Ian Wilson 7:04

So the definition question is, is one that gets thrown around a lot. And what I tend to do is look at it from the perspective of business, what does business or what language does it already use? So a lot of people will talk about AI and drop back to the kind of the technology aspects, which really for a business is not that important. It’s like saying, Oh, it’s built in Java, or it’s built in, it’s built in c++, it doesn’t really matter. And so when I look at, if I get asked from a business, particularly what’s the difference between AI and any other software, and then just from that definitional perspective, and it’s not really that important a rule. A definition is that other software generally follows rules. And it does what it’s told, AI can learn from patterns to figure out the rules for itself. Now, that’s quite a broad definition on why that’s interesting is that the many parts of your business, lots of chunks, you can define the rules you make, you’ve got a production line, you can specify exactly what’s going on. But many of the most interesting areas like making decisions, you can’t if somebody asked you what are the rules you follow to make decisions, you couldn’t actually figure them out. Because most of it’s in your head, it’s very contextual. You’re using lots of connections to do that. So if I asked you, what is the rule to define a picture of a cat, you probably couldn’t say what it was that there were too many rules. So that’s the area where I kind of bifurcate traditional software from the new world of AI, because it can figure the rules out for itself, which is a step changing capability. Now, where do you put that in your organization? Is that it? Is it data is analytics. I tend to follow what businesses do currently, which is, they call that analytics often. So I would say, you know, Ai, for me is just an extension of analytics. So that’s a mindset to think about how that how you use that capability in your organization. Perfect, perfect.

Carl J. Cox 9:12

And, and by the way, for the listeners, I don’t normally wear a tie. I mean, those are watching on YouTube. But for Ian and I saw when we have a pre discussion about being on the podcast, he likes stepped up. So I’ve stepped up my game. So thank you very much for having me more properly dressed, I think actually have a presentation in Mississippi where I think I have to wear a suit and tie here later this week. And so thank you for getting me prepped again to wearing a suit and tie. But yeah, exactly, exactly. So thank you. Um, so what are some of the

so before I’m going to ask one more definitional question, because you kind of described a little bit when you’re describing AI, but then we’re going to start talking about some of the lessons that you’ve learned in AI, especially once again, since you’ve been really involved with with a significant part of your career. So So what is the difference between machine learning and artificial intelligence?

Ian Wilson  10:07

So definitely schnelle a machine learning is just a subset of AI. AI is the big umbrella and encompasses everything. It’s a pretty broad umbrella term. Now, what’s interesting with AI than other technologies like robotic Process Automation is that it’s an umbrella term that contains many, many things. Currently, the big gorilla in terms of techniques is machine learning, or it’s big brother deep learning, but they’re just techniques. And if you’re a business owner, from the business side, you don’t really need to know that it’s just like a programming language only when I talk about AI to the business, I talk about capabilities. And the capability, for example, is the capability to read a document and understand what it’s talking about. Now, how the AI does that. on the business side, we don’t care, we just care that we have that capability. And so what’s important when you’re talking to business is, you know, through machine learning out the windows through talk about data science out of the window, it’s irrelevant, doesn’t matter. All they care about is what can it do for me, and that is a capability.

Carl J. Cox 11:15

And I love already, how you’ve been describing a lot of this is your understanding the difference between theory and practically applying, right? Because I think a lot of times we get stumped right on the on these big words and language. But the point is, what are we going to do with it? How do we really apply these concepts? So let’s talk about these what what are some of the key lessons that you’ve learned over the years in artificial intelligence?

