 Yes, good morning. Good morning. Good morning. Bonjour. Salamat Paghi. It's really great to be here, a great pleasure to be here. So my name is actually Gard Leonhardt with an H, but we can skip the H. It's fine. I was born in Bonn, Germany. So if my presentation is perfect, that's my German background. I live in Switzerland in Zurich. So if there's no risk in my presentation, that's why because of my Swiss background. And if it's awesome, that's because I lived in the US for 15 years. It's also because I speak rather fast sometimes. So please apologize because if you speak in the US and you speak slowly, then you get kicked off the stage, right? It's just no point in it. So first of all, I wanted to say briefly what a future is. As a futurist, I don't predict the future. Some futurists have done that like Alvin Toffler and Isar Gazi-Moff and, you know, brilliant people like this. I basically observe the future and this is something that all of us are doing, but I do it about 95% of the time. So I don't operate a business while I have a future business, but it's basically thinking about the next five years and I want to share some of my key insights with you today. So my real job is listening and I would definitely recommend that we start thinking about this as being part of your job as well because it's so important to listen to the future to the next five years. You know, in China, they say if you want to know about the future, ask your children. And if we're open to understanding what the future brings, you know, the next five, seven years, not 50 years, then things change for us. And the most important thing that's happening today is the convergence of man and machine, technology and humanity. I happen to have a book on this. I just wrote Technology vs. Humanity. It's been out for three weeks. So all of your questions you may ever want to have, they answer in the book. Just kidding. This is the most important curve of the day. Exponential technological change. It's very hard for people to understand exponential because obviously humans are not exponential. You know, we do learn occasionally. We get a little bit linearly faster. We go step by step. But we're not computers. You know, what's happening now in computing and pretty much everything around computing is that it's mind-boggling explosive. Not just Moore's law and computers, but also artificial intelligence. Now we're seeing things around us that used to be science fiction. They're becoming science fact. So I've driving cars, automatic language translation, intelligent machines, robots that people fall in love with. You've seen the movie Hurd. It's actually happening already. I'll talk about that a little while. So we're now moving into a world where you say this can't be true. This is actually working. I mean, we have two years away, pretty much less than two years, until language understanding of computers becomes completely normal. You can speak in 50 languages, even mix languages, and command computers, natural language understanding. And this has always been tested, but finally it's here. So the mistake that we often make is that we say, okay, it hasn't happened and it won't happen because we were here, right? We were doubling 0.01. It's still nothing. But now we're at four. The next step is eight and 16. So we're going to be at the top of this curve in roughly seven years. That's 30 times as much as we're here. So my kids, that are millennials, you know, they will probably never know. The kids of my kids will never know how to drive a car without a computer running it. They will never know what a CD looks like. They will never be offline in their lives. Is it a sad thought or not? I don't know, but it's more like a mental state of being offline. Basically, our future is we're at the pivot point. And if we keep thinking in a linear way, we will see catastrophic results for businesses, but also for politics and culture. Because thinking linear means that we say, okay, we wait and see, but now pretty much in this kind of world, wait and see means wait to die. Because things are moving so quickly that we don't have time. The car industry is the best example. Six years ago, I had a workshop with some of the major German car makers, all of their leaders, and we talked about self-driving cars, autonomous vehicles, car sharing. And what did we get in a room like this? We got laughter. No, we got literally got laughter. Nobody wants to share their car. Are you kidding? That's a crime in Germany. Share my car. We're thinking of that. So today, the number one initiative of all car makers is electric vehicles, self-driving vehicles, car sharing, you know, mobility. Imagine if we would have thought of that earlier. So we would have had possibility of changing things. So today on this curve, we have exponential connectivity. The world is connecting at really high speed, mobile and otherwise. We have data as the new oil. We talked about this for a long time. It's finally here. The data companies are the most powerful companies in the universe as far as we know. So no longer oil, but data. Intelligence. What can you do with lots of data when you don't have intelligence? Well, a doctor can't possibly remember 140,000 cases of cancer of the same patient that he's seeing. The machine can. That's turning us into a bit of a super human, you could say. So this is also the key question. How do we keep the human inside? I would propose to you that it's actually totally meaningless if your business is really great with technology if you have no meaning. This is probably not news to you, but basically people don't make decisions based on data. I mean, we do all kinds of things. What we do as a human is vast and more complex and saying, okay, the focus group has proven X, Y, Z, and so that's what we do. Data is really crucial to what we do in business. It's a big decision point, but just like you don't eat in every place that TripAdvisor tells you, TripAdvisor is a great tool, but God, you'd be in deep trouble if you'd just eat where TripAdvisor tells you. It's a data point. So what's really important is how do we keep the human inside? I've developed in my book these two terms. One is algorithms, of course, you know what that is. And the other one is andro-rhythms, the