As a business leader, you need to make decisions that set your company up for success, now and in the future. The challenge is In this fast-changing world, the rules of strategy are being rewritten and the go-to solutions you once relied on are no longer enough. The Fourth Industrial Revolution is here. As emerging technologies like AI and blockchain become ubiquitous, they will unleash unprecedented levels of disruption.
Drawing on his broad global experience, our guest delivers the essential guide to strategy for this new era. His CLEVER Framework will help you understand the deep strategic drivers of the Fourth Industrial Revolution, reflect on how they affect you and your business, and respond effectively.
If you’re ready to fulfil your potential as a leader and create a future-ready business, it’s time to get Clever.
We welcome Alessandro Lanteri, the author of CLEVER: The Six Strategic Drivers for the Fourth Industrial Revolution
Full Transcript below:
Alessandro Lanteri – Final AAC
[00:00:00] Steve Jobs: [00:00:00] Stay hungry, stay foolish.
[00:00:13] Aidan McCullen: [00:00:13] this show was brought to you with tanks, to Microsoft, for startups as a business leader, you need to make decisions that set your company up for success now and in the future. The challenge is in this fast changing world, the rules of strategy are being rewritten and the GoTo solutions you once relied on are no longer enough.
[00:00:34] The fourth industrial revolution is upon us as emerging technologies like AI and blockchain become ubiquitous. They will unleash unprecedented levels of disruption drawing on his broad global experience. Our guest today delivers the essential guide to strategy for this new era. Is clever framework will help you understand the deep strategic drivers of the fourth industrial [00:01:00] revolution reflect on how they affect you and your business and respond effectively.
[00:01:06] If you’re ready to fulfill your potential as a leader and create a future ready business, it’s time to get clever. We welcome author of CLEVER the six strategic drivers of the fourth industrial revolution. Alessandro Lanteri welcome to the show.
[00:01:21] Alessandro Lanteri: [00:01:21] Thank you very much. Thank you for having me. It’s great to be here.
[00:01:24] Aidan McCullen: [00:01:24] It’s fantastic to have you on the show. Alessandro as a professor of entrepreneurship, executive educator, and advisor, the number one question you were constantly asked is what do organizations need to do to remain successful in the face of such unprecedented change?
[00:01:41] Alessandro Lanteri: [00:01:41] The business world is changing really rapidly around us.
[00:01:45] And business models are increasingly short lived. So the decision makers have to make faster and more frequent decisions and they have to do so, even as the rules of strategy are being [00:02:00] written, these are big deep changes. So for one. Industrial scale is no longer a sustainable competitive advantage as it used to be.
[00:02:11] And the reason is industrial scale is now available for rent almost for everyone you can sell via Amazon. So you don’t need to open a store. You can fulfill your shipment with very efficient express corridors. So you don’t need to have a logistic platform yourself. Logistics suppliers even manage your fulfillment sometimes.
[00:02:33] And of course you can crowd source your workforce on the internet, so you don’t need to have an office or. Computers, and this is a big change. Of course. Another big area of change is that companies do not compete with each other directly anymore. I mean, even as they compete, they also supply each other and they collaborate.
[00:02:58] The first example that comes to mind [00:03:00] is Amazon and Google. They obviously compete in the market for operating systems or mobile phones where iOS and Android are the two big solutions and they compete of course, but at the same time, as they compete Google, Apple, Several billion dollars to ensure to be the default search engine on Safari and vice-a-versa Apple pace, Google for its cloud.
[00:03:29] So it’s very odd to see two competitors that buy the supply each other. So they depend on each other. In fact, when Apple was no longer able to use a. The Google maps, it took a hit. So you can imagine that if one of the two companies were to disappear, the other one will suffer, at least in the short run.
[00:03:48] And other one is that business models are no longer defined by the industry you’re in. I like to think about Uber, Uber, please. Effectively with the same business model across those industries. [00:04:00] Of course they do, right? So they replaced a chauffeur or, or a taxi, but they also deliver food and they also deliver parcels.
[00:04:07] And they also bring patients to hospitals. These used to be entirely different industries, served by different companies, operating different business models, and that’s no longer the case.
[00:04:19] So in formulating the clever framework, you combined methodologies from business research and from the emerging discipline of future studies on you included all the signals of change that you could come across emerging trends and deep drivers of these changes all resulted in the type of framework I’d love.
[00:04:36] Have you shared an overview of the clever framework before we go deeper into each of the six labors?
[00:04:42] Yes. So the, the research took, uh, quite a number of years, as you can imagine. I, I investigated the over 70 case studies. I looked at all the changes I could observe that are emerging in response to the faster, fast changing and [00:05:00] turbulent times we live in.
[00:05:01] This year, our, we call it the 40 basil revolution. And then I looked at the trends that kept these different changes together. And then I dug a bit deeper and I started looking at the forces. So we call them the drivers. What are the big forces that will affect strategy in the future? And I came up with six.
[00:05:22] The first one is collaborative intelligence learning systems. Exponential technologies. And these three really are forces that depend on the shaping and the characteristics of the digital technologies. And the next three are forces that have to do with business model and decision making. And the next three forces are value facilitation, ethical championship.
[00:05:47] And responsible decision making.
[00:05:49] Aidan McCullen: [00:05:49] This is exactly what both Apple and Amazon have done, and it highlights how intangible assets have changed the rules of competition, because they have four unique [00:06:00] properties. I’d love if you shared all four properties.
[00:06:03] Alessandro Lanteri: [00:06:03] Yes, absolutely. So the first property is that they can grow really quickly, weed unlimited amount of further investment.
[00:06:12] So they are scalable. But unfortunately, once you’ve invested even little amounts of money, this amount of money that you’ve invested cannot be recouped in case of bankruptcy. Or if you want to shut down, there’s hardly anything you can actually sell. So economists call these sunk costs. Once you’ve invested the money it’s gone.
[00:06:36] And because many of these infrastructures are available for rent, they are fairly easy to replicate. You can see dozens of companies running effectively identical business models. Uber is of course a great example of a successful company, but as it emerged, there were those of other companies doing it almost the same, because it’s so easy to [00:07:00] replicate existing and emerging solutions.
[00:07:03] But then in order to protect. Your business, you have to find the right balance among different elements, like your brand power, some patents, and especially in this diamond age, the way you collect and you use proprietary data. So these are the four characteristics, scalability investments are some costs, easy to replicate, and you can protect them through complex synergies, well, different drivers of value.
[00:07:30] Aidan McCullen: [00:07:30] I think it’s really important to understand that and to get a grasp of what like you do in the book. You give deeper descriptions of the industrial revolution that came before this fourth industrial revolution. You say. And in the industrial revolution, the manner in which value is created fundamentally changes in response to substantial technological development.
[00:07:50] The previous methods of creating value rapidly become obsolete and new business models and organizations soon emerge. This shift drastically alters how [00:08:00] our economy functions on every conceivable level. The businesses that actively respond to and embrace the new opportunities resulting from this change, achieve success.
