Daniel Faggella 4:44

Yeah, well, I mean, look, it’s in common parlance, in the business world today, and I think this is probably a good thing. Um, AI and machine learning are sort of conflated into a single term, and that’s okay. I think most of The time when people get really uppity about that, it is sort of an intentional snootiness that doesn’t actually contribute to any sort of meaningful business outcomes. Today, when you say AI, practically speaking, you’re referring to normally machine learning in some way shape or form ml is kind of the cutting edge, the cool stuff, it’s it’s not pre trained. If This Then That kind of systems. These are the systems that you know, everybody’s heard about and seeing, you know, tweets about and whatnot that can drink in data about cancerous and non cancerous tumors about fraudulent and non fraudulent payments and sort of make predictions and projections based on that data. So practically, ai means that the simple definition of this we actually have an article on Emerj called what is artificial intelligence, e-m-e-r-j, type that into Google, you can find it on the really rough shot definition is a computer doing something that normally we would require a human being to do. So if it’s naked decision, make a distinction that, you know, historically, we’ve required a human being to do but but now we have a computer doing it. Functionally, that’s that’s AI. And normally, for the sake of a business leader conversation, if you’re not the one writing the Python, two, good enough damn definition.

Carl J. Cox 6:05

I love that. I love that. And I appreciate you curating that simplicity around things because I have had that same conversation like no, no, that’s machine learning, versus AI. And if people get all caught up in their respective definitions,

Daniel Faggella 6:18

not a bad thing to understand what ml is conceptually and how it’s different from you know, 1980s ai. But to get hung up on it is often somewhat counterproductive. We need to understand conceptually how the tech works. And then we need to figure out how to turn into value. And we can let somebody else handle the linear algebra Generally, we don’t we don’t really service the crowd that, you know, writes Python code, We service the people that are marshalling budgets, and like that are there We service the people that run departments that have people writing Python code, and those people really shouldn’t? probably get too bogged down in what reinforcement learning means? It’s especially kind of like on week one of learning what AI is. Absolutely, absolutely.

Carl J. Cox 6:58

I’m kind of curious if I can extend this a little bit further. I’ve got Imagine you have to sell this from time to time to a board for a company, right? And we’re we’re a leading organization, and they’re like, why, why should we head in this direction? Like that? Like there’s this? I almost feel like this what we have to do it but they don’t really understand why they’re truly doing it.

Daniel Faggella 7:18

This is Yeah, this is really painful. So, um, look, a few things. Number one, we don’t cheerlead for AI. In fact, I’m probably better known for shooting down AI ideas than I am for raw raw fish boom, buying artificial intelligence as a concept. Anybody who’s read a single lick of our work or listened to a single podcast will know that we do more battering down of nonsense than we do of rah, rah, Sis, boom, bah, bah, bah. So when we’re brought in, if it’s a, you know, you know, workshop, what have you, it isn’t, hey, bucko, tell me why I should do AI is that that ain’t my job. Um, it’s, it’s, uh, here’s kind of what we’re deciding on the direction we’re going in. And my job will be, sometimes, hey, here’s a whole class of use cases and capabilities that are so wholly unrealistic based on where your data infrastructure is, and your internal teams are that we would be literally insane to actually pour money in that direction, no reason to do it. Um, here’s foundations, we need to build first, maybe here’s projects we need to get up to speed are levels of maturity of our data infrastructure that we need to work on, before we even do AI. So it is a bit of a misconception that whether people bring me in for a keynote at the United Nations, or whether it’s, you know, working with a, you know, a super regional bank or something like that, that it’s going to be everybody should do AI, it’s really sort of, what are your goals? Where are you trying to go? And what logically makes sense given given your capabilities? Um, so I guess that’s a level set for you is I’m not, I’m not a cheerleader for this technology, we would advocate that people invest intelligently. But but we would not advocate that people jump into it for its own sake, are you? I’m trying to figure out how to answer your question with that being the case because again, I don’t argue on the side of my client AI, right. I’m not a lawyer to pay high. But let me know how you want to reword it, knowing kind of what what we are here? Well, I’ll

Carl J. Cox 9:07

hear comments sometimes in strategy, right? So that’s what we’ve typically we’re helping organizations come up with a strategy and they’ll go, Well, we need an AI plan. Yeah. And, and so and I’m like, Well, why? What is your goal? What is your purpose around that? And it’s like, well, we need AI. Because if we don’t have AI in the future, we’re gonna die. It’s tough. So, and we had, you know, this question kind of leading into how do you build an AI strategy, but but how do you deal with that, once again, this, why somebody should really thoughtfully spend that it’s a significant investment to do AI, right. And so it actually provides real and relevant information. So how do you get a board a company, an organization, a government to go, this is where it makes sense. And this is where it doesn’t, you know, we You’re kind of hanging off the ledge because like, they’re already Hey, we got a million dollar check already spent it right now or more on this on this investment, right?

