Excellent. Thank you. So a few quick words about, about myself before we get started, I'm a professor of management and entrepreneurship. I'm largely focusing on, on innovation and AI topics, actually at ism international school of management, which is a private practice driven business school with seven locations in Germany, I'm at the cologne campus. And there's another one also in, in Munich. So pretty close to where many of you are at the moment. And for joining ism some three years ago, I've been a top management consultant, a small boutique consulting company, focusing on digital AI and innovation topics. And I've managed so far cause I'm still collaborating with a number of companies over 25 projects, consulting projects about those topics with companies in different industries. I'm also regular speaker and also coaching executives and managers and various companies. And I've written several articles and books about those topics, including the one that you see here on the right hand side called integrated intelligence.
And today I would like to start, first of all, explaining integrated intelligence with an example, pretty unusual example, I guess then I'll explain a bit more in detail. The concept then also demonstrates some kind of maturity model of managing AI. And finally, I wrap it up with another example from, from Amazon, but I would like to start with an example from, from the football context, and this is a Liverpool football club. So even if you are not really interested in football, I think this story will also be interesting to you because Liverpool football club traditionally was the leading football club in England, but they didn't really have an, an excellent time in the past decade basically. And that dramatically changed from 2015 onwards and they really had a major revival in between 2015 and 2020. So the club won the at the European level, the champions league in 2019 and after 30 years, 30, very long years, also the English premier league, again in 2020.
And not only in terms of the results in, in football, but also in terms of the brand value and also the market value of the players. There was a very, very strong evolution of the club. Over those years. I could ask you right now, who's responsible for that major revival of the club. And I think many people would know that who drives the success that it's largely union club, the originally German manager of Liverpool football club, but this is only part of the story. So definitely he is important. However, there's another person called in Graham he's head of research at Liverpool football club. He has a PhD in physics from the university of Cambridge. So he is definitely not the football guy, but rather the data analytics and AI guy. And he leads the team, data analytics and AI team and Liverpool football club. And the club actually focused a lot earlier than many other football clubs on data analytics and AI.
And in particular, his team prepares the, the team of Liverpool for the next opponents. They also think about, okay, which, which players should we actually hire? So the support, the scouting team, et cetera. So they focused a lot on data analytics and AI actually. And that guy I Graham, and the head of that team, he says that you club, so the manager of Liverpool, he was always one of their dream hires. However, there was a problem. He was in an, at another German club, Bruce Doman before he joined Liverpool and days were performing surprisingly badly actually in the last year, before he joined then afterwards Liverpool in that season, Doman finished seventh on seventh place finally, but some very detailed data Analyst data analysis that, that Liverpool did that Ian Graham did showed that they had very extremely unlucky throughout that season. And actually they should have finished second in the German league at that time.
So many people started heavily criticizing during club with his strong focus on emotional intelligence. He's typically considered to be one of the most empathetic and emotional football managers, but many people started heavily criticizing him, but Liverpool did extensive data analytics with 10 years of data of the German football league that they analyzed to show that he's still an excellent manager and in the end they were perfectly right. And therefore you club also had an interview in 2019. They saw the data analytics team are actually the reason that he had all came to Liverpool in the end. So the Liverpool football club is an excellent example with human club, his focus on emotional particular kind of human intelligence and the club's focus on data analytics and artificial intelligence. That's an excellent and unusual example for integrated intelligence. So what is integrated intelligence all about? It's about combining human and artificial intelligence.
So human intelligence or particularly human skills expertise, any kind of human capabilities on the one hand and on the other hand, any kind of artificial intelligence, data analytics, machine learning tools, et cetera. So we've just heard in the, in this session previously, the different definitions, but that's not the focus here when we talk about artificial intelligence and based on different potential for combining AI and human expertise, we can, can identify four different settings basically. So if we talk about a situation where neither artificial intelligence nor human intelligence really plays a role, I call that isolated ignorance. So here we talk about some of the kind of standard automated routine procedures like robotics in the manufacturing process of the automotive industry, for example. So that might have been considered. Those robotic applications might have been considered some kind of artificial intelligence some decades ago, but this is very standardized, pure automation today.
Nobody would really consider that to be artificial intelligence anymore. And then there is a second part, a second situation. And here the focus really in setting is on human intelligence. So here we talk about superiority. So there are definitely some unique skills of humans, at least for the foreseeable future. And this is exactly for example, the focus on here with, with empathy, with many emotions, but also with, for example, if a football manager is doing some kind of pep talk in during the halftime, these are all topics that you would say even as if AI is strongly improving over the next few years here, here, humans will probably be superior in the foreseeable future. Another example, and another setting, however, will be a focus on artificial intelligence where you don't need particular human skills, but here, the focus is on artificial intelligence, actually substituting human Burke.
