Host Tullio Siragusa held an exceedingly interesting conversation with Miguel Navarro, Patented Inventor and Seasoned Digital Leader, as well as SVP, Business Technology Executive - Conversational AI Enterprise Delivery at KeyBank, about empowering human potential in generative AI advancements and shaping the future through meaningful contributions and human-machine collaboration.
Exciting advancements in Generative AI have opened new avenues for creativity and innovation. With the human factor at the helm, the possibilities are boundless. Miguel Navarro, a seasoned digital leader, and patented inventor, has been at the forefront of harnessing this potential as the SVP of Business Technology at KeyBank. His vision and expertise have propelled the integration of Generative AI into KeyBank's operations, revolutionizing customer experiences and streamlining processes. Navarro's leadership exemplifies the transformative power of combining human ingenuity with cutting-edge technology.
Tullio Siragusa (00:11):
Good morning, afternoon, and evening, depending on where you're watching. This is Tullio Siragusa with Tech Leaders Unplugged. Welcome back. Let's get unplugged. Today I'm getting unplugged with my guest, Miguel Navarro, who's an inventor and digital leader, and SVP of Business Technology at KeyBank. Welcome to the show, Miguel. It's good to have you.
Miguel Navarro (00:31):
Oh, thanks for having me.
Tullio Siragusa (00:33):
We're talking about a really good topic today, something that we see in the LinkedIn stream pretty much on a daily basis. It's about generative ai. That's the topic of conversation today, but we're going to focus on harnessing the human factor, right? There's a lot of questions about how AI impacts work, and what interaction or role would humans have? Can it generate creativity? Can it help with other areas? How do we marry these two things in a way that they cooperate and work well together? That's kind of the, the theme of we're going to talk about today, and I'm looking forward to it. Miguel, let's just kind of go right into it. I read, oh, let's go. I read an interesting thing about how AI can be used for inspiration and brainstorming, right? Many people believe, well, you know, all the creative juices and ideas, that's the human that's the human element of things AI can't really help with.
Tullio Siragusa (01:31):
That's a machine. But I read something really interested, and it made me rethink that idea, and, and this, this being that because of the vast amount of information and, and content that is readily available with ai, especially with platforms like Chat, GPT this becomes a great resource. You know, if you are a writer, for example, and you're stuck and you're trying to figure out a plot for for either a book or a movie you could actually ask a generativity AI platinum to give you a bunch of ideas for a plot. Or maybe you're stuck with coming up with design. You can ask for a bunch of different color schemes and then potentially choose the one that you find most useful to riff off of that and work on that. And I found that really interesting because it basically means if you're a writer, you never have to be stuck again. Writer's block is gone. If you know how to use generative ai. What are your thoughts on that, Miguel?
Miguel Navarro (02:29):
So I really love this, right? Because I think part of the thing that people kind of fear about AI is that, oh my God, I'm going to get replaced. And the idea here is that, you know, it's not necessarily the humans that get replaced, right? It's the tasks that humans do that get replaced by ai, because it can automate it. So I also love the context behind removing the cognitive burden of going from zero to one, right? And I think part of the thing that you're saying is, oh, like if I were a writer, I would not have to be stuck anymore with my writer's block, because I can ask generative AI to then give me, Hey, so here's my plot, now here's what I've written, and here's where I think the char, the character Rx can go, tell me what you think, right? And then it'll give you options.
Miguel Navarro (03:23):
And then you can then pick out the option that you feel like is best, probably an option that you might not even be thinking about. And that's actually going into the world of known and unknown, right? If you're thinking about it you know, the bank or cash of knowledge that you are working with versus ai or generative AI at this point, right? It's just two completely different banks. So with that, if you think about all the different ideas or new ideas that may come up in front of you, that will spark new ideas, because that's part of the human element, right? It's supposed to inspire us. It's supposed to create something for us to be able to complete our task, because the, what AI does isn't necessarily the task completion. That's, you know, part of the chunk to complete a task. So, no, I, I really love that topic. I love that you brought that up. But yeah, no, my thoughts behind that is yes, it's all about removing the cognitive burden of going from zero to one, and also continuing that inspirational element to then allow us to move forward or where we need to be, whether it is something we're thinking about or not.
Tullio Siragusa (04:26):
Let's dig in a little more on this cognitive burn idea, removing the cognitive burden from zero to one. Can you expand on that idea a little bit?