Daniel Faggella 10:08

Well, okay, so breakpoint, I’ll talk about what an ideal circumstance would look like here. And then I’ll talk about what reality looks like in this circumstance. So we have the good fortune of interviewing, you know, the heads of AI at the, you know, slacks and squares. And you know, the, the deca unicorn companies that are really data first digital, first kind of this stuff is already baked into their culture, but then also pulling in people from, you know, the axons and the Intel’s and the Raytheon’s. And whatever else, we’re, this is a bit of a sea change, or whenever they were founded, you know, in the case of, you know, an HSBC, you know, a quarter of a millennium ago, or something like that, right. Um, so, there’s, there’s sort of, there’s ideals, and there’s, there’s, there’s reality, when it comes to legacy enterprises, building AI strategies, the important concept here that’s going to string the two together is what we refer to as executive AI fluency. We, again have an article on this executive AI fluency. If you type that into Google, there is no other article that you’ll find other than ours simple infographic of what its components are the basic idea of the components. In order to bring AI to life at a decision making leadership level, again, getting the tech right is super important. We don’t necessarily work with the people who code but I think that’s tremendously important work. One thing I’ll tell you though, is you could pull them all from the best programs in Stanford, who’s ever been working on unrealistic projects with horrible measures of success that are never going to be a fit for the company in the first place, it just doesn’t matter if they’re all from Carnegie Mellon, it just doesn’t matter. And so we focus on that higher level, again, strategy. So in order to get any value out of AI, we need leadership that understands, conceptually, what does AI do so kind of algorithms and data kind of work together to make decisions just so we can sniff test is that something AI can even help with whether it be vision or language or whatever. And that’s something literally few hours of kind of grasping the relative concepts there, we have a ton of resources and curriculum that, um, it’s not rocket science to graphs, it conceptually, again, it’s very deep, technically, but as a leader, the conceptual grasp, not too hard. Second, Representative set of use cases, you don’t need to understand every AI use case, if you’re in banking, you don’t need to understand if you’re in drug development, literally everything that’s that’s going on in, you know, the most cutting edge e commerce companies, you don’t necessarily have to be you shouldn’t know the representative use cases in your industry and potentially in some adjacent sectors, just to have kind of a panoply of what realistically can this stuff do? What are the precedents where it’s actually added value, we need to take that conceptual understanding and grounded in real examples. And again, you might only need a dozen of those, or even eight of those to sort of say, Okay, I sort of see where it’s making its way into my industry. And then number three is to have some kind of alignment to how those capabilities, how that broad set of unlocking in this paradigm shift of turning data into value, how that broad set of capabilities can tie to our own strategic mandates, and tie to what we’re going to turn our company into so that we can win market share. So in an ideal universe, on the board, or the leading exactly all have some relatively decent semblance of AI, fluency executive fit. And they come to some shared understanding of the capabilities that they’re going to build on over the coming maybe half decade, decades. And the initial use cases that will help them build those capabilities, so that they can reach that North Star of this real data enabled market dominating company, right, this is this is what we could call an ideal circumstance. So this is like Plato’s utopia. You know, this is this is sort of what that would look like. And then there’s sort of like the world. So we’ll talk about what the actual world looks like the art and the actual world. Some maybe it’s an innovation leader, strategy, leader, functional business leader in compliance, or logistics, or whatever, will become pretty AI fluid and will be pretty lonely, because there’s not that many people around him subject matter experts or, or side to side peers in terms of other VPS or directors on who sort of gets it at the same level. And this person will kind of wriggle around enough to kind of get some budget and push forward on an AI project, often kind of to further their own career. nothing inherently wrong with that. But that’s kind of how they get it through the door. And they often sell this project, not based on Hey, boss, here’s a set of capability layers that we need to develop in order to turn into the kind of company we want to turn into. But hey, boss, here’s a plug and play tool, we can jack into the side of this business and get this cool ROI. Can I have some budget, so we treat it like it’s empty. And what happens when we treat it like it’s it is nobody learns. Because we’re not treating it as if this is a new kind of iteration. And really, AI is much more like r&d than it is like it everybody listening in should remember that. AI is much more like r&d than it is it we need to sometimes really do some tough wrangling of our own internal data. We need to take some pretty rough swings at if there’s any gosh darn value in this stuff in the first place to detect fraud to recommend products, whatever it is that we’re doing. And and we’ve got to do a lot of fighting to eventually turn that into a appointment. That’s not to say it’s not worth it, it just means we are building a new capability, we’re investing in a new kind of maturity, we’re not just plugging in a system. So normally we plug in a couple systems, we have a couple failed pilots. And, you know, the enthusiasm for AI goes down. At some point, leadership says we need to get a little bit of a better idea of this. And then at some point, they start to pay a little bit more attention as to what projects are getting picked. And people start having a little bit more of a conversation around the actual strategic and capability side of AI, instead of treating it like a tool. But what I will tell you right now is even in most legacy enterprises, and I think it’ll be the same case, you know, it’ll be this, there’ll be this way for another two to three years. At the highest level, actual fluency of an AI strategy is just not even remotely understood. So it’s, it’s real rough, I, you know, 80% of firms, even in North America, I would estimate are really void of fluency altogether. So there’s the ideal circumstance is the way it happens in the real world. And you know, that there’s certainly practical advice I’m happy to give but the state of affairs is not a great one for how well AI is aligned with strategy.