[00:08:10] While those that stick to the old ways tend to go out of business. Now let’s use that. To give a top line history of the previous three industrial revolutions and how they impacted society.
[00:08:23] Alessandro Lanteri: [00:08:23] Yes. Excellent. So towards the end of the 18th century, uh, that’s when we experienced what we now call the first industrial revolution.
[00:08:33] Of course at the time it was called just the industrial revolution, because we didn’t know that we’re going to be more, uh, the big technological change that emerged the new solution was steam. The steam power powered. Large machines that make mass production possible. And in order to, to put together the huge investments required and to [00:09:00] explore to the possibilities of this technology, very large organizations emerged effectively for the first time in the economy.
[00:09:09] In the past, we had larger economy organizations, but mostly around the army or the management of the state. But business wise, this is the first time we have large, larger organizations really. And that led to. Division of labor. And for the first time we also needed someone to tell others what to do. And so management was born for the first time.
[00:09:34] In fact, probably almost every teen we know in the discipline of business management leadership. Somehow dates back to those, to those days. Now about a century later at the end of the 19th century, we had what we now call the second industrial revolution made possible by electrification. The big change between the first and the second technologically speaking is that.
[00:09:57] With steam power, you can, you [00:10:00] can have a lot of energy in one place where the steam engine was. And then you had to transfer this energy, uh, throughout the building or trout your factory. But with electrification, you could actually bring the power to the, to the each individual machine and each could run its own smaller scale engine.
[00:10:21] So that made it possible to, uh, distribute production in a different way. Assembly line became possible for the first time. Now for the first time we started seeing that entire systems of production could be split into two different tasks and there’s different tasks could be coordinated smartly. And that was the job of, uh, uh, managers.
[00:10:43] It evolved that way. And this is the year when we see the emergence of what we call scientific management. So designing it. The flow of work and activities, uh, designed to maximize the output. So create efficiency. [00:11:00] And then towards, uh, you know, means to late 20th century, we had the third industrial revolution, which is perhaps what we’re experiencing.
[00:11:09] It’s, we’re probably at the, at the end of that. And this is when the discovery of the transistor for first made the computers possible and computers made many changes possible of course, in our business, uh, experience in our daily life. So. Automation became possible for some tasks. And at the same time, communication became much easier.
[00:11:34] So when communication becomes easier and you have more automation, you are now able to do slightly more complex tasks, which are ideally addressed by teams instead of individual workers. So in a way, the dirty industrial revolution became, uh, the stage for teams at work. And when you have teams, you need a new type of management.
[00:11:57] So you have. Project managers, [00:12:00] especially because now tasks are designed and managed differently and they should coordinate and our teams. And then of course we would be talking maybe a little more about the fourth industrial revolution, which is the era we’re experiencing. Now the one thing I want your audience to remember when we think about any industrial revolution, there are a few things that are all important.
[00:12:21] So first you have a big technological change and that’s powers or drives a change in business models. One one or two or a few business models start changing entire industries, get rearranged or transformed around these new business models. So entire industries change. And when entire industries change, soon enough economies are transformed.
[00:12:44] And when economies are transformed, then the distribution of wealth in societies, even where people leave changes depending on where production becomes possible. And so societies respond. So industrial revolution start from a technology, but then impact entire [00:13:00] society.
[00:13:00] Aidan McCullen: [00:13:00] I was throwing myself back in. I was going to go imagine I’m in the second or the first or the third industrial evolution.
[00:13:08] And somebody telling me about one day without this thing called 5g and computers will speak and understand language, et cetera, et cetera. You’d be thinking that person’s crazy. And these. Crazy people, these outliers who can see the future and work towards it are the people who really changed the world because they started introducing these new concepts.
[00:13:30] These changes are coming and because of exponential change, they’re going to come way faster than they ever came in the previous industrial revolutions. Absolutely.
[00:13:40] Alessandro Lanteri: [00:13:40] Right. I couldn’t agree with you more. This is one of the key teams, of course, in my book as well. Look, I, I, I don’t think you even need to go as far back as the second industrial revolution to see that when I talk, when I give presentations, I usually use the example of the GPS.
[00:13:59] I mean, in the [00:14:00] 1980s, the GPS system was a tall radar tower. It was stolen in a military compound because that was military grade technology. So you can imagine looking at this tall tower. And if you, if you pointed at this tall tower and, and told anyone one day, every person will have one of these in their pockets, they would love for you.
[00:14:23] But sure enough, about 30 years later, every one of us has in fact, probably more than one GPS. You have one on your phone, one in your car, one in your smartwatch, you barely notice, but that’s the speed at which we experienced this things.
[00:14:36] Aidan McCullen: [00:14:36] Yeah. And we’ll come back to that because that’s one of the letters that come out of that it’s lever framework, exponential change, but I live the third industrial revolution came three mega trends, and they’re critical to understanding this current revolution.
[00:14:51] And the first is technology and innovation, the rise of digital computing and a lightening fast internet.
[00:14:58] Alessandro Lanteri: [00:14:58] Yes, that transformed [00:15:00] really quickly the speed at which we can communicate. So that’s a very strong first trend that emerges from technological evolution, of course, but that leads to the second big trend, which is a globalization because when communications become communication becomes easier and smarter and seamless, you can.
[00:15:23] More easily control a distributed geographically distributed production system, but then that also opened increasingly the doors to competition from a no cost. Countries. So when communication and transportation becomes cheap, mobilization grows. But when globalization grows now, suddenly you have what economists call efficiency, driven economies.
[00:15:51] So economies where the cost of productive factors are low and that’s where they, they, they derive their attractiveness. [00:16:00] So this, this transformed a little bit, the way we compete. So physical goods are, are no longer, very, very valuable. You can’t really make a lot of money by selling tractors or excavators, all those fees of machineries.
[00:16:14] Uh, if you are an American company, because you’re Asian combated, those now can make a very similar machine, maybe over slightly lower quality, but drastically cheaper. So for a customer, for a business here in the West, Buying tree cheap excavators is still better than buying one expensive excavator because that of course gives a lot of different benefits and the speed of their production.
[00:16:40] And this leads in a way. So with more integrated economies because of globalization, mega trends emerge is that it’s a change, a big change in demographics. So. There’s more people traveling more and consumption styles, overlap and merge. These are very strong [00:17:00] forces that are shaping
[00:17:02] Aidan McCullen: [00:17:02] our word. And I was thinking about how those forces have collided and a huge collision of that has been the recent global pandemic.
[00:17:11] The COVID-19 pandemic, but it also happened back in 2008, 2009 because of these trends, because they were all interconnected because they all collided in such a way that one little ripple, hard, massive tsunami effect throughout the world.
[00:17:28] Alessandro Lanteri: [00:17:28] Yes, you’re absolutely right. And this makes our world increasingly difficult to predict, understand and navigate.