Ian Wilson 11:41

So so there’s a number and the first one, I say, I’ve got a couple of things that go together. And I think I’m paraphrasing somebody and but being early is not the same as being right, which follows on from being right is not the same as being successful. So one of the thing that’s plagued me and you mentioned, the, the date of my, my degree in AI is being I think I started that in 1993. But I started researching in 91. Was that being early is not the same as being successful. Sometimes markets can take an awful long time to catch up. So that’s been I mean, it seems like great hindsight now that AI is the hot new thing. But given that I started so long ago, it wasn’t always quite so hot. So that’s been a painful lesson learned. And often what you see is the fast follower is actually the one that gains all the benefits, because that they’re not going through the pain that I’ve gone through being kind of out there on the front lines actually defining this stuff. So that’s been quite painful. That’s in you know, when we talk about lessons learned, I mean, it’s, it’s great to be able to dig into all that experience, for example, for that, for the training and education, and strategizing I give to business, it’s more a case, it’s not the case of saying, Here’s something theoretically, that I think will work. I’m saying, I’ve tried this, I’ve tried 100 different ways of doing this that you won’t probably want to try, they all failed, don’t do that. I can show you the bruises and scars from doing that. So I can pull on that to say, Yeah, I tried that didn’t work. I tried that yet, don’t don’t do that. But here are the things that did did go well. So I’m trying to basically extract some value from all of that pain. I think a couple of other things in terms of lesson learned is, it’s easy, particularly when you’re looking at the shiny new technology like AI is to get distracted. But what’s important to focus on only what’s necessary. So I look at this from a couple of perspectives. It’s not just the technology or the business perspective of AI, but I’ve been doing this from the perspective of actually building my own businesses. And in that respect, it’s very important to focus on only what’s absolutely necessary. So in terms of, you know, building out a consultancy business, for example, it’s finding those connections where people actually want your service. There’s 1,000,001 things you can be doing. And in my past, it’s particularly doing something really interesting, like AI technology, you can shoot off on all different directions, because it’s cool and interesting and lose sight of what the one thing you actually need to be focused on. So that’s another one. I think the last one I’d look at Well, last couple. I think this one’s important. Focus on where the value is not where the attention is. So that that’s been you know, so quite painful lessons for me to learn is that I’ve seen in hindsight that it’s easy to think, you know, Where the value or where the value is based on where the attention is, and you’re gonna build something for a certain audience only to find that there’s a different part of the value chain, where really all the values accruing. And what I’ve learned from that is to step back and look at that value chain first, before I go and jump ahead and start doing something to understand, where’s all the value going to go, because I could jump into an area that everybody’s in and fight, everybody’s screaming, you know, fighting over scraps that are left at the end of the value chain. And that’s certainly been true with with AI, because there are many areas where lots of people are fighting for really low quality work. And there’s really not that much value in there. But certain areas are where it’s likely to accrue. I don’t want and this is key for my business is businesses don’t want AI, they want answers. And that’s kind of the mantra of my business. Because what it says to me is, don’t get hung up on the technology, call it AI machine learning whatever you want. The business does not care. They want answers to questions, and that’s what they want that capability to deliver. So if there’s a mantra in my business, it’s businesses don’t want AI, they want answers. Yeah, I love that

Carl J. Cox 16:18

part. Because and what I loved also how you talked about the question of when, when are we creating real value? You know, it’s, it’s, it’s so popular today today to say we want to use big data, or we need to do a digital transformation, or we need artificial intelligence. But I honestly think in many cases, people are seeing the shiny object, you know, that’s been there, it’s the next measurement fads and technology fad, but really being able to apply that information to properly use for their business is so critical, because we could waste 1000s millions, you know, in some cases, billions, right? On inappropriate amount of information, because it’s not creating any value. And a lot of our audience members, of course, the majority of the world, right is small to medium enterprises, you know, that that they are the SMEs is the is the nice short term for that. So give me an example of where an SME, who you know, they’re never gonna be able to compete right with your former employer, HSBC, or with Facebook or with Google or with those the nature in terms of the amount of data and information that they’re doing from a big perspective. But what can a smaller SME organization? When should they consider getting into this field, and actually spending money to get value? That’s