Carl J. Cox 16:07

And so let’s talk about the SMEs that all the small to medium sized enterprises. And there’s two different kinds you mentioned, there’s the unicorns, if you may, you know, I mean that that have a have a venture backed plan, right to go to a billion dollars within a short period of time, right, some SAS driven company, and they’re trying to disrupt the market. And then and then your There’s your great company that perhaps a regional driven player, but no real plans, if that makes sense to conquer the world. But how smart man, I know, there’s a tough question asked, yes, sir. I’m happy to answer. How small, you know, is too small for an organization to think about getting into a true AI strategy.

Daniel Faggella 16:52

Really good question. I’m glad that you didn’t frame it as if they all should, like, oh, How can two person companies adopt cutting edge deep learning? Like, like, ah, I will slap those questions down? Very, very harshly. Um, but yes, you’re asking the right question. And I think in fact, there’s a lot of SMB leaders, who are sort of haunted by this notion that everybody around them is doing AI at this high level. And they’re kind of like behind, when in fact, really, again, even legacy enterprises, they’re just running little popcorn project based on excited and enthusiastic leaders that are trying to advance their careers. They’re not even putting this stuff together in any coherent strategy. For the most part. And again, you have the squares, you have the slacks, you have the Salesforce, and you have other companies that will interview to get it all the way and aligns relatively well to the top and seem to have a coherent vision there. But here’s the general rule. If companies even one or two orders of magnitude larger than you, if you’re a normal, SMB, again, you’re not venture backed, you’re not, you know, entirely digital in data first, that’s not the that’s not what’s predicating your entire business model, right, that’s a very different ecosystem, then, you know, what you and I are talking about is a chain of shoe stores in the Midwest, right? Or, you know, a guy who owns a dozen gas stations or something like that, right? The idea is this, if the people one or two orders of magnitude larger than you, if you have really no evidence whatsoever, via kind of sniff test, and a little bit of secondary research, that there’s even the remotest semblance of traction of AI. In those folks that are one or two orders of magnitude higher than you, then probably, you should let your antenna stop zinging and being occupied with AI. And go ahead and focus on your bottom line and focus on growth. And focus on just having a good digital Foundation, get off of the yellow pads, you know, get off of Excel sheets, you know, level up your bi level up your tooling. You know, give yourself a better soil to eventually grow AI and leverage your data, you know, move, move yourself into a more digitally nimble position, and move yourself into a faster growing higher margin position, just smart business advice all together. And then by the time AI tools become accessible for smaller firms, because they will overtime Carl their they’re going to get more and more accessible for smaller terms over the coming, let’s say five years in marketing and sales and customer service, it’s going to trickle its way down, but it’s not there yet. Again, if you’re one or two orders of magnitude is crickets in terms of evidence of actual traction, real deal traction in AI, then you really should be focusing on those two things and not on adopting AI. If you are a leading organization, you work at a global or let’s say North American Top 10 company in a major industry, pharma, banking, etc. Now, you don’t really have a choice again, I’m not a rah, rah, Sis, boom, bah, you’re gonna invest in AI, wherever it is. No, I’m just saying if you’re at that level, the people hunting you down and trying to end You are so big, so powerful, and actually do have r&d budgets that you do need to start to align the strategic picture and begin investing in this capability. I’m pretty much off the cuff but it’s really only for that Echelon, that thoughtlessly. Carl, I can say you better damn well be doing something almost nobody else can. I thoughtlessly say that. But if your global or North American Top 10 and a major industry, I can basically say, I don’t even need to see your balance sheets, I don’t need to see much else. I just say, if you were to show it to me, I would be asking where we’re experimenting with this stuff. But But SMB? Hopefully that’s a good rule of thumb, Carl, let me know if that makes sense.