Miguel Navarro (04:33):
Yeah, absolutely. So think about it from the sense of if you are a business person and you're thinking, oh I will, I, I need to create a deck for my executive, you know, and you are thinking, okay, this deck is supposed to talk about the story and think about the storytelling that you're going to be doing. So going from, oh, let's talk about the 2008 financial crisis, and then now where we are and where we've succeeded in the business. So if you think about it, okay, would you really take about like two to like four hours to go pick up items that you already know have happened within like the 2008 financial crisis, and then grab pictures, grab decks, spend about like two to four hours on that, and then get onto the story that you want? Or do you just ask generative ai, right?
Miguel Navarro (05:21):
Or chat GPT, or really at this point, some of the tools that's for decoration, like tome and be able to say, Hey, create me a deck about the 2008 financial crisis, and then hit enter, and then move on to your stuff. And then after that's done, then just really, oh, just go into edit mode. You know, because you're not, you are, you no longer carry the burden of starting something and then finishing it. You get the A 0.5, right? You get the 0.5 of that idea, and you only have to complete half of that because you're just editing at that point to get to the finished point of, you know, let's say talking about the slide that, or the storytelling arc that you're trying to do, and then add the data that you're trying to do. And then from there, you're able to have a complete story that you'd be able to tell your executive and probably more than, or less than half the time.
Tullio Siragusa (06:11):
Yeah, I love that. I, I have to agree from personal experience, I find it as a way to get more done by having access to the content and ideas, but I still have to put 'em together and make sense of them. But having sort of a partner at your fingertip that can help reduce the amount of effort that would've normally taken days, if not weeks into minutes, is fantastic. And that really talks to this idea of co-creation. I want to read another interesting thing. I, I found that generative AI can act as a tool for co-creation generating a broader radio of ideas based on minimal input. And that's the key thing too, is the, you can just have some basic input and it seems to spark a lot of ideas. And then this can help spark creativity in humans. And, and the initial concepts, like you said, going from zero to 0.5 and then into one that you can build upon in the context of business, where do you think some companies are succeeding at leveraging this, and where do you think some companies are kind of missing the boat and so to speak, by not leveraging it in a proper way?
Miguel Navarro (07:23):
Well, I think where other companies are hitting this, I think as part of the individual use cases, right? As you can imagine, there's definitely a lot of risk of bringing in generative AI into an organization. So a lot of them probably today are just not, you know, diving into it as much as they should. But if you look at it from the individual businesses, like a real estate person, right? And you know, again, trying to create a real estate listing, I already have a lot of friends that are in real estate that's using chat GPT, right? To be able to create them a listing, something that would, they would spend hours on to then break it down into minutes, you know, for certain folks that have small businesses and they are using you know, chat GPT at the moment, to be able to create a job description, let's say, for a person that they're looking for, instead of doing a little bit of research, all they're doing is typing in the input and refining it, right?
Miguel Navarro (08:21):
So and the keyword part of that right, is refinement, because the idea is, is that we also need to understand that whatever, and this is where the co-creation piece you were talking about comes in, is that we need to understand that whatever it is that AI is creating should, shouldn't really be considered a finished product, right? You would still need people to eyeball it to be able to say, Hey, is this correct? You know? And in today's world, those folks are called labelers. So I think where folks are missing the opportunity, right? Going into the question that you had, is I do, I do know that in the future there will be two kind of jobs, right, that will come out of ai. The first one will be labeler, being able to, you know, eyeball certain things. And those are essentially our SMEs today, right?
Miguel Navarro (09:14):
The SMEs in our business who are using ai, those will be the ones that will be using ai, come out with the output from ai, eyeball it, see if that's great or not, and be able to retrain you know, their existing model, right? To be able to come out with better answers or be able to say, oh, I'm just going to go edit this, and then it's all done. You know? So that's job number one. The second job that I feel like is going to come out are prompters. So I know right now you know, and if you think about kind of where we were on mobile phones back in the day, the game, right? The, game that we had on the mobile phone, it's a giant red button where you're tapping it as much as you can over a period of time.
