unfortunately, one of the most outstanding things is ethnicity. And so all really want to be doing is teaching the AI those bad habits, unless we can find a way to strip out implicit bias.

Jim Rembach (14:25):

Well, you know, it’s kind of funny that you say that, um, for me, this is one of the things that I always struggle with when people talk about and asking the question, are you racist? And from my, you know, thinking about what you just talked about, implicit bias, I’m like, well, we all are, everyone is,

Neil Sahota (14:45):

Yeah.

Jim Rembach (14:46):

Just the way we word it, doesn’t matter your color. It doesn’t. I mean, if they’re relevant, I mean, we, because we’re creatures that are focused in at the very basic level on survival, anybody who’s different from us. We now have a biased against, because we don’t know if they’re going to kill us or if they’re going to help us.

Neil Sahota (15:08):

I think about it, right. It’s not like it’s all bad, but if you just say, Hey, I was talking to an engineer, everyone has their picture with engineers, like in their mind. Right, right. Maybe, maybe they’re good at mapping. There were glasses, but it’s just so intrinsic to us. How in the world we find that out to teach the machine. Right.

Jim Rembach (15:29):

I think that’s, I think that’s going to be one of the most talking about the democratization component. And I also want you to start talking about the customer experience component. If we start allowing the AI to handle more of the interaction from the point of education to sell, or, you know, the different transactional processes, I think we’re going to run a huge risk like that. If we don’t keep the bots in check

Neil Sahota (15:53):

Very true. But that’s is why diversity inclusion is so critical to training the AI systems, right? This isn’t just, Hey, is this a nice, nicely, wherever we need that broad set of perspectives, help us understand this could be interpreted this way, or this appears to be happy this way, or this seems very stereotypical, right? I alone, I’m not going to get it. And I went to my buddies may not get it either. If you bring the right collection of people together, it’s actually amazing where you can actually pull off.

Jim Rembach (16:24):

I think that’s critically important to note because it goes back to, I did some research on artificial intelligence and automated systems from a knowledge management perspective and in customer care operations, as well as from the, you know, the chat bot and servicing component and found three very distinctive generations of technology. And when you start looking at how, the ability for it, for that technology to respond to the customer with a correct answer, it varies widely. I mean, it’s quite significant. And I don’t think that’s what people understand. They think, well, you know, they should all have the same level of quality of response and to be able to handle the iteration and understand what I’m saying and reply back. And that’s just not the case.

Neil Sahota (17:09):

Like it it’s, it’s, it’s a challenge, right? It’s not an insurmountable challenge. Whether they think that’s one of the key things, people will, they probably build an AI solution. They always run to two challenges. One is around the data. Do I have the data? I have enough of the data, but is the data good? Is the data is the data biased, right? The second is actually the training, right? AI is not a magic bullet. It doesn’t just come out knowing things. You actually have to teach it. And yeah, you tap into the day to do that, but it still needs those human coaches to shepherd along. And Nope, think of it this way. And AI like a three year old kid, when it starts doing something to you, right. You give it, we call the ground truth, some rules on how to make decisions and you let it try stuff here. So data, here’s a question. What do you think the answer is? Right. Great. That’s right there a question. Okay. That’s wrong. It should have been. And so you go through this iterative process. So it goes from three year old to, you know, PhD in a few weeks, but that’s all contingent on the rehab and we have the data. Is the data good? Is it reduced bias and how good our teachers are?

Jim Rembach (18:18):

Uh, I bought teachers are there, they are, um, very critical in this entire process. So I had talked about in the book, you didn’t even define artificial intelligence until chapter six. Well, we’ve been talking for awhile. So What is artificial intelligence?

Neil Sahota (18:37):

Very simply AI is a machine, a system that can actually do a task that requires some level of cognition. I mean that, there’s not just an answer. You’re not looking at a search engine and not following like a path that the decision tree, you actually have to think about something and make the judgment call on it. Right. And the secret sauce with AI is that it can actually try and answer questions. We don’t know the answers to,

Jim Rembach (19:09):

Well, as you’re talking about that, I start thinking, going back to that whole thing that we were talking about in regards to fear and paranoia, and there are a whole lot of the unknowns around AI is what’s one of the wildest things you’ve heard about. People’s MIS misconceptions of AI,

Neil Sahota (19:27):

You know, aside from the terminology and stuff, I actually had someone once tell me that of AI is already conscious, and it’s not that we’re living in the matrix or a simulation. It’s like, we are actually all robots, AI robots that don’t realize it. You know, there’s AI overlord that has tricked us as AI robots that are thinking we’re a human