Carl J. Cox 20:16

No, I think that’s very logical what you said there, and I hope it actually gives some of the listeners out there that have a small to medium sized business to relax. That’s it. Yeah, he knows. Just relax. Yeah, just relax. Because it’s this is not impacting I think the one thing why strategy makes sense. for that? Well, let me clarify, I believe a strategy, of course, for everybody. But when the AI part of the strategy, you know, getting that component into it, is when one of these bigger players is attacking them directly. And then being aware of what their strengths are to protect their turf, versus what’s being attacked, because of new data is now getting information to help bring them to the to the larger base, but then they all the more important to focus on what their strengths is because they’re never going to have the r&d budget to match the big players, right? So they have to really realize where their true secret sauces and they’re never going to put the investment to match their AI compared to what you know, the Exxon Mobil of HSBC is, I loved it, by the way, the quarter of a millennium. I think the the only time I’ve ever heard that coined phrase. So Daniel, nice, nice work on saying that comment. But But you know, do you agree with that, to some extent is that I do I do? Yeah.

Daniel Faggella  21:31

In fact, will you have a book behind you, Carl, so I’m going to double down on Good to Great, Joe. Good to Great Collins has a great chapter on how great companies thought about technology adoption. And in essence, I mean, I won’t steal words from the man’s mouth. And, you know, his book deserves being read unto itself. But I’m more or less than paraphrasing. The idea is that the companies that really skyrocketed their relative market value over the course of whatever two decades span that Good to Great was was studying focus not on jumping on every technology trend when it became hot, but focused on really thinking about where their core competencies were that that in the coming decade, we’re going to pull them ahead of the competition, we’re going to genuinely separate them and win market share, win profitability, achieve their business goals, and they they thoroughly sunk in their heels in technology investments that would lower those core capabilities. And what that does, and I can tell you, this correlates directly with AI. What that does is it ties to a strategic mandate, we have an article called if people type in e-m-e-r-j Lead with Strategy into Google, there’s a whole article kind of about this idea of strategic anchors that we talked about AI projects that are going to see their way through the inevitable hurdles of bringing AI to life and the legacy enterprise are going to be those that are tied to the strategic anchor, they are locked to a mandate that we are driving towards as a company, we this is part of our differentiated vision for the future. This is part of one of our core thrusts, this is part of what we’re going to become an AI as part of that investment. It’s not a toy that we’re throwing into the mix. It is part of how we’re going to get to what we’re going to be in the future. And if we can get that and again, that requires some fluency doesn’t get Carl, we can’t get executives to buy in on that unless they see how I actually fits in. And again, now we need that broader level of understanding. But if we can do that, with AI, you know, we can make much better investments. And to your point, for the small to midsize firms, it finally gets to the place where now AI becomes relevant. You’re absolutely right, they shouldn’t be jumping on every tech, they don’t have the money for it, they’ve got to be doubling down where it matters. So I’ve got to give a high five to Mr. Jim Collins, and thank you for highlighting that point. Cuz I think everybody needs to hear that.

Carl J. Cox 23:44

Yep. Okay, so there’s an AI event. And so how when you are looking with your clients, and there is an AI strategy, and it’s done well, how do you measure success that it actually worked?