[00:17:36] So there is this very famous acronym, VUCA. The UCA, which stands for volatility, uncertainty, complexity, and ambiguity. And we use now in this was, uh, initially, um, an acronym used by the military. After the end of the cold war during the cold war, the award was kind of simple [00:18:00] either you were pro Russia or pro us.
[00:18:03] And that was it. If something bad happened to one of your lies, it was bad. If something bad happens to one of your enemies alive, it was good. It was much easier. Now that’s no longer the case, of course. And for the business world, what you just said is a great example, a tiny, small, relatively small event at the other end of the word can put you out of business in a matter of a couple of months.
[00:18:31] There are economies that, uh, where the GDP one down 12 is expected to go down by 12, 13% in 2020 for something that allegedly started in a West market, in, in a province of China. And these are of course, or, or for the financial crisis of 2008, it was started most with mostly because of the, of the underwriting practices of mortgage officers in [00:19:00] banks in the U S no, if something like this can affect your business.
[00:19:07] Unexpectedly and dramatically, you can’t keep track of every such change that’s going on around the world. Right? There’s too many things. So what you have to do instead is prepare, be prepared for multiple contingencies.
[00:19:21] Aidan McCullen: [00:19:21] I was thinking about this on how it’s impossible for a CEO of an organization to be involved in.
[00:19:29] Every decision within the organization. And this is why innovation change transformation is also reliant on the evolution of leadership within organizations, or as you said, the origin of leadership styles and organizational structures came from army and religious entities. And unfortunately they’re still in practice.
[00:19:49] They’re still in use. And this is why that all has to change.
[00:19:53] Alessandro Lanteri: [00:19:53] Yeah. I don’t know if this makes me more sad or it makes me laugh more, [00:20:00] but you’re absolutely right. I think that there are many organizations, including the state, including the most governmental structures, even in this diamond age are managed and run according to principles that.
[00:20:12] Probably emerged. Uh, we’re probably refined last in, you know, after the second world war, when the word was actually much, much simpler and easier to navigate, there is an expectation that you could, um, have a hierarchy where the person on Dettol. Eventually determines decisions and strategies where, whereas as you correctly said, this is no longer possible.
[00:20:38] So, but leadership is, uh, is in fact changing and, uh, tricking down and permeating organizations in various ways. Uh, one of the examples I find the most fascinating in this, in this respect is the example of, of, uh, what we call the dowels or decentralized autonomous organizations. For those who are not very [00:21:00] familiar with this, if you’re familiar with, for example, with Bitcoin and blockchain based solutions, one of the interesting elements among others, but one of them is worth pointing out.
[00:21:13] That these are organizations which affect lives and they have economic impact. They do not have a CEO. They do not have shareholders. They don’t have a board meeting. So they’re all run on consensus, probably democratic style. Although the democratic level there might change and protocols and rules that are agreed upon at the beginning.
[00:21:39] This is a very, very different way of running a complex organization.
[00:21:42] Aidan McCullen: [00:21:42] Yeah. I love that. And I was researching, I was telling you, I’m writing my own book and I was researching about Amazon and I was watching this video with Jeff Bezos and he was explaining how his leadership style has evolved. And he was like, sure, it’s evolved because the company’s evolved.
[00:21:58] And he said, [00:22:00] now I’ve arranged my work day. And my work practices in such a way that I work two to three years in the future. And if I’m drawn into the presence, something’s wrong because I need to delegate so much and give my team’s autonomy to such an extent that they can make decisions certainly on where to go.
[00:22:21] But certainly not to say how to do it as well. I know that that is one of the big problems within organizations is that. Leadership feel they need to micromanage every decision. And that in turn is driven by quarterly earning calls and then pressure from the board and pressure from stakeholders and shareholders.
[00:22:41] The whole system needs a new lick of paint. It needs to all change in order for the structure of companies to change.
[00:22:49] Alessandro Lanteri: [00:22:49] I love this example. It also reminds me of another notion of these future ready companies and other characteristics is that they, they are always in [00:23:00] beta. They’re never, never finished. Uh, not only the products keep evolving, but organizations themselves and the way they address problems and the way they keep refining and fine tuning their business model, the assumptions and so on.
[00:23:15] And when we come back to this point later, because it relates to the last letter in the, in the clever framework, it’s a responsive decision making, but I think that. You pointed it out very, very clearly here.
[00:23:26] Aidan McCullen: [00:23:26] Let’s get to this level of framework because we’re running out of time. We won’t even have spoken about it.
[00:23:31] So let’s start with the C of the clever framework and the storytelling of Teslas wo. When the company launched its first mass market car, the model three and use an extremely advanced automated assembly line to fulfill their orders. Everybody thought they were geniuses. All of a sudden they discovered actually the human has some value here.
[00:23:52] Alessandro Lanteri: [00:23:52] Yes. It’s a, it’s funny because when you, when you see the, the, the photos of the factory floor [00:24:00] Tesla, one of the things is trying to use the fact that there’s no one. These are huge setups of machines that do a lot of nice things and very efficiently, um, when there’s no people. And when they tried that, uh, unfortunately there are some things that machines really do super well.
[00:24:17] You know, they’re very consistent and precise and repetitive in certain tasks, but when it comes to the last stage of assembling a car that requires pretty much putting together tens of thousands of different parts and pieces. And so many things can be one inch off or they can be, you know, a few seconds delayed and robots are no longer as efficient as they could be.
[00:24:43] And that’s the stage, the stage where a human should step in and a human has this responsiveness and these ability to. Troubleshoot and problem solve on the go. And so what’s, what’s eventually, uh, was, um, was forced to do, is to bring [00:25:00] humans back in the picture. And there is this very famous, uh, tweet of his saying humans are underrated even in this time and age.
[00:25:10] So I think what that teaches us is that. We are increasingly accepting and realizing that humans are really extraordinary at doing certain things and machines slash robots are incredibly talented and are excellent at doing other things. And so the first layer of the first criteria in here, the first concept is that there should be some sort of a division of labor between humans and machines.
[00:25:40] But in the book. So the first letter is refers to collaborative intelligence and, and for me that means a bit more. So it’s not only the division of labor, it’s a, the combination of humans and machines into a system that can achieve something that couldn’t be [00:26:00] achieved separately. So what, what are those scenario?
[00:26:03] The situations where you must give machines some sort of superpowers. And where are the situations where machines give humans superpowers so that humans and machines together can, you know, the show, what I call collaborative intelligence
[00:26:19] Aidan McCullen: [00:26:19] when it comes to humans, collaborating and effectively, I love how you said water is made up of hydrogen and oxygen, but understanding all the properties of the elements of hydrogen and oxygen separately, won’t help you fully understand the properties of water itself.
[00:26:35] When two hydrogen atoms and one oxygen atom are combined, that becomes something else entirely. And this is the impact of effective collaborative intelligence. I love that.