Jim Rembach (19:49):

We’re beyond matrix right. Way beyond matrix. Okay. So when I, when I look at, um, that splitting of humanity component, um, and, and I think about leveraging AI to that customer experience and it being more vital. Do, do you see a time by which, you know, we’ll have, um, more segmented type of customer gets served by different types of companies because of the sophistication of AI. Think about this. We’re not talking about, um, like for example, um, uh, product cosmetics, right? I mean, you have your cosmetic set out at the lower end and the top end. I mean, we’re not talking about that. Differentiate our product. I’m just, I’m talking about a particular product, has the servicing component around it. Maybe even product selection, component component around it. And now because of splitting of society, I’m now catering and marketing to these people in a different way. So in other words, my persona is more defined and broken down. Is AI going to help to define that better and a service better?

Neil Sahota (21:00):

Yeah. Yes it is. I mean, it’s already starting to help with the microsegmentation, but it’s actually believed that probably within the next 10 years, we’ll be able to target people at an individual level through psychographics and your own linguistics. So we always talk about the demographic information and we know their purchasing history. It’s one of the things, but with AI tools that are already out there, people are deconstructing your, your personality, your behavior patterns. You can figure out your interests, your hobbies, even your political party, right? And then you marry that with neuro linguistics, where you have AI, that is actually listening to what you’re saying, the words you’re using, even the tone of her voice and write a lot of context to be able to tell this person learns auditorily. This person learns like verbally, this person cares about the price and the fun factor. This person cares about the features and the value proposition. And here’s how you should actually talk to this person, focus on these parts of the product or this part of the service and use these words for maximum residence. I mean, it’s like an ultimate communication coach. So it’s like we’re raising the point almost now where we can tell exactly who the person is inside and outside and what their language is and how to speak in their language.

Jim Rembach (22:24):

That’s really powerful because talking about the whole thinking aspect and speaking ex aspect, um, that, that, that is a puzzle, uh, that has been difficult for anybody to really master. Um, and just when you think you have a right, somebody throws you a curve ball, but you know, you talk about, um, a lot of the AI going on detected. You had mentioned that before. Uh, and also there’s some parts of creating some of these AI solutions that has just inherent within decision making processes. And we talk about group think issues, and you had mentioned eight symptoms associated with group thing. And then, and this detrimental mentality, you talk about the illusions of vulnerability or in vulnerability, unquestioned belief in the groups, morality, rationalizing away warnings, stereotyping those who opposed the group self censorship of ideas, illusions of ananonimity among group members, direct pressure to conform and mine guards. How does AI actually help prevent these human pitfalls?

Neil Sahota (23:34):

Great, great question. Cause you know, this also ties in with some of the bias we were talking about early. We know that at least on some of these small scale levels, we can teach the AI to look for these bias, to be the crunch contrarian. In some cases, if you will, and be that, you know, lack of better word logical kind of sounding board so that, you know, Hey, let me do some negative testing. Let me kinda counter, let me see if I can find the flaw or some data that disproves your hypothesis type of thing, because we tend not to be good at that and even really good at what we do. We get used to working with the same people that we all start actually start thinking the same way, absolutely hard to do. If you always have like an AI project team member and they were trained this way, then that AI would always be looking for the group, think stuff, and actually try and help break that pattern. Cause it’s hard. It’s hard to try and do that.

Jim Rembach (24:31):

So then that leads me into the question. Just the, you know, the title of the book means sum it all up and it says own the AI revolution. So how can an organization actually do that own the AI revolution?

Neil Sahota (24:43):

Uh, if you, if you’ll indulge me for a moment, Jim, cause there’s a funny story behind the title. The original title was actually Uber yourself before you were Kodak. And my publisher hated the title. I had tested it out. People thought I was really catchy, but my whole thing was help people understand that this was a way for them to take a leap forward, right? That they don’t have to be a passenger on this journey. They can actually be the driver because it’s a tool. It’s an opportunity that we can all wheel. It’s a little back and forth my publisher because you know, they wanted to call it AI or die a little too negative for, you know, I was trying to be optimistic about this. You saw it on the eye revolution. Cause I think a lot of people are afraid of the AI revolution we’re currently in and they’re wondering what’s going to happen. It’s like you actually have the power to shape how we use AI. How’s revolution goes and I want people to feel empowered. I want them to feel enabled. I feel like, are they actually, they have an understanding of more control than they actually realize.

Jim Rembach (25:46):

Well, it goes back to your whole, you know, focus on the social. Good. Right. And I forget, I mean it fits and I understand that. I’m glad, uh, that definitely the glad the AI or died it wasn’t

Neil Sahota (26:00):

Yeah. Yeah.