Daniel Faggella 24:01

Yeah, I mean, measuring the success of a strategy can sometimes be challenging, I think, honestly, you might need half a decade to really get full shake of that. Although there there are some kind of earshot ideas about is this thing conceived? Is it Ill conceived? Or is it well conceived? There are ways to sort of make those distinctions. So I’ll talk about that. And then I’ll talk about measuring success. And in that sense, I’m going to have to turn to actually AI project. So for a strategy in order to kind of gauge its level of ill or well conceived madness. If those are words. We’d want to get a sense of what the existing kind of digital transformation vision of the company is, what is it that we think we’re turning into that’s going to help us better run our operations to be profitable and grow and better service our clients into the future? And does AI fit in as a natural enhancer and kind of supercharger to some of those core capabilities? Or does it not we have a whole article actually called the AI transformation vision Which is about kind of aligning the elements of how data and AI can kind of enhance existing mandate. And if we have a project where AI is kind of this cool extra thing that we’ve tacked on in the side, that’s going to give us some, some bonus side superpower. This is generally of the ill conceived nature. Or sometimes if the buzzwords are just thrown in there like words like predictive or just kind of wrapped in without necessarily having an understanding of why predictive would be particularly valuable in that space or in that area. So again, we want to get a sense of what are we genuinely good at where we’re headed? And in our, our, our projects, and our mandates for investment aligned with those core capabilities we’re already committed to, and that we’re already seeing evidence are helping us pull ahead. So those are rough rules of thumb on a strategy on projects? The answer is, there’s a little bit of a faster feedback loop of projects. And I would simplify this by thinking about three different kinds of AI ROI. So this is actually one of the more important concepts. So in fact, it’s probably among the simplest infographics we’ve ever created. If people got people typing the three kinds of AI ROI into Google, there’s an infographic it’s so ridiculously simple. It’s super easy and memorable. But frankly, I don’t think it’s talked about nearly enough. And we’ve crystallized this and we beat this word from day in and day out, because we watch a lot of projects fail, and we watch it fail a lot of the time, at the level of communication from the onset. Here’s the explanation, three kinds of AI ROI. The first is what we would refer to as measurable ROI. Generally, this is financial, what’s it going to cost? What’s it going to make me sometimes there’s other factors such as, what are our customer service scores, right, something like that. So there’s some needles that are movable that aren’t necessarily dollars. But for the most part, we’re going to make some dollars, we’re going to save some dollars, something along those lines are measurable return on investment, very important. Oh, never advocate to leave out measurable ROI from any kind of AI deployment, this is something we need to consider, we need to have benchmarks that we need to shoot for, they should be realistic, we should talk to data scientists and subject matter experts to make sure that these lofty goals that we have are in any way within the realm of possibility. But um, but measurable ROI is a beautiful thing. The problem is when we we don’t communicate AI, the leadership in any language other than plug and play, here’s what you’re going to spend, here’s where you’re going to make the other two lenses that we advocate, communicating the value of AI through and thinking about investments through our strategic ROI. Strategic ROI is basically answering the question, how much closer does this investment get us to a strategic anchor to a strategic mandate. So again, that could be a key technology thrust that we’re already investing in, it could be part of our digital transformation vision that we want to turn into anyway, on, it could be a three to five year goal that we absolutely are committed to hitting, does it? How much closer does it get it get us to a strategic anchor that we’ve already committed to? And that should be seen as a kind of ROI unto itself? Again, that’s what’s gonna pull tough projects through and allow us to invest in the good to great sense that that you and I refer to the third and in all honesty, I don’t hear anybody talking about this to overt degree to which we do I think a lot of ideas are are bandied about in in great depth by by many voices. I think this one, frankly, should be talked about much more particularly the C suite or big trucks are being cut. And that is capability ROI. measurable ROI is for try capability, Alright, we’re just capability or ROI, essentially, how is this project or experience and investment in this project, setting us up to succeed with future AI projects and unlock the value of data to help us transform into the future. Let me give you some examples of capability ROI. We’re an e commerce company, and we’re going to work on product recommendations. Okay. So we’re going to have to align our you know, our on site analytics, our, you know, CRM and purchase behavior of our customers, maybe our email systems to determine who’s responding to what kind of promotional messages, whatever the case may be. I’m just in the course of lining up and finding the way to harmonize those different data sets, we now have rich new capabilities for business intelligence and potentially rich new capabilities to unearth more AI sort of use cases in the future, we’ve now found that we’ve gotten a deeper potentially understanding of our data, maybe we’ve learned interesting distinctions about features, we’ve learned that purchase frequency is a more important facet of, of, you know, customer lifetime value than we thought or whatever the case may be. So what are the retained lessons learned? What are the improvements to our data in fro? What are the things that we’ve learned about how teams work together? One of the really tough things here, Carl is getting data scientists and subject matter experts to work together to set expectations to work together to monitor the success and iteration of an AI project. That’s challenging. If we can come up with some runbook some playbooks about what we learned about making teams work together productively in terms of a weekly cadence in terms of check ins in terms of how much SEO s SME dedicated time we need to contribute to a project. Now those would be awesome rules of thumb. They’re going to help us move forward. Ultimately, early AI projects in the enterprise, if if done very diligently, are more likely to see capability ROI as the most important return on investment of those projects than they are measurable ROI. That’s not me being a pessimist. I’m not saying Yep, just give up on the money and plan on setting yourself a better foundation for future AI. I’m not saying that I’m saying all three are important. But what I’m getting at is that early projects, you know, run into a ton of hurdles. And if you don’t learn, then prepare to smash your face against those hurdles. 1000 more times. And if people if people don’t take capability, ROI, seriously, if they don’t see projects as a conduit to building maturity, than they are absolutely failing, and how they are conceiving of AI projects moving forward. And if you’re an internal project leader and outside consultant, and you’re communicating through measurable ROI only. wrong move, not doing well by yourself not setting yourself up to succeed not doing well by the client. Hopefully that makes sense Carl.

Carl J. Cox 30:56

Yeah, you know, I think it’s excellent. And I and I love the three different lenses. Who’s the best company in the world in AI, in your opinion today?