things that humans do. And there are some little things that humans do, mystery, secrets, lying, deceptions, humor, empathy, compassion. You know, this goes on. And this is very important for business because business is about trust. You could say in a way that the only currencies that really matter in business are trust and data, but sometimes they're of course at odds, because you don't really know what to say. Ginny Rometti, the CEO of IBM, and IBM is one of my clients just to divulge this, but Ginny said one day that, and a speech that basically in the future, decisions, major decisions will be made by algorithms and data, not by intuition and experts. I don't know if you agree with this. I would say I hope not. I think basically data is becoming increasingly powerful and important. But will we remove the human part of decision-making based on data? Will you let an AI decide whether you should have a baby or not based on the reading of DNA of that baby? Or would you let an AI run the government? That would be a logical conclusion. Well, at least in Germany, it may not be useful. So we have to think about what that means. Basically, this is bringing home an interesting thing called the Morovac paradox. And that paradox simply put says that whatever is really easy for computers is hard for people, and what's hard for computers is very easy for people. In other words, the stuff that we do as people is almost impossible for computers. For example, when I meet you in the hallway, it takes us about one second, sometimes less, to figure out if we're basically in the same tribe, you know, if we can talk to each other. And that's without saying anything. So there's a huge amount of information that takes place between people. That is sort of unquantified. And never mind those issues like ethics, morals, values, understanding, trust. So at that point, we can say, well, it's kind of an interesting angle. You know, for example, you remember that story on Facebook? This Pulitzer Prize-winning photographer, Nick Utt, he had put his photo on Facebook. And Facebook kicked them out because Facebook's algorithm said no nudity allowed on Facebook. And well, I took out the part here, but basically it's more or less a new child, right? But this was a photograph and it's won all kinds of prizes. And he was kicked off Facebook for the picture. That's how smart the algorithm is. Did not recognize art from pornography. That's the Morovac paradox. So we have to do better than that in the future, because basically the bottom line is machines don't think like humans do. We think with our bodies. Daniel Kahneman, the Nobel Prize-winning psychologist once said, we think with our bodies, not our brain. So I would submit to you when we're talking about data analytics, that's the good thing. Let the computers think like they're going to think like machines. Let them think in an entirely different way. They don't have to be like us. They shouldn't be like us. It's fantastic if a computer can do its own thing, its own thinking, its own cognition. Do we need a computer to really understand what we feel like, what kind of ethics we have? Do we need a computer to decide who to kill when an autonomous car has an accident? I would submit to you that 99.999% of what we're talking about here could be very well achieved with computers being cognitive without going anywhere close to this. And that's the low-hanging fruit of what you guys are doing. In business, we don't need computers to think like us. We can take care of that, right? There's a few things that we should keep. We have to think about the possibilities that may leave us baffled, like this one is a software made in Switzerland by accident. It's called Emotion Advisor. Check out the video and then let me know what you think. It's called EnViso. This software analyzes people's facial movements as they're looking at financial products. So if you... It's kind of funny, isn't it? It would not be happy what mostly we would discuss, I suppose. So you're looking at things and the software says, this is what people are really thinking. It's like a lie detector. It's a very interesting angle. This is kind of baffling because maybe very useful. Do you believe that this is a useful product? Well, maybe, or maybe not. It's actually an interesting angle. It's really hard to understand where it goes. Just take it one step further yesterday. Newsweek was running an article saying that some people had proposed the next president of the US should be an artificial intelligence because the argument was an artificial intelligence and machine would not have misgivings about things, wouldn't lie, wouldn't have any bias. So after Trump it's going to be IBM Watson, or something like Watson. I suppose you could say that Trump already is an AI but a badly programmed one and that really wouldn't make any difference. In any case, I think that's probably not a good idea, right? But here are some of the mega shifts I think that are underpinning the DNA revolution. If you understand those, I don't have time to go through all of them but I'll show you a list and if you understand those, then you can look forward and say, okay, how do we actually use this? Here's the list. They all end with Asian. That's why I used to call them the Asians. I live in Switzerland so I use the data-fied cow as a symbol because now the cows are actually getting wired in Switzerland to track them and to see what they're doing, how much they're eating, how often they have sex and things like that. So the mega shift underpinning this is really a very complex scenario of many things you've seen before. Let's just look at one data-fication. That's kind of a fake word but data-fication means something like your doctor that you used to go and see. He used to scribble things with hand and put it into his folder with a bad handwriting. Today, the doctor has all the data about you from your Fitbit and from your remote diagnosis and writes on an iPad. So everything you talk about is recorded. Well, some doctors at least, right? Not my doctor. That data is turned into actual data streams. Now we have a hiking boot from an American company called REI. The hiking boot connects to my app when I'm walking and it says, you know, 43 kilometers, you need new