[00:26:45] Alessandro Lanteri: [00:26:45] It’s an a, it’s an example that I, based back to my, my days in graduate school during my PhD either age. Uh, so that was a long time ago, but it stuck with me.
[00:26:57] I think it’s very powerful and you can even [00:27:00] bring into, you know, push it further and say, okay, if you combine different. Other chemicals, then you can have, for example, you can have proteins and if you combine proteins, then you have, uh, uh, muscles and then you put muscles, you have creatures and yeah.
[00:27:15] Society is, and, and they all, at the end of the day, 60% of human society is made of hydrogen and oxygen, uh, because we are made up of water and yet you can’t explain anything at that level. So this is what, this is the concept of emergent properties. So when you combine two elements or more, Together. They become something new and something different.
[00:27:41] And I think that humans and machines are actually going precisely in that direction. And we don’t understand them yet. I don’t think that we have the sophistication to understand that. Well, one of the big challenges is that as you were saying earlier, the organizations where we work are still managed based on the knowledge we have.
[00:28:00] [00:28:00] So you can imagine jobs, job descriptions, job assessment. These are our tasks performed by HR. An HR doesn’t necessarily understand the enough to redesign.
[00:28:12] Aidan McCullen: [00:28:12] I was thinking of that emergent properties thing. And I remember there’s an artist. I love guy called Olafur Arnolds and he’s a pianist. And he is also part of the bond cosmos and he and a friend developed this algorithm and the algorithm picks up when he plays a note.
[00:28:32] And then the algorithm is actually into other autonomous pianos, if you want to call them that. And the reason he says he does it is because when he plays a note in music college, You’re taught that this note goes with that note. It forms a paradigm for how you think, but when you use it with the algorithm, the algorithm will bring them in a totally different direction that he never had thought of before.
[00:28:57] And I thought it was such a beautiful way to think [00:29:00] of. The utopian view of where symbiosis in a way with computers or collaborative intelligence can bring us to somewhere totally emergent, totally new on somewhere where it freezes up to the more collaborative thinking, more strategic thinking, solving bigger, more complex problems
[00:29:18] Alessandro Lanteri: [00:29:18] I find is these examples of excellent.
[00:29:20] I want to check him out now.
[00:29:24] Aidan McCullen: [00:29:24] I’m his manager.
[00:29:30] Alessandro Lanteri: [00:29:30] You could see that in a different way, in a different form, but there was this incredibly influential and very important example of AlphaGo. Uh, playing, uh, developing an algorithm that plays this ancient game of strategy, uh, go, uh, where humans have dominated forever because it was too complicated to be replicated by, to be played by machines, but [00:30:00] a deep mind developed set of algorithms and AlphaGo learn how to play.
[00:30:05] In a way that’s not only better than the way humans play, it’s a effectively different, so lease it all the time. In 2016, if I remember correctly, the reigning champion of golf, uh, Play this incredibly important game with the algorithm and he lost, but, uh, after losing, he also commented that the way the machine approaches the game will go.
[00:30:34] And some of the decision it makes are qualitatively different from the way yawns makes the same similar decisions. And I, I find this also inspirational and fascinating. Um, there are a few more examples and other one I really like is, is what they call a freestyle chess tournament, where you could play as a combination of [00:31:00] humans and machines sort of software.
[00:31:04] So you could play in a team as a team and it could be one or two humans using software. Together to play against someone else. And it turns out that in those a freestyler tournament, it’s not the best computer betweens, and it’s not the greatest crime master that we use. It’s the team that collaborates the best.
[00:31:28] So you have the machines. Punching the numbers, so to speak, uh, evaluating all possible moves and strategies and the humans bringing in their own creativity and their own perspective and strategy and the combination of the two dominates. And I, I love that.
[00:31:46] Aidan McCullen: [00:31:46] Yeah. And I love another thing you talk about, which is kind of the idea of.
[00:31:50] Technology as an assistant or a peer on you say, as an assistant, it helps humans perform their tasks. But as a peer, it does the [00:32:00] work of an employee with a human only intervening to solve more difficult issues. And you give the example of a traditional delivery warehouse versus an Amazon one.
[00:32:09] Alessandro Lanteri: [00:32:09] I sell more of these videos online, available, showing the.
[00:32:13] The way Amazon manages its warehouses, uh, using a robot called Kiva that, which they acquired a few years back. So it’s very interesting because instead of sending humans around the warehouse to pick up the items, to assemble a package for shipping, uh, it’s actually the robot that brings the shells to the human so that the human doesn’t make a mistake, uh, effectively just fix the item.
[00:32:42] From a shelf and puts it in a box. Uh, I have a little anecdote to something that’s happened to me a few years back. And at the time I was completely shocked and I had no idea what it meant and now I understand it and I find even more fascinating. It doesn’t happen very often, but I received the wrong [00:33:00] shipment from, uh, from Amazon.
[00:33:01] It might’ve been that one time. It was a one book. So I sent an email tool to them to mention that I received the wrong book and they returned to me and they asked me only one question. And the question was. What is the weight of the book. Wow. And I was with specialist and I had no clue what’s going on.
[00:33:22] And then later I figured I’ve learned that after a ship, a package is ready, uh, there is quality control and even traditional human led organization, quality control is made by a human who opens the box and checks, making sure that everything is there. But what Amazon was it weights each package. And they determine whether the weight of the package is within a range.
[00:33:47] So if it’s in the right range, the package is approved and it’s shipped out. And so. When they shipped me the wrong girl, the wrong book, the problem was that the book they sent me at the same weight as the book I [00:34:00] ordered. And that’s why the quality control phase is quite unexpected. So it is indeed interesting that you also machines use different skills to perform the same tasks.
[00:34:11] And you could really have someone else part of your job
[00:34:15] Aidan McCullen: [00:34:15] when you’re told about this later on the book that actually frees up cognitive capacity, which is so key in the world of so much data, so many decisions, et cetera, going back to what Jeff Bezos was saying about. Actually, if he doesn’t have to think about the present, it gives him more scope to think about the future.
[00:34:31] But I want to talk about one thing cause you, you really highlight this is going to be a huge leadership challenge in the future. And I wanted to let you know, we had Charles handy, the grace business thinker, great thought leader on the show earlier on in this year. And he highlighted the difference between leadership and management.
[00:34:49] And he said that leaders lead people, what managers manage things, processes, et cetera. On in your book, you see machines as managers, which leads to a dilemma. [00:35:00] There is no notion of leadership or team management that trains as how to coordinate the human on the machine, which is the dilemma that you talk about a huge leadership dilemma, because that is a total paradigm shift for the future.
[00:35:13] Alessandro Lanteri: [00:35:13] Yes. I think that this is incredibly difficult to grasp and to manage you didn’t even need a very smart machine to manage a human. You know, there are some systems use for call centers, which had. Uh, call center operators deal with, with customers, especially when they speak English to each other, but English is not the native language of either of those of them.