Daniel Faggella  31:04

Oh, geez. I mean, look at the idea of a company at AI. I mean, this is hard, it’s hard to directly you know, compare firms. I mean, there are obvious answers here. And I lived in the Bay Area for two and a half years, when I was initially selling my previous firm and getting this one off the ground. So I’ve been in the headquarters of Facebook and LinkedIn and you know, Baidu is AI lab and you know, the rest of that and in all frankness, the preponderance of really far reaching super cutting edge sort of AI action and traction is in you know, the Bay Area. Um, so you know, the the the Amazons the Google’s the Facebook’s of the world are Netflix even our you know, killing it in a great many regards, Airbnb, etc. Um, but but it’s, it’s not a skill set that’s entirely locked and loaded there. I’ll give you a slightly broader answer that’s maybe less drop dead, stupid obvious than the one I just gave you. Which is more around which industries pound for pound are the most permeated with AI? And this is a little bit of potentially, I think, a better question and what what company, what individual company, some company with 90 people in San Jose right now, that is so crystaline in their conception of AI and strategy, so perfect in their initial data infrastructure that, you know, I should be lauding them as the highest but I don’t know of them well enough to understand or know how to compare them. But I think a more you know, potentially useful lens for people to look through. If you want to look at industries that generally speaking are much more AI fluent and AI enabled. online media broadly. FinTech broadly. And e commerce like pure e commerce, not brick and mortar, retail, e commerce, pure ecommerce. Those three sectors pound for pound, if you were to say what are the industries where if I pluck a random company out there most likely to actually have AI in deployment, delivering real value to customers into the business, online media. FinTech and e commerce are really, really cutting edge. So if you want to watch hot spaces, you want to watch what smart folks are doing in terms of making things predictive, running their operations more efficiently personalizing

Carl J. Cox 33:18

the messaging, whatever, I would say go ahead and tune in on those those domains. Where is an example of where a company, a well known company, and then once again, this just helps us because you’re in this and you live this day to day? Or there’s been one of these very well known or perhaps not so well known why reasons why customer service or support, or perhaps even profits have gone down because of just a major AI. disaster, right, meaning the AI gave them a decision path that ended up making such an impact where it it harmed them actually because their logic was thoughtful, but it got them in the wrong direction. Do you have an example of something like that?

Daniel Faggella 34:01

You know, um, I don’t have I don’t have overt examples of this, that leap immediately to mind. The things that come to mind a little bit more frequently are, are potentially, you know, PR gaffes, you know, where we’re AI makes some decisions that, you know, put us in a spooky spot. You know, in the banking space, there’s obviously a lot of trepidation around implementing artificial intelligence because of things like bias and breaking certain compliance laws. I don’t Top of Mind have a single example though. Even in that space, where there’s a lot of nervousness and a lot a lot of money being spent on consultants to figure out how to not step on the bias snake. I don’t have that many good examples of companies, you know, really sinking there. I’m sure there’s some examples in logistics where, you know, some boats steered the wrong way or plane landed the wrong way or you know, some some literal physical disaster ensues. More of the examples, you know, from big clunky legacy enterprises, I guess maybe a benefit of being big and clunky, is you’re probably not going to deploy something that’s going to radically alter the entirety of your customer experience overnight where you probably can’t even do that you can only roll things out small. So, you know, so yeah, I actually don’t have any OMG we made AI do this, and now we’re all bankrupt, you know, half our people are laid off. Now, I don’t actually have one for you. I can tell you, there’s companies like a Wells Fargo or an ally bank that made boodles of noise about their chatbot for you know, half a year and then just killed the project, you know, four months later, but you know, they got a little egg on their face, maybe some customers are disappointed in their share price go down a heck of a ton. I don’t really think so. Um, so yeah, I don’t have any good examples that frankly, I mean, if I had a little secondary research time, maybe come up with some but nothing top of mind.

Carl J. Cox 35:52

So I’ll give you a give you said I was a little bit of a leading and once again, I want to be careful, I will not name the major airline. But I’m pretty confident one of the largest airlines in the US world. They have been using AI to, to change the routes that you’ve done, let’s say you’ve you’ve booked something in advance, and they’ve changed the route. So at least cost routing. Okay, so and and as as a once again, I want to be careful to names. But how do I say is the how quickly it comes out without without being clearly automated. Where it’s sending me off route to I’m going around the United States, as opposed to going direct where how I originally did the path goes three times longer. It was a little bit of an exaggeration, but like 25% longer? Yeah, it’s one of those. For me, it’s one of these examples of a great organization is hurting their customer service where a point where I’m getting ready to change airlines permanently, as of but they’re there to me is one of the small examples of where where I get the logic that’s coming in, they’re thinking about how they can maximize their profits to help get me on to a cheaper flight because I put something in I got an A cheap fare. But for myself, my own consumer standpoint, I booked that flight for a reason. And don’t give me that price if I can’t get it.