shoelaces because it can analyze this. Everything is turned into data. Everything is becoming automated and everything is becoming virtualized and robotized. We put all these trends together, we get things like the blockchain, which I'm sure you heard about. This is the current hot topic of the day. It wouldn't be possible without all those trends. We get the HoloLens, which you'll see outside, the possibility of seeing things differently. We get the fourth industrial revolution and we get intelligent digital assistants. The last one is a biggie. Very soon, we're not going to have websites, we're not going to have apps, we're still going to have those, right? We're just going to talk to computers, wherever in whatever shape they are in the car, on the mobile at home and hopefully that fly to Morocco and my favorite Riyadh, right? And off it goes and takes care of things for us. So you've heard this before, you know, data is the new oil, but now intelligent data is the petrol, is the gas that we drive on. So data is one thing, but you know, garbage in, garbage out, you have to be intelligent about the data. And there's no way that we humans can actually take all that data and say, okay, let me work on that, right? That's just not going to happen. So what happens here is we have all these helpful tech companies who are now the new oil companies. You know, if you look on the left, just 10 years ago, the big companies in the world were the data of the oil companies and of course some banks. That has changed too bad. Now we're over here, all the big companies, market caps, our technology companies, data companies, social networks and platforms. If you look a little bit further down, there's Chinese companies. That's the next edition of all of these guys, right? In fact, Baidu and Alibaba are already in the upper piece. So that's really interesting to see where things are going. Facebook, for example, has a great correlation to this whole discussion about data oil. You can see, for example, the market cap of Facebook is higher than the market cap of Walmart. Connecting people and being a pleasure trap and doing stuff. It's amazing. I mean, we are the content of Facebook. Maybe we should get a piece of this, but you can bet that regulation is a certainty. Just like your oil companies were regulated. I mean, it's interesting to see the most powerful companies of the world used to be very regulated, but now they're not or very little. That's going to change. This is Facebook's battle plan, 10 years roadmap. We can learn something about DNA here. They're mining us. I'm not saying this badly. I'm a Facebook user, but you do feel sometimes like you're being mined. Look at the key point here in Facebook's roadmap. Connectivity, that's obvious, but artificial intelligence. DNA. The global brain. So how do they do it? I used to be in the music business as a musician and producer. I helped the record labels to understand the internet, which of course was a useless undertaking. In fact, the most incomplete transition to digital is the one of the music business. But you see on this really happy chart here, as I have kind of a laugh and a look at this, is the music business actually not doing so bad because we're kind of in the valley of death where it's gone down a lot. But all of a sudden streaming, all of you guys are probably streaming music, Spotify or YouTube. It's about 20 euros a month for 20 million songs. And it used to be about 20 euros for one CD, for 12 songs. That's kind of an issue on a per song basis. But do you see this happening here? Who are all these new guys, the streaming guys? Well, they're not the record labels. They're Spotify, they're Baidu and Alibaba and maybe Facebook eventually and companies like YouTube, of course. They're doing a data analytics. Spotify makes it work and the money is there but you're not getting it. Somebody else is getting it. Same goes for print newspapers. You've seen these curves. I mean, if you're in a publishing business, you know how painful it is. The advertising, look what happened here, the internet and then of course the mobile phone and before you knew it, deep dive into the valley of death. At the same time, everything. There's a great saying on the internet, Facebook is eating the world. It's 92% of digital advertising in users run by Google and Facebook. And what do they do? The answer is data analytics, artificial intelligence. That's their tool. So these are the areas that are ripe for disruption by intelligent data analytics. There's four areas. One is the complex environments and obsolete intermediaries to access. And then we can clearly say that banking and financial is quite a few banks I think in the room. I think banks qualify for all four. That's good news, isn't it? Just kidding. It means that you're ready for disruption but looking at this, for example, obsolete intermediaries, media, travel, transportation and government and military. And again behind all of this is taking data analytics to the next level by using computers that can think about the data. I think it's actually all good news because quite clearly in the summary is Sundar, the new CEO of Google says basically we're going from mobile first to AI first. Computing will be universally available like air. We interact with it naturally. We're descending into a world of digital everywhere. There's fantastic opportunities here. I would say at this point there's 90% positive. There's 10% of issues, you know, privacy, application, losing control, all of those things that we're currently already seeing around us. But this is really a question of governance, you know, of policy. But really great to see that this sort of global brain is emerging and that's what we talked about earlier, right? Basically what we're talking about is a place where we keep all data and analytics and intelligence is kind of a global brain, right? We're all flows together. We're not talking about Skynet here just in case you're thinking about Skynet, right? Thinking about much more positive way. But this is a place where intelligent assistance is the number one thing that happens, right? I turned this around the word, right? Rather than artificial intelligence I call this intelligent assistance, IA. This is a place where you