Miguel Navarro (09:56):
And, that shows a high score of who tapped it the longest to, versus where we are today that we have console-level games on our phone, you know? So if you think about the utilization of AI and where we are today, we're literally on the red button-tapping stage at the moment. It's extremely light. We haven't necessarily gotten to the meat of it yet, so we haven't created mobile deposit yet, right? We haven't created p2p payments. We haven't done any of that within the AI space, if we're, you know, creating analogies and banking. So if you are thinking about, hey, like imprompt, right? Or being able to put in a command or a request, right? That you would then ask about the large language model or a, you know, private large language model that you have in your organization that is fine-tuned to the specific business that you have. There is definitely an art form to it for it to pop out the, out for it to pop out the output that you would want it to be, you know? So again, those two tasks again or two jobs that I'm thinking would be happening in the future are labelers and prompters. That's
Tullio Siragusa (11:04):
Interesting. I guess that also follows the historical trend that we saw coming out on the industrial revolution, right? Where we went from manual manufacturing to automation with machines, but you still needed people who monitored the production and gave inputs to the machine to keep everything running smoothly. I mean, obviously, we're talking about knowledge-based work here, but it sounds to me like what you're talking about is really the ability to augment skills. And small businesses have a massive need for this. They're usually staff constraints, budget constraints, so they're staff constrained. And I read, this is an interesting statement that I, I'm going to read that I think is worth continuing to talk about from a skills augmentation point of view. It says that AI can help to augment human skills, particularly where position, scale, or speed or concern. I mean, , that's clear in terms of you're going to get a lot more precise input from a general ai less risk of human error.
Tullio Siragusa (12:07):
The speed in terms of how you get information and how you put together information is, is incredible. And scaling that, you kind of talked on that, that's probably the challenge. You know, are they the right roles that could be applied into an organization where you can use that to scale? But it says, in the creative process, AI can handle repetitive or labor-intensive task, freeing up humans to focus on more creative aspects of their work. For example, an ai, an AI could handle the laborers task of animating individual frames in an animation, allowing the animator to focus on character design and story recording. Pretty interesting. Wonder if you're movie studio, how long that takes today? And by automating some of those functions, how many more movies you could put out possibly? What are your thoughts, on this idea of skill augmentation? I think this is where a lot of the fear lies too. Y you know, there are thoughts about skill replacement versus skill augmentation. What are your thoughts?
Miguel Navarro (13:04):
Well, I think you know, there's definitely going to be a disruption in plenty of different spaces, right? I do think that you know if you think about it from a, a skills perspective, right? How much of your skills could be automated and how much of that is a huge part of you that, you know, you feel that that huge part of you kind of becomes useless when, you know, kind of generative AI or AI kind of takes over. But the idea there, and that's kind of like the thing that I keep telling people as well as displacement, right? So, the idea here is that you know, people are thinking that, oh, if a person wouldn't have to do a certain task anymore, that they would, can now concentrate on, you know, X, y, and Z. Let's just, let's just call it that for now.
Miguel Navarro (13:53):
The idea too, and I think the part that some folks are missing is nothing's automatic, right? Like, so even today there's, you know, AI has been part of our world today coming from like, you know, just the, the smart keyboard that we have right? On our phone. And that becomes better and better every day, every time we use it, every time we create suggestions and as we train it. But the piece that, you know, I would tell people is I do believe, and part of that human element is we have to remember that part of training that isn't only automated, but also at the same time, you also need that human factor in there because designs and styles change, right? So if you're thinking about it from a reactive model, you know, if we are saying, Hey, bell bottoms are in, right?
Miguel Navarro (14:38):
And then we just kind of like keep going at for a very long period of time, that bellbottoms that are in what used to be the right model, then becomes the wrong model. So if you think about it from a technology perspective, operating systems, et cetera, cetera, the operating system is only really as good until it's not, right? Like, so that is kind of really where like the human factor really comes in, because we still need like litmus tests happening every single time with a model that is coming in. So even though it is yes, automated, I do think that yes, people would then be able to concentrate more on the x, y, and z tasks, but people will still need to tap on that. They're probably not going to tap on it as much, but that will still be part of the job.
Miguel Navarro (15:21):
Now, that job, that job may evolve right to the different thing that it will be in the future. And we don't necessarily know what that is yet, but we know that, you know, the faster kind of people remain cognizant on that task and the evolution and the adaptation that people need to do to be able to complete that task and live in the new world that it is. Yeah, you know, I think the, that's kind of where the elements are as to like how companies would then compete, right? Because I do believe that when it comes to generative ai, there's going to be tools, very, very similar tools that were coming out there, and there are little advantages that a certain tool will have versus the other. But at the end of the day, right, and say, this analogy I use commonly in my workplace as well, is that you can give me and Tiger Woods the same exact golf clubs, the same exact tools, maybe a little different, whatever it may be, but the output would be very different, right?