Jim Rembach (26:02):

Fueling fire is one of the things that we do on the show is we focus on quotes to help us get pointed in the right direction. Is there a quote or two that you’d like that you can share?

Neil Sahota (26:12):

Um, I will share it to you that I think really resonated with me. I can’t see their mind, but the first is that good judgment comes from experience and experience comes from bad judgment. So don’t be afraid to try. Don’t be afraid to take risks. Right? I know it’s tough, but I’m not saying just jump out of a plane without a parachute, you know, take calculated risks, but you’re never going to go anywhere if you’re just hoping that all this stuff will go by you, nothing bad will, um, that’s not taking control of your destiny and you are definitely along for the ride. And I’ll be honest with you. People will be looking out for you. Right? I talked a lot of companies and that’s what I hate to say. They think if you’re not trying to disrupt yourself, someone is going to disrupt you.

Neil Sahota (27:01):

So, you know, take some risks. The other I know is from Winston Churchill, where I believe you said, you know, the, uh, pessimists looks at opportunities and finds problems. The optimist looks at problems and finds opportunities. And I think, again, this is this whole mindset I’m very big about is to get the right mindset and opens doors. So you went with a person that’s always just saying, like finding the flaws in, Oh, we can’t do that. We’ve never done that before. Or do you want to really be the person that says, you know what, you know, I’m, if I really am worried about losing my job or anything like that, right? Not that that’s the goal, but what are the opportunities then? Could you create something, do something, change a process, whatever it might be to turn that problem into an opportunity for yourself.

Jim Rembach (27:51):

Yeah. Well, and as they say, uh, when you look at the jobs of tomorrow, um, the jobs of tomorrow have not even been invented today. And I think AI is going to be a massive driver of a lot of that. That’s for sure. And that’s what they project

Neil Sahota (28:04):

Very, very true. Think about it 10 years ago, we didn’t have Uber or Lyft drivers, you know, people weren’t what’s around cryptocurrency like Bitcoin, right? Even social media, marketers was just a flinching field back then. So don’t lose hope. Right. You know, there’s going to be so many new jobs out in the future that we can’t even conceive of yet today, but you have an opportunity to get ready.

Jim Rembach (28:29):

Oh, that’s for certain now. But talking about getting ready, sometimes we have to, you know, have, you know, some of those learning opportunities in order to put us in a right spot. You talked about taking risks. You talked about sometimes they weren’t good ones. When we talk about learning opportunities and getting over the hump. So time there you have gotten over the hump that you can share.

Neil Sahota (28:50):

Uh, there’s a ton. Um, pro probably one of the biggest I’ll say is that, you know, after Watson and won the jeopardy challenge, and we’re talking about what we’re doing, I was fighting for kind of the ecosystem model, open up the platform. There was another big conversation about who to target, right? Who do we engage to, to get people on board? And you know, a lot of people said, we have to go with the tech people, right? They know the technology, you have all these tech startups. And I was one fighting for it. We actually have to engage the business people. Right. They understand the problems are on the ground. If we really want to build solutions, they’re the people that need to be at the table with the technologist. I initially lost that fight. Right. But I never gave up the fight and you know, we ran into some problems and finally, I kind of had an epiphany moment where I, you know, I made the thing like, look, we’re, we’re treading water at the moment, but think about it.

Neil Sahota (29:48):

If you, if you lock five really amazing technologies, like the best technologies of the world, the room together, what are they going to think about? Right. They’re going to think about the cool things that they want to do, like self driving cars and missions, Mars. Right now, imagine that you have, you put in five of the world’s smartest doctors in the room. What are they going to think about? Right. We’ll cure in cancer. Great. What if we put them together? And that’s when suddenly the light bulb kind of went on for people. Oh my God. He’s right, right. I don’t know how many technologists, you know, or amazing lawyers or accountants, teachers or whoever, but if you really want to have, make that impact across every sector, every industry it’s that amalgam between business and technology and those thankfully a big hump I was able to overcome.