Ian Wilson  17:40

interesting. So I was looking recently at the recent chief data officer, for one part of HSBC, noted that he’s managing a transformation, and his budget is in the billions, wow, for his transformation. Wow. So when you talk about not being able to compete with that, now, here’s the interesting thing. With his billions, he may achieve less than an SME can do with millions. Now, why is that? Because their real estate and their scope is massive. And it’s gargantuan, and so they have to spread that money out across so many areas, that it’s easy for an SME to think, Oh, I can’t compete with that. But actually, you may be able to very much focus for my business is actually on SMEs. And what it’s the focus there is to take the lessons learned from major enterprises, and bring that to an SME. Now what, while they have an advantage in scope, an SME has an advantage in speed and agility. And that actually, for me, is the most important thing with AI right now. And also, that they can learn from the mistakes that the enterprises have made, because they were the first out of the gate. And they made many and I made money with them. In a working at many of the banks as an example. You know, one major Swiss private bank, I helped them deploy their first big data application and their first AI application together, so they’ve never deployed any of them. So of course, all of that was learning. So bringing that those lessons learned, or rather, bringing the things not to do to SMEs is important, but there are some very simple rules for me, and it all starts with executive understanding, because it is pointless for you to go out doing AI the shiny new thing without really understanding why you’re doing it. Why is this going to benefit my business and universally across across the globe? Everybody’s run out like that, let’s do AI. And they’re in ties perhaps by consultancies or other thing. Yes, you can get some quick wins. Let’s do some quick win use cases. That’s the mantra almost universally ends in failure. And the point of the day with AI is where’s the ROI? They’ve had groups haven’t found ROI because they’ve set off on the wrong direction. They’ve set off trying to do something quick, cheap and easy. And AI is anything but quick, cheap and easy. So you set yourself up from the beginning for failure. So what you need to do is try and set yourself up from the beginning for success. So that means step number one, your board, and executives have to understand why, from a business and economic perspective, were even doing this in the first place. Where is our business gonna go? And there’s three key elements of that, that I really build a framework around. One is optimization. That’s generally operational optimization. The second is innovation. So how do we reinvent our business using this? And the third that almost universally doesn’t get talked about is monetization? How can we monetize our data and analytic aspects? And the almost nobody’s doing that right now, which is odd considering when you look at the, the market capitalization of a company like Google 95% plus of that market cap is intangible assets? What does that mean? data, data and analytics. So if you’re a traditional business, and you want to be valued like Google, you need to be able to monetize those assets and understand the value. So the second step is, once you understand why you’re even doing this, you need to understand what how you’re going to get those benefits. And that starts with a strong reality check. To understand that this is this is not cheap, easy or fast, there are not going to be quick wins, yes, you may get some, but don’t start off thinking that that’s where you’re going to be, because you’re setting off on the wrong mindset.

And then you need to understand, you know, if you want to do this, sustainably, scalably, you’ve got to put in place foundations, and that’s typical, every emerging technology. Nobody ever wants to do that, they always end up having to do that, having gone through a period of doing it without foundations. So yes, I understand the need to want to move quickly. And that’s fine, you can do these things in parallel, you can start doing some biting off some chunks while you’re building the foundation. But if you know you want to build the foundation, when you do these little use cases, or bite off chunks, they’ll be in service of building the foundation as well, because you’ve thought of that ahead of time. So that’s kind of step number two is think about what are the foundational structures you need, put in place an organizational structure like a center of excellence. So you’ve actually got a team of experts, It never ceases to amaze me how enterprises go from one emerging technology after the other by tacking a project manager on the shoulder and say, you do big data, you do digital transformation, you do IoT, you do AI, with people that are not experts, you would never go and try and get open heart surgery from from somebody at a grocery store. Why? Because they’re not an expert. Why does the enterprise continuously not use experts? It baffles me anyway. That number three, then is, once you once you understand, you know where you’re headed? What’s the road that you need to plan to actually get that? So that’s your strategy. And again, the key point with strategy is it needs to be in your AI strategy is something that’s in support of your kind of corporate and your business strategy. It’s not something in and of itself, generally, unless it’s part of, you know, how are you building your foundations, but you say, What can AI do for me, if you’re if you’re a business owner, and you’ve got a strategy? Let’s say it’s, you know, you want to expand your customer base? How can AI help me do that in a way that other capabilities can’t do? Because of course, it has to have a reason to exist, it has to be something that you couldn’t do elsewhere. Otherwise, in a way that’s cheaper. And there are many capabilities it offers like product recommendation, market segmentation, all sorts of amazing stuff. But you that’s the way you look at it, we’ve got this strategy, we want to do X, is there a way that AI consumer challenges getting to x? So that’s the strategy. Then last thing, then is once you’ve got that, how do we look at across our enterprise, our business, whatever the size, is to look at each individual business area and see where can AI help us in a quote unquote, use case fashion? What tasks are available, what decisions need to be made? What answers Do we need, that AI can provide across our enterprise and of course, there’ll be 1000 areas that you need to evaluate and rank because you can’t do them all. But that’s really the next key point is listing out all those items, valuing them, ranking them, and then executing is the final thing. Which is what everyone would like to do a step one. But really, it’s not, it’s the end of the road. So just be methodical use a framework. It’s not really rocket science. But again, so many people just want to chase the shiny object, start delivering something immediately, which I get I understand. And then they come back in a year, two years and say, where’s the ROI? Why did we waste all this money? You know, like your business? You’ve seen this 1000 times, I’m sure. 