So we talk about automation of activities that could not be automated in the past, but that now may be automated actually because of the advances in AI. And if you bring that two to those two together, a strong focus on AI and strong, strong focus on particular human intelligence, that would be what integrated intelligence is all about. So that's some kind of synthesis and there's some complementary because there are interdependencies between AI and particular human skills. And this is exactly what happens at Liverpool. So in Grant's team and analytics team, they are only say one source of information, but of course you can club and his, his managing team, they also get information from more traditional sources. And in the end, it's kind of a mix of very data driven AI based analytics based strategy, and also with more emotional and human based skills and competencies from a conceptual point of view, we can distinguish three specific types of interdependencies.
The first type are pooled interdependencies. So that's a collaboration that the computer, the machine, the AI is taking over a certain task. And at the same time, humans are doing some tasks. So think about risk management in many investment funds and agencies and banks. And they partly have settings where the AI is doing a risk analysis and human and humans are doing a risk analysis and then they pool the results and then continue to work with these pooled results that will be pulled into dependencies. Sequential interdependencies will be situations where you have first, the AI or the computer or the machine, and, and then some human input or vice versa. So very easy example in that regard are call centers where we would call and then there's first machine answering actually for some relatively easy interactions. And if the machine, if the AI thinks, okay, you cannot handle that situation anymore.
You are then further processed to a human employee. And then finally the most complex type of interdependency are reciprocal interdependencies in here. We would talk about multiple steps actually going back and forth between the machine and their computer. So for example, in resource planning, processes and companies, sometimes human employees set some specifications, then they do some analysis based on AI technology and machine learning. Then the results are checked again by humans and then another round of planning stars. And then would be some examples for these recipe program, more complex types of interdependencies. The challenge is that most companies and many companies that that I've seen in the past years, they largely focus on relatively isolated applications of artificial intelligence. So the focus strongly is on the right lower part of that matrix. So on the substitute field. So we talk about automation rather than augmentation.
So it's rather automating relatively easy human tasks and not really integrating, not really augmenting human intelligence. And I think the challenge really is for many companies, is that the partly successfully apply this kind of substitute focus of AI and they profit from it. And they think we are really leading in terms of using AI. And this is partly right, but not actually fully, right, because they completely neglect the opportunities, but also the challenges, but in particular, the opportunities for innovation and further growth by integrating isolated AI applications and this advanced automation with particular human based core competencies that they already have. And the problem with this substitute approach. So advanced automation only is that many competitors of those companies in many different industries, including automotive, including machinery companies, electronics, but also service based businesses. The problem here is that many of these applications have been very specific applications some years ago, but they become more and more standardized.
And the challenge then for established companies that use AI so far and think that they are really good in doing that, the challenge they have is that their competitors will use just exactly the same or nearly the same, or even better solutions in the very near future. So how can firms really achieve a sustainable competitive advantage, not only for a few months, but really something that they say, this is an AI based core competence in the future. And that's a major challenge at the moment for companies, but also for the customers. And that should be the ideal situation, but this is something that often is not really not even strategically addressed in many companies. So the challenge actually is how do we get from this kind of ignorance or at least the automation focus more towards integrated intelligence. And here I distinguish five different maturity levels of how firms manage AI.
And I think this also is, is very consistent with what we've heard from Fabre and to be us in early on in this particular session. So if you talk about the, I call it level zero, actually actually of the, in the maturity model, this is isolated ignorance. So it's either complete ignorance of, of the relevance of AI or the companies simply deliberately strategically focus on, on different topics. And then there may be the first real left maturity level of managing AI. And this is what I call initial intent. So companies start experimenting with selected AI technologies. They check whether this is really feasible and implemented. In some degree in the company, the second maturity level would be an independent initiative. So the companies really have ongoing initiatives there. Typically they focus on advanced automation. So it's not really integrated, but is advanced automation clear focus on enhancing efficiency.
Innovation doesn't really play a role here. The next level will be interactive implementation. So here the companies focus, those companies that are even a bit more advanced, they focus quite strongly on different kind of AI solutions. Sometimes they also profit from pooled interdependencies, and they really try to professionally implement these solutions and coordinate different multiple activities throughout the organization. And then this is the level three is the level that many companies that use AI and that successfully use AI actually achieve. But then there is a major next step and that would be maturity level four, which is interdependent innovation. So the challenge here would be to go beyond this pure automation focus, this pure application of relatively standardized AI solutions to really leverage AI for innovation beyond near efficiency and automation. And here we would rather than talk also about sequential interdependencies and how to, or orchestrate multiple solutions for completely new applications.
And that's a level that actually many companies, even those that consider themselves to be leaders in terms of AI do not really achieve. And then the next level beyond that would be integrated intelligence or continuous renewal recombination and with all types of interdependencies for achieving completely novel solutions. So that will be possible today. And then we can still go beyond one step and that will be looking into the future. What is possible if technology further advances in the future, we could talk about intuitive ingenuity. So that might be some kind of shared management of human intelligence and AI, some systems with a, with at least some level of self awareness and consciousness that might be a scenario for the future. And then of course the impact and role of AI would further increase. Actually, there's also a paper that I've written, which is the source for, for this maturity model.