Daniel Faggella 37:17

Well, look, I mean, in all fairness, from their perspective, like they have to cater to you as an individual person. And like, if you write angry tweets, they have to reply in a friendly way. But at the end of the day, brother I’m sure you’re well aware that if there’s X number of you that are like frowny face, but they make X amount more money than Oh, you know, that there’s a certain deal with it factor going on there. Now, of course, they’re not going to give you such a such a Curt hold understanding of reality, but of course, you understand that we’re operating in reality. So I don’t, I don’t really know, the actual net impact of that particular use case. Also, routing for flights is like two and a half decades old AI like that stuff’s been going on since way before and now it’s like, like, you were I don’t know, playing Ark, you were in the arcade or something, you know, like Pac Man or something like that. When when that when that stuff was kicking off. So that’s that’s not wholeheartedly new, some of the stuff around pricing, and, you know, uh, you know, the expedience and whatnot are doing some pretty interesting ml stuff. So I don’t know how new any of that is, um, nor do I necessarily have evidence to know if this particular AI use case you’re talking about has made them or lost them more money or customers. So it’s tough for me to judge sounds wasn’t good. So I’m sorry. Yeah. Well,

Carl J. Cox 38:26

I know, it’s been a little public. Once again, be careful of like saying too much about it. But but the fact was, it was it was interesting case of once again, I know though, it appears there’s a logic that’s going behind what’s taking place. And unfortunately, I’m a frequent flyer for so I can call back up and get back to the old one. That’s good. That’s good. But, but the general public who doesn’t have the frequent fliers, they’re not right, you know. Exactly. And so as I said, it’ll be interesting to see. And by the way, I think this is a bit new, because I’ve been flying them for decades. And the amount of flights I’ve been getting changed have not been to this level, which, Okay, interesting, you know, I mean, so anyways, let’s move on. We’re gonna move on, we’ll get just a few more minutes. I’m gonna ask just one personal one personal process. You are obviously really busy ton of research. You are speaking on a regular basis. How do you keep yourself on a personal level to keep the motivation, the excitement? What are you doing to help drive your success on the personal level?

Daniel Faggella  39:39

Yeah, you know, um, well, I think I think everybody sort of has a certain amount of smoldering white hot, something within themselves that I don’t know how, like, you know, nature or nurture, I don’t know 100%. And those things are, I guess, tougher to quantify but you know how wise, I think that one of the things I’ve learned over the last five years, I think if you talk to 22 year old me, I would have ignored these things wholeheartedly, is that, you know, a really strong cadence of proper sleep and food at the right times. And you know, caffeine only at the right times. And, and a good regimen of exercise, I think, is a great base. So I think that there’s all kinds of things psychologically, that are important to sort of, you know, being mindful of progress, I think we’re all motivated by progress. So being able to keep those dashboards in front of us where we can see not only the beautiful end objectives that we want to move towards, you know, those North stars that are really motivating to us personally, and professionally. But also be able to see the dashboards that are showing how we’re moving forward on those. So you don’t have those in front of in front of one I think are important. I know that’s important for me, but frankly, I think that stuff all has to sit on top of a base of, you know, you get enough sleep to be functional, and you get enough enough exercise during the week to kind of keep your body in the right spot. And I think if you have that, if you if you handle the monkey suit, as I frame it personally, if you handle the monkey suit, because we have to operate in one I like to pretend maybe I’m not. But ultimately, I’m I mean, here steering, this hominid thing, I got fingernails, I got I got all kinds of weird monkey stuff. So if you handle the monkey suit, then I think it allows you to focus the preponderance of your time on the great and lofty goal that’s exciting, and the progress that you’re making towards it, which I think both of those things are, are the things that, you know, can really get somebody up in the morning, so, I don’t know that. That’s, that’s, those are probably the big things for me there, Carl.

Carl J. Cox 41:41

So there are settings specifically in 2021 that you’re working on, because you’re just like, Man, I’m not been good with this. And I’m working on it right now.

Daniel Faggella  41:48

Yeah, yeah, yeah. So um, last year, I worked on a certain amount of servings of vegetables every day as a habit. And now I got that really down pat, I’ve always been great with exercise, my always been a jujitsu guy, and always just kind of naturally been able to pull that off. But frankly, bedtimes and wait times are big deals for me now, you know, instead of kind of sleep whenever I’m tired and wake when whenever I kind of need to or you know, where as early as possible, being able to keep a cadence that’s a little bit more predictable, so my body can get a little bit more renewing sleep. So that’s, that’s been a big one. For me, I got a little, a little personal KPIs where I’ll put a one or a zero in the box, if I’m in bed by 1030 or not. If I’m not, it’s like, oh, I’m putting a zero in there tomorrow. That’s gonna suck. So yeah, that’s, you know, that’s, that’s one of the ones working on this year. Good. Good for you. Okay, so

Carl J. Cox 42:40

my love to ask what is a book or a couple of books that you recommend for our audience?