[00:35:40] So it’s difficult to capture the new answers of a frustrated customer and how he’s feeling when he’s complaining and decide what’s the right answer. So there are some of the softwares available now that analyze the. The voice, the Sanford, the frequency, the voice, and [00:36:00] they interpreted and they determine the emotional state of the client complaining.
[00:36:06] And so based on that, they recommend to the operator and number of sentences that he could try and say, To manage the situation and it’s, uh, it’s obvious. I mean, there’s a selection where there’s two or three options. And so the operator reads one of those, but at the same time machines learn and the next time a similar situation presents itself when she will no longer recommended three options, you’ve been on to recommend two, because one is obviously inferior.
[00:36:36] And then the next thing you know, is the machine will only recommend one. And then when that happens, it’s almost as if the human is the assistant of the machine, the machine makes decision and tells you what you have to say. And then the machines are increasingly used to, uh, assess performance too.
[00:36:57] They’re also used for recruitment purposes. [00:37:00] We have a remote, uh, interviews performed by AI these days. So I believe that this is a huge change and combined with the notion of collaborative intelligence we were discussing. How do you put together? Uh, two, two coworkers, which such different, uh, skillsets.
[00:37:20] The machines and the humans who also don’t communicate naturally to each other. Who’s your boss tells you to do something or gives you feedback. You may still have a discussion. You can disagree, you can, uh, learn from him. But when a machine tells you what to do or says your performance is three out of five, it’s very, very difficult to receive that feedback or to receive that order.
[00:37:44] We’ve all been able to learn from it or. Respond to it. So it’s at the human level is a, is going to be very, very difficult, I think. And I, at the leadership it’s even [00:38:00] harder
[00:38:00] Aidan McCullen: [00:38:00] and it’s going to take huge amounts of educating and re-educating and forgetting, you talked about song costs before. Sinking the cost of the education we’ve had today and the way we’ve been trained and the way we’ve got to the roles that we’re in, we have to all wipe that out and forget about it and rebuild for the future.
[00:38:18] And that’s the most difficult thing. Isn’t it letting go of the way we’ve learned? I’m actually learning is the next in that level of framework. So we’re only on two. We won’t get through them all, but we’ll maybe speed up a little bit. Al of the level framework stands for learning systems. And here you say you don’t like to talk about artificial intelligence.
[00:38:41] You prefer learning systems, and there are three major approaches to learning systems, supervised learning, unsupervised learning, and reinforcement learning.
[00:38:51] Alessandro Lanteri: [00:38:51] Yes. I, I, I don’t like the word intelligence because I truly believe that the way we understand intelligence intelligence is a [00:39:00] human notion. It has to do with the way humans do.
[00:39:04] And when we use the word intelligence to talk about something that is different from humans, I think that’s. Most many of us have a sort of an emotional response and that scares us and leads us in the wrong direction. Um, there’s a, there’s a great book I read when I was educating myself in this field, the, a beautiful example, uh, about airplanes.
[00:39:29] So we’re not anymore during COVID, but we’re all used to traveling by plane regularly. And no one is worried that the plane might. Swallow us, but if we called a plane, instead of calling you to play, if we called it like a mechanical bird, All the emotional association with the notion of a bird would transform our relationship with the item.
[00:39:55] You could think, okay, you enter inside the bird. What if, what if birds become more [00:40:00] common? What, what are they going to do? What if they become autonomous? What if they decide to do things that they can’t decide? Uh, it’s just know the metaphorical association that we have in our minds association, associational thoughts that we have in our mind that.
[00:40:16] Lead us to believe that artificial intelligence has certainly of possibilities and capabilities, that it doesn’t have, what it does have. As you mentioned, it has some very effective ways of learning and by learning what we mean is that. And this is true for humans, as well as for machines. It means changing your behavior as in, in response to a, uh, to an experience.
[00:40:42] So you do something and then as a result of that, you change your behavior and is what machine learning does. So in supervised learning, the way it works is that humans. Tell machines, how to classify information. [00:41:00] Probably the most intuitive example there would be. You know, when, when you tag a person on a social media, uh, in a photo, you upload the photo and you say, this is our Sandra.
[00:41:11] This is eight. Look what you’re doing, you’re at your training inaugurate and you’re supervising the learning of this algorithm. You’re telling the algorithm that whenever they see an image with those properties doors, the characteristics, that’s how the sun and over a number of repetitions of the staggering and the supervision supervised learning.
[00:41:32] The algorithm learns to autonomously, associate the characteristics of those photos and what all those photos have in common with my name. But of course it doesn’t know who I am. It doesn’t even know that I’m a person, you know, they don’t have a notion of a person. So that’s why I don’t like to call them intelligent.
[00:41:52] Some of the things they do, however, are incredibly fascinating when it comes to unsupervised learning. For example, [00:42:00] we’re actually usually asking the algorithm to figure out for it on its own well different, uh, that the sets have in common. I’m going to use an example here, because I think it makes it a little easier to understand.
[00:42:15] So instead of telling the machine what to look for, we let it do it on its own. And this is used for example, in marketing. Uh, one of the obligations of the technology is. Identifying users or customers that have certain things in common in a way that humans can’t do it. So there is a, this example from a, from a Chinese FinTech company, it’s part of the Alibaba group and it’s called ant financial.
[00:42:45] So they, they have a payment and mobile payment service, and these payments are very common in China. So they have data from millions of transactions performed every day. And one of the, and they also have insurance [00:43:00] companies. They have a number of companies associated to all under the financial umbrella.
[00:43:05] But what happened there that I found fascinating is that at some point the algorithm to figure it out the door, certain users who spent more money than other users, To get the Mo the screen of their mobile phone fixed. And those same users also spent more money for items that are classified as skinny jeans.
[00:43:28] So when you combine these two information, you could find out that customers who wear skinny jeans break their phones more frequently. And so they get it fixed more frequently. Well, it’s not something that humans can do. So add financial loans, then insurance policy targeted exclusively at girls, whereas Kenny G’s and it’s an insurance policy from your mobile phone screen.
[00:43:52] Aidan McCullen: [00:43:52] I love that, man. That’s brilliant. It reminds me of, we had this great guest on the show before AK per deep, and his book is called [00:44:00] AI for product development and marketing. Okay. He told us on the show that. Again, AI did this, it spotted patterns in people talking about ice cream at breakfast time. So again, kind of like the Oliver Arnold’s idea of the emergent solution that came and exactly like this idea of the, the skinny jeans on the phone insurance.
[00:44:25] They came up with this idea to use the milk from breakfast cereal to make ice cream. And it became an absolute hit product. And it’s that
[00:44:34] Alessandro Lanteri: [00:44:34] exact thing that’s
[00:44:35] Aidan McCullen: [00:44:35] symbiosis of thinking of human and machine that can bring us in these brilliant places. If we. Restructure recalibrate the world around it to enable it.