Miguel Navarro (16:15):
Because Tiger Woods will use those golf clubs in a much different way than I would just also because of the skill level that we have. And I think that is, you know, like when we're looking at tasks that people are doing, we need to concentrate, and the human factor still needs to come into, yes, you're being able to concentrate more on the X, y, and Z stuff, because you don't have to worry on that task, but you still need to make sure that that automation, the tasks that you're embedding into that automation is still fine-tuned in a way that you can perform like Tiger Woods.
Tullio Siragusa (16:47):
You know, I, I love this analogy you've made to Tiger Woods, because it really speaks to something that AI will never, ever replace. And from an execution point of view, it's kind of like you could come up with a great business plan and a great idea, but unless you've had an experience doing that kind of work or putting that together in the past, it, it's all going to come down to execution. And that's going to come from experience people that actually know how and have done it before. And it's fascinating, but I think there's also an opportunity, this is another thing I found where AI can help explore new creativities. And I, this was shocking actually as I found this, because I'm, you know, as a design thinker, I'm very much focused on this idea that everything is tied to human ingenuity, especially tied to empathy, which a machine can't really learn.
Tullio Siragusa (17:41):
Probably, maybe, maybe it will in the future, but not today, <laugh>. But, anyway, this is what I, found, and, and I think it's worth discussing. AI can help humans to explore new forms of creativity, for example, through machine learning. AI can generate content in ways that humans might not have considered breaking traditional rules and conventions. This can inspire humans to think outside the box and push the boundaries of their own creativity. And this is so true because we are all born into whatever biases through our upbringing or limitations and beliefs through our upbringing. And those limits limitations and fears can limit creativity and also create boundaries around what can be done. But machine doesn't have any emotions or feelings or bias, right? So at least hopefully it's not been programmed that way. So this idea that it can open up new ways of seeing things you think, how far do you think we are from that being a reality? Or is that ha available today?
Miguel Navarro (18:44):
Well, and I honestly think that both if anything, the Alpha Omega have got this happening, right? And I'll start off with the not knowing, right? The burden is sometimes the burden of knowing, right? Makes us stop doing things as well, right? So I don't know if you've seen and you know, again, like I love American football, so it's just like, you know, a huge Bells fan. So one of, like, the things that I love are those aw w s commercials, right? Because they like to tell you stat that, you know, that type of campaign that they have, and you'll start seeing like, oh, the probability of someone catching something, right? Catching the ball at, you know, you know, like scoring that touchdown was at 8%, right? So if you think about it, right? And going from like the negative aspect of it, of not knowing, if someone told you that, oh, your success rate for this is 8%, you know chances are you're not even going to try it, right?
Miguel Navarro (19:44):
Because it's almost like, what? Like, why am I going to do that? 92% of the 92% chance of failure is what I see here, you know? But that is what differentiates a winning team from a losing team, right? It's that one touchdown, that one field goal that we make. And it's the same thing in business. You know, we need to be able to accept and measure risk, and sometimes not knowing things allow us to be able to do that, you know? So now going into, you know, oh, knowing the numbers and getting to become a little bit more accurate, I definitely think that we're still going to hit both, right? That there's still going to be an analysis paralysis. But then also at the same time, like people who are, and again, going back into the whole Tiger Woods analogy, there will be people that will be gutsy, right?
Miguel Navarro (20:31):
Though this is a very unpopular opinion or an unpopular you know, it shows a failure rate, et cetera, that they will still override and they will do. You know? So those are kind of like the balancing acts that I feel like we're going to need to do that we, we as humans, haven't necessarily evolved yet to that level. And again, the evolution, right? Happens both ways. So we're going to have to evolve with ai, and AI will have to evolve with us. And through that, I think that's really when you meet the massive potential, right? Of like where things would be going. So if you're looking at it from a business perspective of let's say let's, let's call it, you know assuming you know, a success rate or assuming risk on something, I do think that that human element is still very important to be able to say and say, Hey, here are some like, and utilize AI in a way of like, Hey, here are some of the perspectives that we're looking at.