Jim Rembach (30:42):

Well, I dare to say what you started talking about is really that inclusion piece. I mean kind of coming back the whole fertile full circle thing. And how do you eliminate the bias? How do you create better solutions? How do you impact the humanity? It’s no, first of all, being aware that we’re all, you know, riddled with bias of all kinds of, of different measure and, and, um, you know, impact without a doubt. And it’s being able to overcome that. So when I started looking at own the AI, not AI, then I start thinking about some of the things that you were talking about impacting humanity, the different types of technologies that are existing, the democratization of AI, uh, the impact of the customer, the segmentation, the micro segment. I mean, there’s a whole lot of things that I could be thinking about from a, you know, a targeting and a goal perspective. And so I have to ask you, is there one

Neil Sahota (31:40):

That you can actually share for AI for me? Um, I, I, at the end of the day, I think like, you know, when you were in my bio, my goal is to try and leave the world as good as if not better than I found it. And I think AI is actually create an interesting opportunity to do that. Not just in the tools that we can create to help fight climate change and try to end hunger fight poverty. But one of the interesting things that we’re actually finding out is he has given a chance giving us as people, a chance to be more human. You know, there’s a project going on in Nairobi, Kenya right now called loving AI. Probably not what everyone thinks. What they’re trying to do is teach it, you know, machines, unconditional love because the biggest illness in the world is actually loneliness.

Neil Sahota (32:35):

And at least before COVID, that was 40% of the, and they went down this path. But to teach the machine, you have to be able to say, okay, what’s this concept of unconditional love. Well, how do you define that? Right? How’s that different than regular love? And what are the different types of love? You have the love between a parent and child or between siblings or between spouses. And so rather than just become this, Hey, we’re going to teach this chat bot to be an outlet for only people became this really deep exploration to what it means to be human. And I think that’s my one real goal is with AI. It’s actually a chance for us to really explore the human experience.

Jim Rembach (33:22):

Fast leader, Legion wishes you the very best. Now, before we move on, let’s get a quick word from our sponsor. And even better place to work is an easy solution that gives you a continuous diagnostic on employee engagement, along with integrated activities that will improve employee engagement and leadership skills in everyone. Using this award winning solutions guaranteed to create motivated, productive, and loyal employees who have great work relationships with our colleagues and your customers to learn more about an even better place to work visit [inaudible] dot com forward slash better. Alright, here we go. Fast leader Legion. It’s time for the home. Okay, Neil, the Humpday hold on as a part of our show where you give us good insights fast. So I’m going to ask you several questions and your job is to give us robust yet rapid responses that are going to help us move onward and upward faster. Neil Sahota. Are you ready to go down? Oh man. I don’t know, but let’s do it. Alright. So what is holding you back from being an even better leader today?

Neil Sahota (34:17):

It’s, it’s really just a people problem that it’s tough for people to believe what machines can do and to create the buy in. Cause there’s just so much resistance to change. And what is the best leadership advice you’ve ever received? Uh, not to give up, but know when to fold when focus on winning the war, not the battle.

Jim Rembach (34:39):

And what is one of your secrets that you believe contributes to your success?

Neil Sahota (34:43):

I actually think it’s because I’m able to see the perspective of other people. I can put myself in the shoes and I know what’s going to click for them and what won’t. And I focus on that rather than just what I like or I think will work.

Jim Rembach (34:58):

What do you feel is one of your best tools that helps you lead in business?

Neil Sahota (35:03):

It’s really emotional intelligence. People have told me that I’m very empathetic, very personable, and they feel like, you know, it’s like a buddy conversation or having a, a beer rather than, okay. It’s like a professor lecture type of thing. And what would be

Jim Rembach (35:19):

Book you’d recommend to our Legion? It could be from any genre. Of course, we’re going to put a link to own the AI revolution on your show.

Neil Sahota (35:27):

I think other than my book, I recommend a book called made to stick fantastic book that helps explain how you actually create, buy in and resonance with people chip and Dan Heath are the authors

Jim Rembach (35:38):

And you can find links to that. And other bonus information from today’s show by going to fast leader.net/neil-sahota. Okay, Neil, this is my last Humpday head on question. Imagine you were given the opportunity to go back to the age of 25 and you’ve been given the knowledge and skills that you have now, and you know, you can take them back with you, but you can’t take it off. You can only take one. So what skill or piece of knowledge would you take back with you and why?

Neil Sahota (36:02):

Great question. Um, I’ll be totally forthright. I would probably take my knowledge of who won the sports games. Go back in time and make the right bats.

Jim Rembach (36:15):

I can relate to that. I take a few backup Powerball numbers, right?

Neil Sahota (36:20):

I figured with all that money, I can probably move things much faster. So

Jim Rembach (36:27):

I’m with you today. How can the fast leader Legion connect with you?

Neil Sahota (36:30):

Well, they can come to my website at neilsahota.com or they can follow me on LinkedIn or Twitter. I’m very frequently posting and no always looking to meet new people and hear new ideas.

Jim Rembach (36:41):

Neil Sahota thank you for sharing your knowledge and wisdom. The fast leader, Legion honors you, and thanks you for helping us get over the hump.

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