[00:44:47] Alessandro Lanteri: [00:44:47] I love this example.
[00:44:50] Aidan McCullen: [00:44:50] If you put them all together and then sell that ice cream to skinny jeans on the phones, they’ll be like AI, AI, human triumph. Let’s move on [00:45:00] quickly. Cause we’re, we’re not going to get through everything in depth. But speaking of moving on quickly, excuse the point. We’ll move to exponential growth.
[00:45:07] And here you give us a brief explanation of the first E of the type of framework, which is exponential growth, because I’d love within here. What you do is you go a bit deeper into the breakdown of exponential technologies into the six stages of development that Ray court’s fall introduced.
[00:45:25] Digitalization, deceptive growth, disruptive growth, dematerialization demonetization and democratization. Would it be great to explore? As you do in the book through the lens of Kodak. I love the way that this in the book
[00:45:40] Alessandro Lanteri: [00:45:40] is a sad story in a way as a cautionary tale. I chose this for the book because it’s fairly well known and I didn’t want this to sound too, uh, exotic to most of my readers.
[00:45:53] But what happened with we’d call doc is that had an engineer who developed. [00:46:00] And patented the technology of digital photography. And when that happened, it was the mid seventies. Kodak had a 90% market share offer analogic camera, which film a film or photography in the U S so they had 90% market share.
[00:46:17] So there were. Effectively monopolist. They were the largest operator in the industry. They had lots of resources, great brand recognition, and obviously superior R and D capabilities because they developed this technology. And yet they didn’t believe in it. Enough to, to transition, to, to eat as a new, as a new system.
[00:46:40] And there’s a few reasons for this, of course, one of the reasons that they didn’t want to cannibalize their existing business, and this is something we’ve learned. And, um, I think that nowadays, most companies. Are no longer worried about cannibalizing, their sales? No, you have Apple releases, an iPhone and new iPhone every, every [00:47:00] year and more than one reading.
[00:47:02] And obviously then you can embolize the sales. So the previous iPhone, that’s still pretty good. Mmm. The second thing is that what, and this is really the core of this chapter. What Coda couldn’t see is how rapidly the technology was evolving and how rapidly it was going to be viable as a solution and are rapidly there for the market, for the image around it.
[00:47:27] You could even find the, you know, some of the minutes of the board meetings. At the time. And it’s obvious that the decision makers cold duck had a prediction that the technology would only become viable many years later. And what happened is that, is that it became viable by. Let’s say the early nineties, mid nineties.
[00:47:49] So digital photography in the mid nineties, uh, got to a resolution of about one megapixel, which is what was good enough for most, uh, most [00:48:00] consumers. That’s probably also the time, maybe mid nineties, early 2000. That’s when everybody had their own digital camera for the first time. The point in time, uh, the film technology was doomed.
[00:48:14] Because the film technology can keep getting better and better, but as a much slower pace, slower pace. Whereas these exponential technologies really get better, super fast, and it’s difficult to grasp how fast, uh, one of the ways I look at this is if you think about your phone, I mean, I’m sure everyone listening to the show has a smartphone and I’m sure that they have.
[00:48:41] Probably a very good camera, maybe something around 16 megapixels or so, which was the best technology available three, four years ago. But then two years ago, the new generation came out and the new generation is close to 40 to 48 megapixels. And that [00:49:00] sounds incredible. But last year, the new generation came out and that’s 108 megapixels.
[00:49:06] So some lessons to be learned here, the first is that each new iteration that the new generation technologies are coming out really ready fast. Well, the other thing is that each new generation has an improvement. Larger than all of the previously accumulated improvements. So we went from zero one megapixel to 14 megapixels in 25 years.
[00:49:36] And then in one year we went from 40 to 108. So that improvement is bigger than everything you’ve ever seen. And that is a huge challenge for decision makers because this technology has become so much better, so fast, but you have to leaving the future, literally to understand what can be done with that.
[00:49:58] And how cheap [00:50:00] they become, how fast
[00:50:01] Aidan McCullen: [00:50:01] it’s actually that deceptive growth that catches us all off guard individually as well. So people, all of a sudden, you know, may have been thinking for a long time about changing their job. And all of a sudden, some shift in the environment comes where they are made redundant.
[00:50:15] And they’re like, Oh, I should have made that decision when I was in charge of it. And this is what I like about your book. You’re giving people information in a clear framework in order for them to make really good strategic decisions. And it does help individuals who work in organizations as well. And before I move on, I would just want to thank our sponsors, which is Microsoft for startups, for all their help and support today, I was thinking about this idea of exponential growth Alessandro.
[00:50:42] And I saw this and you probably do as well. Alexa, Cortana and Siri, and these voice assistants where most people try it and see it as a kind of a trivial thing for children to play around with, or maybe something to use for simplify, like [00:51:00] turn on a plug or a light or the radio or play Spotify, whatever it might be.
[00:51:07] Alessandro Lanteri: [00:51:07] They dismiss it quickly
[00:51:08] Aidan McCullen: [00:51:08] as being not very effective or not very advanced, but meanwhile, it’s actually advancing at an exponential rate in the background and all of a sudden it re-emerges. And one of those technologies that you pointed out in the book that went through a similar cycle that, because we heard all the hype about this before in the past was three D printing.
[00:51:30] That this is this thing, this great thing. And some people will have bought them little three D printers at home, but again, see them as kind of trivial toys or kids’ toys or novelties in some way. What three D printing. Is going to have a massive effect on the world of production and industry and business in every aspect, because it totally interrupts the supply chain and moves us towards more needing platforms than actually providing physical products [00:52:00] themselves.
[00:52:00] Alessandro Lanteri: [00:52:00] It is a great example of what we call as a category like immersive technology. You mentioned this earlier, so these aren’t technologies where we. Interact with them in a natural way for humans to do so. So instead of having to learn how to type certain commands on it on a keyboard, as we used to do in the eighties, uh, instead of, you know, knowing when we use a computer, we, we drag a mouse, we click on an icon, which is more intuitive, but we still have to adapt our behavior to the user interface.
[00:52:34] Of computers, what, uh, for example, uh, augmented and virtual reality is doing, and what is conversational technologies are doing? They’re there, they are learning how to communicate. Yeah. One way in a more human way. I think this is a huge change. Uh, one of the things that you already highlighted earlier is that it decreases the cognitive effort for humans to use the information.
[00:52:59] So [00:53:00] imagine when I do a search on Google, you have to choose a number of things and you have to read instructions, and then you have to act on that. Instead of with these conversational platforms, you can just tell them what to do. It’s faster, it’s more spontaneous. And it, it comes naturally to many of us.
[00:53:17] And when it comes to truly bring thing, uh, I don’t know how much it’s going to change the word economy as we know it, but I think it’s going to be really dramatic. Um, I’m not going to say the name, but I I’ve done some work with a very, very large money for producer of high quality aluminum. So they make the aluminum we have in a, in premium cars in the airplanes.