Miguel Navarro (21:27):
And be able to ingest that, create a very, very informed decision, and still make that decision just the same as, you know where we are today. That, you know, sometimes we know that a lot of information just kind of like paralyzes a team, you know, or a group of folks within an organization. And sometimes it really is about like the doers just being able to do their own thing. I think there's still going to be a balancing act when it comes to that. It's just, you know, a different type of balancing act, especially when there's a, a lot more information coming in.
Tullio Siragusa (22:00):
It remains to be seen how it plays out. I mean, I recently had a conversation with the head of a VC just trying to get a pulse on where things are moving. And she made an interesting statement. She goes, in about five years, the biggest business opportunity is actually going to be helping people to think, helping people to think, rationalize, and know how to be in a relationship with each other. <Laugh>. I was like, that's interesting because there's a big movement to be data-driven. But to your point, sometimes the data all lines up one way or the other. And there's this thing called human intuition. That gut feeling, the experience, the wisdom that comes with time that makes us make a different decision. And you have massive breakthroughs, not always, but sometimes. So it'd be that's right to see how it all plays out.
Tullio Siragusa (22:50):
It sounds to me that we need to have a balance between being data-driven and informed by leveraging these tools that give us more insights faster and still tapping into good old fashioned human intuition to make sense of it in a way that's practical. That also takes, into consideration the experience and feelings that making a decision might have on other people, especially clients. But it's an interesting thing that we'll have to keep an eye on. But for now, it sounds as though if you're stuck, if you want to move faster, you want to be more efficient. If you want to replicate yourself and you need more help, well, generative AI is probably your best helper. Would you agree with that? Miguel?
Miguel Navarro (23:35):
Oh, absolutely. I couldn't agree with that more. And you know, again, if you're thinking about use cases as well, it doesn't necessarily have to be pen to paper either. It could just be something social. One of the things I keep telling folks is be you know, utilize generative ai. I actually created a Apple Watch app on generative AI where I'm able to just ask a question, you know, and be able to say, Hey you know, I need to know three things about this person so I can start a conversation with them. So if you're looking at it from a sales perspective, you know, and you meet someone at a conference, I know we do a lot of virtual stuff now, but you know and a lot of the physical conferences are coming back. But when we, you know, when you're there, think about it to the point of being able to talk to your watch, having some like Jarvis, right, or a personal assistant with you at all times and be able to say, Hey, can you tell me a little bit more about that c e o and what their potential three hurdles are just company-wide?
Miguel Navarro (24:35):
Probably, you know, just from like public things that's out there or just maybe something that this person posted on their LinkedIn, you know, and then in that way, you can start that conversation and then now your experience with them, instead of it being like a cold call or a cold meat, right? Like you'd be able to then make sense of their world before you handshake and when you handshake, that handshake is, you know, has become a little bit more meaningful.
Tullio Siragusa (24:58):
Absolutely. We were up on time, but it sounds to me like LinkedIn has a huge opportunity. Do you know how they have the ability to show you your contact information on your calendar? Well, maybe they need to proactively send out some bits and information about them, you know, use their, some of their own engines through Microsoft to enable Oh, I'm right, absolutely. At LinkedIn, if you're watching, you got a product enhancement idea right there, <laugh>, unless you're planning it already, that would probably be the most useful thing, especially if you're in sales and marketing. One of the most useful things is to be able to look at my phone and say, oh, I have a meeting with so-and-so, and look at all, the insights I've just Yeah,
Miguel Navarro (25:37):
Absolutely. Yeah. And be able to say like, you know, listen you know, this person has created like three posts about generative ai. They're probably interested on generative ai. Exactly,
Tullio Siragusa (25:48):
Tullio Siragusa (25:49):
Now that would be a great tool. So LinkedIn, if you're watching Microsoft that you're watching, please bring that into LinkedIn because we could all use that. Miguel, it's, it's been great to have you with us. Thanks for sharing your insights. Really appreciate it. Just stay with me as we go off there in just a second. That's a wrap for today. We have a new guest coming up. We have a bunch of guests coming up. We got next week we have Diana Deca, who's the CEO at Neurobotx And we're going to have an interesting conversation about becoming a meta pilot, bringing flying cars to life through VR gaming. That should be an interesting conversation, you know how things are if you can imagine it eventually to become real. So the metaverse allows us to imagine and then potentially, who knows, we're already working on flying cars, but come join us next on that show. But till then, have a great weekend and take care, everybody.
Miguel Navarro (26:42):
Patented Inventor and Digital Leader, SVP, Business Technology @ KeyBank