Carl J. Cox 25:30

Oh, yeah, it’s, yes, I have multiple times. And it’s worse. And I can’t say I’ve not been guilting myself, right, I’m getting excited of the shiny object, right? And that that’s there. But, but from a strategic perspective, and from an ROI perspective of measuring success, we have to figure out what it’s going to do, whatever it is, whatever that whatever that goal is, in determining strategies to actually get the outcome that we’re actually trying to get to in the first place, right? The outcome of AI for the purpose of AI is foolish. Right? If it’s not actually solving a problem, it’s just information at best. And and I think it’s even interesting when you brought up Google, you know, Google was floundering in its early years, right, it had a really cool website, that we can search faster than in their place where it had beforehand, but they didn’t know how to monetize it. Until they figured out how to monetize it, all of a sudden, the value got created, right. And now therefore, it’s one of the most successful companies in the world today. And that I think, is really fascinating of once again, learning to see where we can discern habit. So. So from your perspective, it’s it’s common and important, it sounds like that. Organizations have to have something that they’re measuring simple success. And I think in your case, you’re saying often there should be a return on investment for doing it is that fair to say?

Ian Wilson 26:50

So there’s a number of elements, actually the most interest one interesting point I find with with any type of organization is even if they’ve identified where they’re headed, you know, we want to be the next Google we want to do whatever. And they’ve identified once between where they are today, and where that Nirvana is, is what I call the fog of war. So that problem is not often one of desire, or even one of budget, it’s one of risk, because they can’t see between where they are and where they want to get to. And that’s logical from an executive or a board’s perspective is, they might know that want to do it, they might even see the benefit. But there’s too much risk, they can’t just go all in, because that would be too risky. And for me, the key then is as a consultancy, is to de risk that journey. By continuously getting rid of that fog of war, and giving a business or a methodology to go, de risk by de risks on some of the single fundamental focus is de risking at each stage, because every time you do risk, the company gets learns, gets more confidence, and there’s less threat ahead. And that for me is the fundamental problem with new technologies is because every organization I talked to, they’re so keen on using it, but where how when, what its own risk in front of them. And so when we come to talk about foundations, that becomes a problem, because it’s a lot of money and time and effort to build that. And what comes at the end of it, well, you can’t exactly see because the fog of war is there. So when we’re looking at there’s many measures of success, but for me, probably the top level one is caught that continual de risking, because that gives you confidence to move further forward at speed.

Having said that, I kind of break measures of success in into two, two layers. One is from the enterprise level, the other is from the level of an actual AI use case, which is what people typically think about when they think about value or success from AI. They think about the individual use case, which is point of reference that I mentioned whenever I can, is an AI use case is not a customer journey. It’s not a business process. It’s a task, an individual task. Now, if anybody takes away anything from this discussion, think about tasks for AI, it doesn’t live, it doesn’t do everything by itself, that’s really important. But in terms of, you know, success, so from the enterprise level, I think there’s three elements adaptability, resilience, and and assets. So adaptability for me is actually one of the is probably the key driver because what you’re trying to do fundamentally as a as an enterprise, you think, you know, you’re just there to deliver your products and services, but you’re actually not you’re there to adapt to the market because it changes it’s not Static, the business is not in a static environment, it’s in a dynamic one. And so how well and how quickly, you can? Well, it’s really about how quickly how quickly can you adapt to changes in the market. AI can help you do that, particularly if you think about it, automating things like making decisions that are really complex. So enterprise adaptability is, you know, how quickly efficiently and profitably, the enterprise can first of all identify and then take advantage of opportunities in the market. So if you’ve come cheap move on a dime, like a startup can, you can quickly take advantage of those opportunities. As we mentioned earlier, with HSBC, for example, or any other organization of that size, that it’s very difficult for them to move, you know, they like with Titanic, it takes a long time to move, so they’re not really able to adapt to market changes the way an SME can. So that’s where SMEs can focus on winning. Next one is resilience. And we’ve seen this with a pandemic, the companies that one were those that were resilient, and particularly, you know, I note I saw, for example, Goldman Sachs saying that 90 or 95% of their staff are working from home. And it didn’t interrupt their business at all in any way, because they were so advanced in terms of their digital transformation, which is entirely unsurprising given the nature of their business. But this is really, you know, to look at to step back from resilience, it’s how quickly efficient, efficiently and with the least drop in profit, yes, I’m reading this, the enterprise can identify and then avoid market shocks. So this is kind of the flip side of agility. It’s where we’ve got a market shot, and how can we protect ourselves from that. And again, protection is all about the speed of how quickly you can do it, because if it takes you too long, you’re already dead. If you can do it quickly, you can protect yourself against those shops. And again, that’s all coming into optimization and automation, where AI can help with that. And so how well you can do that, for me is an overall measure of success. Then the last one, and I mentioned this before, is looking at enterprise assets. So this is looking at really building your enterprise value. And so many businesses right now would have, you could sit to most enterprises and small SMEs, for example, you could ask somebody in that group, how much is the value of all the chairs in the business, and somebody probably has that information and can give you that number for chairs, or pot plants or coffee. But if you ask any enterprise, or SME, how much is the value of your data and your analytic models? Nobody would know. And as I mentioned, from, you know, only s&p 580, or 85%, or more of the market cap of those companies is made up of intangible assets, which is mainly data. So the majority of part of the value of your entire business, probably nobody can actually tell you, which to me is amazing. So the third piece of this measure measuring success is how well can you measure the value of your data? And how many how much can you value the value of your your enter your analytic assets? So that’s kind of the third part. There’s more on use cases. But I’ll pass it over to you at that point, because we can maybe step through that a second part.