[00:53:47] And one of the things that, uh, that, that seems to be problematic for a company like that is that three D printing will not only change the way [00:54:00] we, we, we manufactured parts, but it’s also the materials we use now. Alumina is a, is a great material because it’s cost effective. But there are materials like titanium, which are superior to aluminum in almost every way, but they’re too expensive.
[00:54:17] One of the characteristics of printer printing is that it almost eliminates waste. So in some parts, some, some parts can be print three D printed using as little as 15% of the raw material that’s that will be required with the traditional manufacturing techniques. So suddenly if you have an 85% saving on the raw material, You can probably transition to a new type of material.
[00:54:45] You can now use titanium because you need so little of it. So, so many things are going to be transformed and I completely agree with you. And then of course, logistics would change. We no longer need to manufacturing things and send them. You can just [00:55:00] print them closer to the point of views. And increasingly I believe that this is in line.
[00:55:08] What we’re saying from the very beginning, the value. Uh, value-creation will no longer be with the physical version of the item. It will be with the digital version of the item. So when you want something new, you pay for the file that lets you really print it. And the physical version of it, you can do a dozen times.
[00:55:29] It doesn’t matter anymore.
[00:55:30] Aidan McCullen: [00:55:30] So for example, I smash a Moke in my kitchen and I say, okay, I’ll go and print a new one, but. The volume may be in the file that I print so many to download a new file or a matching file. Or I thought about this actually that it could drive new business models. So for example, like an espresso machine, I buy the machine.
[00:55:52] It’s probably, you know, razorblades in model where the money is made on the capsules, not the machine itself. But I bought the three D [00:56:00] printer machine. I sign up for a subscription the way I do for my Netflix, that entitles me to an ongoing supply of printed materials. So I can print in 3d and then I might get the file for free.
[00:56:12] And the reason I mentioned that is because it brings us to the next part of the framework, which is V and. This is really important to understand how do I attribute value or how do I create value? Because the value creation is where the gold is.
[00:56:29] Alessandro Lanteri: [00:56:29] Yes. And this also moves us in the second part of the framework.
[00:56:32] So shows three things, we said C L N E. Are really about the characteristics of the technology and digital technology, particularly no viz is more about how we make decisions to create value. As you, as you said, it is made possible by digital technologies, but it’s independent from it. The example I use here usually to explain this concept is that.
[00:56:57] So you mentioned, you mentioned when you, when you, when you [00:57:00] rent an apartment on, on Airbnb, you give some money to the landlord and the landlord gives you access to his apartment. And for you as a, as a, as a tourist, as a, as a host, um, as a guest, um, the ability to use the apartment is worth more than the money you give.
[00:57:22] And to the landlord, it’s the opposite. That lender values the money more than the apartment. So in this exchange, you create value. There’s a bit of extra value for me because I have something that’s worth more to me than the same for the land. What’s interesting. However, is that Airbnb doesn’t really do any of it.
[00:57:41] They don’t have apartments, they don’t try and do apartments. What they do is they facilitate. All of those exchanges that actually create value this way. What I talk about is this approach value facilitation, rather than value creation. Many people talk about these as they’re [00:58:00] referred to these as platform models.
[00:58:02] I don’t have anything against the notion of a platform, but the word platform is used in other senses, to be honest that in, in the business. So I thought the value facilitation was clear. It’s interesting. Then if you look at it, as we said, Airbnb doesn’t have any real estate. They don’t have all the tangible, physical capital that’s for the being required.
[00:58:28] Normally two to become a large, a large company in this industry. You think about the largest on the planet would be Marriott. They have over 1 million rooms and their market capitalization is a fraction of Airbnb. Oh, well, Marriott has also a few billions of dollars worth of real estate. And Airbnb has probably now about 7 million rooms with no real estate.
[00:58:58] So what makes this business [00:59:00] model is so powerful is that they can grow really quickly. They require littler labor, litter, um, workforce, uh, because most of the value creation is done by your users interacting to each other.
[00:59:14] Aidan McCullen: [00:59:14] You mentioned Airbnb because they leverage users who create value for each other platforms imply fewer staff.
[00:59:20] And this is one of the big challenges of the future with technological unemployment is because. Some of the biggest companies in the world are platforms and platforms traditionally employ less staff and, and in a way higher skilled staff. And then if you bring in robots and automation, automation, they’re going to have fewer of those highly skilled people.
[00:59:42] And this causes a huge dilemma for the future, but I’m going to get back to this. So Airbnb has just over 3000 employees while Hilton has 169,000 and Marriott has 176,000. And among the 10 most valuable global brands, nine operate platforms. [01:00:00] And according to McKinsey, seven of the world’s 12 largest companies employ the model, Alibaba alphabet Google’s parent company, Amazon, Apple, Facebook, Microsoft, and 10 cent all operate platforms.
[01:00:14] So given the increasing power of platforms, traditional pipeline businesses must make a strategic decision on how to respond. And you give these examples in the book of how they can respond to there’s three main ways they can do so.
[01:00:28] Alessandro Lanteri: [01:00:28] Yes, indeed. Every traditional company, we call them the pipeline company because they’re, they operate like a pipe.
[01:00:33] You have the inputs at one end, it goes through the pipe. Value is created. Then it comes out at the other end, then you sell it. The first ones are more managed. They are, they, you have more users interacting with each other. So how do pipeline companies respond and react to the trap? The trade platforms?
[01:00:51] There are three, three ways, as you said. So the first one is to try and replicate some of the value propositions of the platforms. So one of the things that [01:01:00] we found out through the big success of Airbnb is that, uh, uh, tourists like to have, uh, the experience of the local when they travel. And the other thing is that they like to socialize with their hosts.
[01:01:15] And so someone tells what they’re doing. They’re developing what we call hotels models. You have a very small, tiny bedrooms where people go and they are cheaper, of course, because they’re small. Uh, but you have large socialization areas. Um, so in a way you would replicate the experience of meeting people as your travel.
[01:01:35] And so that’s the first response. The second option is to have a sort of a hybrid missus model where you can keep running your existing model. But then at the same time, you can make some of your rooms available. For example, through Airbnb, there are my take hotels and motels that are doing that. So they become more hybrid.
[01:01:55] And the third option is to try and become the platform yourself. [01:02:00] So to embrace this business model completely. I think one of the great examples here is the example of, uh, of John Deere. Uh, they traditionally manufactured the farming machinery and you can’t imagine, you can’t think of anything more pipeline than that, right?
[01:02:16] You have raw materials, labor, capital machines, you, you, you, you, you do your magic. And at the end you have a tractor. Well, they managed to do or whatever was doing store sensors on all of their, uh, machines and these assessors, let machines gather data and communicate beta. So they’re slowly transforming into a.