Daniel Faggella  42:46

Sure. Um, you know, I guess it all depends on your age. I think your original framing of this pre interview was what’s your favorite book? Now I’m having to think of an entirely different answer. Feel free. What’s your favorite book? Start with that for sure. My favorite book is Plutarch lives. So Plutarch, a biographer of Greek and Roman statesman, and generals, and he happens to I think the some of the loftiest the loftiest English prose. It’s all Translated by john Dryden. It was the first Poet Laureate in England. So it’s just gorgeously written. But it’s also I think, a really strong grounding on how character affects the trajectory of a person with lofty aims, how does character affect one’s sort of influence on others in a leadership perspective, on the way one responds to different kinds of stressful situations. And it Plutarch lives you read in stressful situations that are vastly beyond Carl, I don’t know the entirety of your life. But I would imagine you haven’t, you know, had to trudge across Alps, well, most of your soldiers are dying, you know, living on the bark of trees for like a year and a half, you probably just haven’t done that. And it’s my estimate, if you I don’t know, you’re

Carl J. Cox 43:57

not fair, I have not done that one yet.

Daniel Faggella 44:00

So um, so it’s a great grounding in sort of what actual hardship is and sort of a reminder of the sort of the petty softness that is the vast preponderance of our own problems. And again, also, it paints a great picture of dealing with with challenging sort of noble and super inspiring ways, but also paints how different character traits kind of good and ill or sometimes neutral things that could could go either way on how those rattle their way forth, over the course of one’s life, through through the lives of very prominent people who, you know, made an important dent in the world. And so I’ve always found that to be one of the most enriching sort of cauldrons that I did my ladle into time and time again for you know, for a good a good pre bed reading experience. I’ll say that.

Carl J. Cox 44:42

Any so is there any other No, that was a great one any other like one maybe, quote unquote, more modern book that’s out there right now. They’re like, you know, you got to read this if you want to learn more about AI to get a basic understanding.

Daniel Faggella  44:57

Yeah, sure. Ai books. Um, honestly. I think I spend my time again like that the head, the head of AI at, you know, the chief AI officer at IBM or something, it’s a guy, I don’t know, if I want to write a book or do I want to spend another hour with the guy running AI at Raytheon or something, it’s like, Romo was always gonna go with the ladder sort of, um, but, you know, if I were to, you know, if I were to if I were to list resources that I think are pretty strong in that category. Um, there’s an AI first company book written by one of our guests, by the name of Ashe Fontana, which is a pretty good book, it’s it’s not quite startup oriented, actually has a lot of transferable lessons to what it looks like to unlock AI advantage, even in a larger enterprise. So an AI first company, ashes book, I think is, you know, at least a worthwhile read. And I’m also a huge fan of Good to Great, I read it again earlier this year, and I think that the section on technology is as piercingly relevant. Exactly. Now, with AI, it’s probably the best distillation of the attitude people February I like invented it before it before it came around. So I, I would and I have recommended people dig into the technology chapter of vertebrates, most of the modern things they can do. I don’t I don’t really like reading modern authors. But if you put a gun to my head here, Carl, I’ll give you a couple. I’ll bark them out if you if you force me.

Carl J. Cox 46:17

No, that’s good. That’s good. You Well, you did there was perfect every dead people. Hey, that’s a principle. No, I love and I love the first recommendation that you had. How can people learn more about you and your firm?

Daniel Faggella 46:36

Sure. So people are generally interested in what we do at Emerj again, I mentioned a couple of resources, I people go to emerj.com and go to the menu. And there’s literally 1000s of use cases across every industry. So if you want to sign up for the newsletter, and you’ll see all of our latest interviews, I mentioned the caliber of people that were able to get in touch with on a weekly basis. You know, they can they can sign up there, we have a resource that’s usually most relevant for non technical folks. And that’s sort of how to detect AI. Ai trends. So if you want to figure out sort of what are the advantages we might unlock in our industry, or where my our industry be headed, there’s sort of secondary research keys that we tuned into, which are really simple to understand. It’s like a five page PDF, it’s emerj.com/t3. So that’s T, like the word trend, and then the number three, so emerj.com/t3. And that’s three ways to detect AI trends, again, for for non technical leaders that want to unlock an advantage, again, not write code, but figure out where could I have the biggest leverage in my business? emerj.com/t3 would probably be a good place to start. Otherwise, you can find me on social and let me know that Carl sent you up, and I’ll give you a big Hello.

Carl J. Cox 47:34

Awesome, awesome, Daniel, this has been fantastic, really appreciate you taking the time for our audience, and to be here today and provide your insights on AI and strategy. And I think it’s really, really excellent education. And to everyone else out there. We just thank you so much for listening to this podcast and wishing you the very best at measuring your success. Have a great day.

Outro 47:59

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