Carl J. Cox 33:32

Yeah, yeah. And it’s this has been fascinating. I you know, it’s interesting, one of those pieces that I’ve heard beforehand, when it comes especially smaller organizations, even consultancies, is your asset growth or value is your email list? Right? You know, there’s an example of right where you’re at, it’s like, well, how is that a valuable by definition, right? If you haven’t continuous connection with those who want to be connected with you, and you provide value, you know, there should be theoretically a measurement where were those assets, meaning your email addresses ultimately, a buyer who might be willing to either share or give or provide are actually buy directly from you. And that’s the, you know, part of the value and it is interesting sometimes, but people often they just watch it in an email list. And they’re not really seeing how it can be driven and being done for more value going back to, you know, Google had all this information and they didn’t know how to monetize it, going back to that original point. Four times siggy. And I can see we can go on for hour especially because we both love strategy, blah, blah, blah. I’m going to flip this around to the personal side, and we’ll just kind of touch briefly here. You know, you are a busy person and you’ve been involved in a lot of different aspects you are clearly keeping on top of your game when it comes from the business side. How on the personal side are you keeping yourself together so to speak, to stay away from the stress and keep up at night to read all the things that have Have to do. And so to give, give the audience a little bit of a feel for what you do on the chair,

Ian Wilson 35:06

I’m keeping it simple, enjoying what I’m doing, I think those are a couple of the key points, you know very much I look at myself as a work in progress. So, you know, perhaps I look at personal life in a bit of a business perspective, but I think that, that, that’s quite typical. But, you know, I, you gain the hindsight of, of having done many different things and trying to learn from that. So life now is a constant educational process, it’s great if you actually enjoy learning, which I do. So I enjoy keeping on top of things, which I think is important, but I try and also, you know, where possible, keep on top, focus on my strengths, and try and get support for where I’ve got weaknesses. So you know, working with partners, for example, that a great at doing things like sales outreach, which is not really my thing, my thing is understanding the strategic aspect and helping those business, but how do I, how do I reach out to those, I’m not great at that. So how to de stress will try and help get people to help you do the things that you’re not great at, focus on the things that you are, so that that that becomes good. But I also just try and stay grounded and simple. Just get the basics right, eat well, exercise, you know, sleep, well keep a balance in life, try and have a sense of humor. So if you’re not keeping things to set to serious, it does help having a family because that keeps you focused on what’s important. You know, depending how old you are, that can be that can be different. But certainly it does take you out of focusing on the wrong things often, because you’ve got responsibilities, you’ve got accountability that you need to keep focused on. So I mean, those are, you know, for many people, for most of human existence, those have been the basics. And I think it’s easy for us in our 21st century life to get distracted by 1,000,001 different things. But these things that are basic and simple, have worked for us, you know, for all the rest of the previous time. So you don’t need to reinvent this stuff. But it was already there. So I find going back keeping to those basics is what keeps me kind of sane and focused, and going along a pretty straight path.