[01:02:41] Farming management system, where you can connect a farmer with, uh, uh, an agronomist. You can connect to the weather forecast information to the farmer on the ground. You can have all sorts of beta gathered across [01:03:00] the United States where you learn a lot of information from all sorts of sensors. Planting all sorts of different plants in different conditions and learning from it.
[01:03:11] Then you provide that advice to all of the actors on the platform. So at the end of junior year, here is probably the most advanced response you can see in this, in this domain. And it’s kind of surprising because as you said, there are these very large corporations running a platform business model. And yet I think if I remember the figures correctly, less than 3% of the words or the, of the companies on the planet are doing the same thing, and this is very surprising and I don’t think it can last very long.
[01:03:46] Aidan McCullen: [01:03:46] Unlike you said. Business models are not set in stone. And it reminded me of that. The reason I brought up Jeff Bezos saying that his leadership style evolved because the business evolve, so your [01:04:00] business model and they need to evolve as the company grows and external conditions change. Moreover, but the great example you give is Microsoft, LinkedIn, and how that business evolved.
[01:04:11] As the user need evolved as new market conditions evolved, et cetera. And they came up with total new products.
[01:04:18] Alessandro Lanteri: [01:04:18] Yes. LinkedIn started as a, as a platform where professionals could. Post their CVS, so to speak and they interact with each other. But as soon as you have a number of professionals meeting all in the same place, that place becomes incredibly attractive for recruitment.
[01:04:40] So companies looking to hire these professionals and that changed the, the nature of the, of the platform. So suddenly, uh, you had someone willing to pay in order to be part of it. And when it’s so many companies and professionals started interacting on that platform, [01:05:00] then there were additional service providers who are attracted to it.
[01:05:03] For example, a company selling a consulting services or training services. I LinkedIn is developing its own learning platform because it’s so. Incredibly incredible attractiveness of this synergy or the synergy of offering, uh, uh, training. And so in a way, LinkedIn is evolving both as a platform, but also as a pipeline business, so to speak.
[01:05:27] So. They create new services made possible by the platform.
[01:05:34] Aidan McCullen: [01:05:34] And again, having the mindset open to spot those opportunities is one thing. And then having the leadership that will back those ideas is a totally different one. We’re going to finish up with the last two very briefly. The last two letters of the lever framework, the is ethical championship and the, or then is responsive decision making.
[01:05:53] Maybe you give us a top line on both of those is Alessandra.
[01:05:56] Alessandro Lanteri: [01:05:56] Right. I’ll keep this very short, but you’re right. So ethics is [01:06:00] incredibly important for a number of reasons and the book explains in greater detail, but it’s a, it’s a driver for value creation. And here, I think about all the businesses with purpose, there is a great example of Unilever’s brand dove and a few more, the known is going in the same direction.
[01:06:19] So these are businesses that exist to. Bring about a positive change in the world besides making money. They’re still for profit, of course, but there is a huge response from the market and there is a huge demand in the market for this type of companies and they actually perform incredibly well. So this is interestingly enough, this is the chapter where I talk more about ethics and it’s the chapter with the greatest number of, of data.
[01:06:47] It’s full of numbers because I didn’t want my readers to think all of these is all the softer wishy washy, you know, all nice to hear, but it’s not real in business. And I wanted to back it up with [01:07:00] lots of numbers. Uh, responsible decision making is really about this idea that we need even a word is.
[01:07:06] Changing and evolving so rapidly that you need to constantly learn from the change, gets feedback and make decisions that are reactive. So respond to what you learned earlier today. Or yesterday that segway is a, is shutting down some ways. It’s a personal vehicle that was supposed to revolutionize the way people walk.
[01:07:33] Now it turns out most people don’t really want to change the way they walk, but it took many years for them to decide to fold. But what is interesting is that this company developed the product in complete secrecy. They spent, I think a hundred million dollars developing it. They were. Absolutely confidential for seven years, then they launched with a massive communication campaign without [01:08:00] ever really hearing from the users.
[01:08:03] Will they thought what users need it? Was it the right way to address the problem? And so on now we have a lot of techniques and methods to do this better to learn. Better, mostly coming from, uh, from the world of startups, because for startups, everything is new. They don’t know anything. And the more startups we have, the more we we learn.
[01:08:26] Well, we learn how to learn in a world where you don’t know anything. Traditional companies know something. Would they be what was successful in the past? Startups are much more. Learning grievance. But if the world around you changes as fast as our war is changing, even if you will operate a traditional company, you’ll have to start learning like a startup.
[01:08:47] And I think that this is, this is what the last chapter does for you. It starts giving you a set of messages around the importance of doing this. And if you will, examples of how to do that successfully. So that’s the last part of the framework,
[01:08:59] Aidan McCullen: [01:08:59] a lovely, [01:09:00] excuse the point here. This is terrible segue for me to say our sponsor is Microsoft for startups.
[01:09:05] So thanks to our sponsor, Microsoft for startups, if you had a parting message. So I mentioned at the start that you formulate this framework and you have that question that most organization leaders ask you what would be. Your one piece of advice. If you had only one opportunity to go, just do this, what would that be?
[01:09:25] Alessandro Lanteri: [01:09:25] The reason I wrote this book to answer that question is because I don’t have an answer. These are the forces between effect well to good dancer will be in the near future. So all I have to offer is understand the forces and then adapt them to your needs and your business and your business model. Uh, at the end of each chapter, there is a, there is a list with a few questions that readers can can see.
[01:09:53] What does each driver mean? To them and to their company. And I, I think that’s the most they could do to make it [01:10:00] relevant to every user. I don’t want to give advice because the world around us is changing so fast, but then I’m pointing in the direction where I think the readers should look to find an answer.
[01:10:12] Aidan McCullen: [01:10:12] Where can people find out more about you and your work Alesandro? Cause I’m,
[01:10:15] Alessandro Lanteri: [01:10:15] I’m fairly active on LinkedIn. I have a website dot com. So that’s a, a L E N a N T E R i.com. And my book is available on Amazon. If you want to find out a bit more about the book before, before buying it, I also have a few videos on YouTube illustrate the book in a, in a bit more detail.
[01:10:40] Well, I want to take a chance if you don’t mind to say one more thing, because you’ve been talking about the leadership challenges in this world that I described in the book and will prepare you in any way for this. But there’s one thing I want to tell you. So my plans for the future are to go exactly the direction of what you just [01:11:00] said.
[01:11:00] And I, around the leadership challenges more specifically, my plan is to transform clever the word itself. From being an acronym of the six drivers to being an objective. So my next book will actually be on clever leadership. Precisely because the problem is so important as you highlighted multiple times during our conversation,
[01:11:26] Aidan McCullen: [01:11:26] we look forward to reading that one when it comes out and cover it on the show author of clever the six strategic drivers for the fourth industrial revolution, Alessandra Lanteri.
[01:11:37] Thank you for joining us.
[01:11:39] Alessandro Lanteri: [01:11:39] This was a great, great conversation. Thank you very much for having me.