You can somehow see it here in the, in the source below. Otherwise just contact me on LinkedIn or write me an email it's, it's openly available in the journal of innovation management, maturity levels of managing AI. How do companies actually get to the next maturity level? So if you are currently in a company that does not do AI at all, of course, then you need to activate the organization. If you have some kind of initial activities you need to arrange and really set up an initiative to get to level two, then you need to get things done, actually to get to level three. There is often some kind of, of challenge here in many firms. So level one and two, okay. Check and level three, a bit more challenging. And then often as I already said, and getting more towards an innovation focus in using and leveraging AI, this is something that often does not happen, unfortunately doesn't happen.
And then the next level would be really to integrate AI with human skills and expertise. And that would be then the next level, what we see, however, is that the companies on the first three levels, they actually, especially on the third level, they are actually celebrating in many cases and applauding themselves for saying and saying, well, we are actually AI leaders, at least in our industry, maybe true to some degree, but the problem is they would need to go one step further towards innovation and integrated intelligence to be really outstanding in terms of leveraging AI. And I have one final example in that regard for a company that is really outstanding in terms of leveraging AI in many different ways. Also in, in some previous part of the session, you talked about Amazon, Amazon was also considered the world's most innovative company in a meter ranking of different kind of innovative innovation rankings of the world's top innovators that are published sometime ago.
So it's considered to be the most innovative company worldwide, specifically for its focus on AI and digital innovation. There are a couple of other tech companies, maybe not surprisingly here, very high on that list, but Amazon is leading. And one very interesting example in terms of how Amazon leverages AI is a pattern from Amazon that you see here about anticipatory shipping. So the idea is that Amazon may already start to ship a product that you've bought on Amazon to you, even though you have not actually clicked the buy button yet. So Amazon may anticipate that you will probably buy the product and may already send this product not directly to you, but at least to logistic center close by. So that Amazon is then able to actually deliver it to your home within just one or two hours. So, and Amazon, Amazon anticipates that you will probably buy the product maybe because some, two weeks ago you look for that product on Amazon.
Maybe you then checked for some price comparison website to ask some friends and family about it, checked on Amazon again, and then talked with your partner about it, checked it again. And then Amazon may know that maybe tomorrow you will probably hit the buy button. And then Amazon has already the, the product quite, quite close to you to de deliver it very quickly. Amazon did not stop in developing that technology, but they also thought and linked that technology and this AI predictive analytics technology, they linked it to the business model and to some business model innovation. And it may allow a transformation of their business model based on human intelligence, human competencies, human ideas that they had to transform the business model from shop and ship. So you would click the buy button and then something is sent to you towards ship and shop. So something's already sent in your direction and then you actually hit the buy button.
And that shows perfectly how Amazon thinks about AI and how AI may help to actually further strengthen the business model of Amazon. And this is exactly the perspective that Jeff Bezos, the founder of Amazon already had on that topic quite some time ago. And most interestingly, if you think, well, this anticipatory shipping technology is not really that relevant and maybe this is still future focused, it's already from 2013. So that gives you some idea how Amazon, how advanced Amazon thinking about those topics already while was in 2013. And you may also get some idea and imagine how it is at the moment, actually looking into the future. And so Jeff Bezos never got his very positive view of AI. And he basically also says that the most exciting thing is to make these techniques accessible to different other organizations. And I think that should really ring in your ears.
So if Amazon then rose this out like a cloud based solution to other companies then will further strengthen the logic of how different competitors of your company potentially use very similar AI application. So that further shows the need for really focusing on integrated intelligence. So that's the first key point I think. So you should go beyond isolated data analytics to really make this kind of integrated AI doesn't matter if you call it integrated intelligence or not this kind of integrated AI a top priority at the firm level, you should also try to balance efficiency and innovation. I'm not saying that using and leveraging all these automation benefits in terms of efficiency increases optimization, that this is not a good idea. This is definitely important. But what is also important is to think about when, think beyond that, to also think about innovation opportunities, business model, innovation opportunities, and growth opportunities based on AI.
So it's not only about efficiency. I think that's really essential and you should also be open to enable experimental transformation. So go beyond technology. That is also also pointed being pointed out and previously in this session, and also consider, say more holistically the challenges related to markets, HR and overall and competitive advantage. And I think if you do that and consider these three points, that's actually really a first important step towards fully leveraging AI in your organization. Let me finish and close by returning to my initial example of Liverpool football club, they have this analytics team and some persons in this team, they don't focus on selecting the next best players. They also don't focus on preparing the next match for Liverpool, but they actually do some research and development with more complex data models, including video tracking. And on that basis, they really try to change the game of football.
As we know it so far, which has typically resisted innovation for quite some time. And they think about opportunities to script plays, which was hardly possible in the past, at least not in traditional football. So I think the end of this presentation is hopefully the start of the implementation in your companies. And I hope that this idea of integrated intelligence may be helpful in that regard. So think about connecting isolated AI applications to your existing core competencies and human skills. And with that, thank you very much for your interest. And thanks for joining. And I look forward to a couple of questions and comments.