Carl J. Cox 37:28

Perfect. So is there one thing in particular that you’re working on right now you’re like, man, I, you mentioned your work in progress. I think that’s that’s why statement we all are working progress by definition. The question is how well we’re working at it. Is there one thing of from an exercise or reading or personal life thing, like you’re trying to focus right now to get to get better at? And just kind of curious on that side?

Ian Wilson  37:52

Um, I would say? That’s a good question. What am I focused on, there’s probably a number of aspects. Just, I think reaching out to more people, building on more people building those team connections. Interestingly, that’s been something I’ve really focused on during COVID. And this virtual looping has helped me do that and build a lot of partnerships with different people. Again, this is part of it is just to focus on where I’m not strong, but also part of it is just to build those connections and understand that from learning from other people like this, the discussion we’re having here, I know lots flows from that lot. It gives me support, it gives me you know, encouragement that perhaps I’m on the right direction, or I can learn that Oh, no, actually, no, they’ve got some good points that you might want to think about focusing on different areas and seeing how other people are doing things, too. So I think that that’s been probably a focus is reaching out as much as I can to other people to build builds a bit of a stronger connection, which is ironic in the COVID. Virtual age.

Carl J. Cox 39:11

it’s wise to i think that i think that’s a really good suggestion. All right, so we’re not in the book stage. You gave a great list about just just run through. Because I you had the two business ones you mentioned, run through, you don’t have to give like a whole really actually explanation. What are the six books that you recommend into the audience that they should consider?

Ian Wilson  39:33

Yes. So I wanted to put together a bit of a few different perspectives. So I broke these up into business kind of science focused and fiction, too. So business and these were all books that that really made me step back. Oh my god. That’s a different way of thinking. So it wasn’t just something new, but a different way of thinking. Two business books that are closely related. One is called the Four Steps of The Epiphany by Steve Blank and That was followed up by his, you know, almost disciple, Eric Reese, both of those guys I’ve met and talked to who produced a book called the lean startup. And those were two kind of very foundational business books because they were talking about how to think, particularly from a startup perspective, but it applies to any business. So those were ones that really made me step back. From a science perspective, a couple of one book that many people may have heard of, is A New Kind of Science by Stephen Wolfram. 1200 pages long, absolutely stunning book. And the kind of thing that you should try and read end to end would really kind of blow your mind at the end of it. Another is called Zen and The Brain, really obscure book. But it helped me understand some foundational aspects of how we think, and also put me on the path to learning about zen meditation, which I would love to do. But I don’t have the patience to two books about fiction, focused on science fiction, which is not surprising given the name and kind of a bit of a techie. One is Snow Crash came out in 1999, and gave me a vision of the future that actually is very similar to what we see now. But again, made me think about things in a completely different way. Second one is The Three Body Problem which I forgot the name of the author, which it shouldn’t have, but gave me two interesting perspective, from a perspective of thinking about science from a completely different way than I’d ever thought, but also is written by a Chinese author. So it gave me a perspective of life from the perspective of someone from China, which to me, reading authors, from completely different backgrounds gives you a completely different way of thinking. And I think all of those six books have that key theme running through them, they gave me a different way of thinking, and that for me, is a real asset to have.

Carl J. Cox 41:53

So Ian, where can people find you to learn more about you?

Ian Wilson 41:58

Yes, so you can find me a couple of few places, LinkedIn, so just search for Ian Wilson, and you will find me. But if anybody’s interested in taking my masterclass in business, Ai, that’s 1000 minutes without more than 1000 pages of presentation 15 modules and you can find that courses.strategy4.ai or if you just come to strategy4.ai You can find me there as well.

Carl J. Cox 42:26

As Perfect. Thank you so much for sharing that Ian. It has been a true pleasure and learning experience to have you on the Measure Success Podcast. Thank you so much for being on the show.

Ian Wilson 42:35

Thank you for inviting me, Carl, thank you to the audience. Absolutely.

Carl J. Cox 42:39

And to everyone else, we’re wishing you the very best at measuring your success. Have a great day.

Outro 42:46

Thanks for listening to the Measure Success Podcast. We’ll see you again next time to learn from the best. Remember to subscribe now to get future episodes.