can go to the garage like pretty much every car will do in the near future. That is the low-hanging fruit. So to me, this is a really powerful thing that's happening all around us. For example, the e-mail service that Google has just launched that can answer the own messages, you know, very simply, quickly or slack where you can set up a meeting instantly by automating it and this is basically the reverse of artificial intelligence. A great, called Discover Weekly. Here's a funny story on this. You know, I used to be a musician and producer but under a different name 20 years ago and the other day I got the list by Spotify curated by a robot essentially by artificial intelligence and on the playlist, six of the 20 songs were mine. Of course, Spotify didn't know that it was me but recommended to my own songs to me which was kind of interesting. You know, so that's disgusting. Then we got rid of these songs, right? And then we have automated insights where you can write articles based. Computers write articles, right? We have IBM Watson doing the things for the jacket company North Face, right? I'll show you a short clip on this. Todd Spalletto, president of the North Face. We are working on the prototype to match customers to gear. Watson, let's give it a try. See, it's mid-June and there are 353 jackets. I can recommend nine. Watson, what if it rains? There is just a 3% chance of rain so I recommend the breathable stretch fleece-fuse-formed Dolomiti jacket. A perfect choice, Watson. No wonder our customer loyalty numbers keep climbing. I believe we can do even better. I like the way you... If you go to the North Face website you can actually try out this engine by IBM Watson. It's really funny. I think the bottom line is we shouldn't confuse a clear view with a short distance. This is working somewhat today but it's not entirely here. It's not like we can just go there and do this. It still needs a bit more time. So regardless of this we're heading into a new relationship of man and machine between the algorithm and the humor rhythm. And this is happening in financial industries, for example, on the bottom line level. And that's happening everywhere. That's actually a work and I've tested quite a few and it's really interesting angle. And then there's some people saying that maybe we shouldn't risk putting our knowledge in the hands of machines. It's kind of this back and forth between those two things. But one thing that's quite clearly happening is that we're moving from apps to the global brain so from the mobile phone to the digital assistants. Imagine if you have a real estate company and you're planning to build something. And now you do all your research online, you have people doing the research in the future just in a few short years. You sit down and say give me analysis of the city of Frankfurt the need for this kind of housing in this area. Very complex question and the assistant will get back to you 14 seconds later with the super power for us. Obviously commanding the global brain via voice control. That's right here. I mean if you try to use voice control now on a Mac for example it works pretty well. There's a few things that don't really work too well like mixing German and English words and names you know not so good in a year over. Most of our kids will never actually type with a computer. They'll just speak. Like a lover. I guess. Talk about that in a second. So you have apps like the Amazon Echo. Four and a half million Americans are using this at home. The Echo listens to you and then you say Echo please turn on the bedroom light. Play a song. Buy me some new toothbrush. Alexa order me some pens. Multiple matches in your past orders. One point two millimeters. Black ink pack of twenty eight. The order total is nine dollars and ninety nine cents. Should I order it. Yes. You could say this heaven or it's as hell right. It's hard to tell. I mean this device is listening to you the entire time. Literally the entire time just paying attention to whatever is you're saying it will know if you're drunk or tired although all these things about you right this is a minor infriction on privacy. But. So we're talking about these intelligent agents that can do a sector this. These are not dumb machines like Siri and Cortana used to be right they're actually getting very smart now. They're not like you know you speak to Siri and you say I am. I think I have alcohol poisoning and Siri suggests that you would go to the next liquor store to get some more. And today you can ask really complex questions and you'll get an answer from this global brain. So now we have these intelligent agents you know deep driving diving into into our heads you know finding out what's inside of us. Extremely useful but also extremely open to all kinds of abuse right and this is something we have to think about when you think about data because this is only years away not decades. This merging of man that machine that's really what we're talking about here. This is only years away. Not two years but you know most people say the singularity called the singularity the place where man and machine are have the same capacity seven to ten years and 2050 experts are saying that one machine will have the capacity of all human brains. One machine. So at that point you know the ethics are no longer nice to have. Imagine if we weren't thinking about the rules of how we use data when we when we get this we already have quite a few issues about that today. But why would we go down a road of the Internet of Things of connecting everything if we don't have a way of saying well who's actually in charge. How does it work. So when we think about data analytics you know it's inevitable and this is the power of commerce driving it clearly. So we have to think about you know what is the social contract in what we do. Who agrees to what. What's the deal. In this global super brain. I mean let me make no doubt about this right. There is about a chance of some 90 percent increased efficiency in having a global brain for example in logistics. There's some estimates saying between 40 and 65 percent of cost of logistics could be diminished by using a global database Internet of Things right. And this kind of concept. So now if you think about what does it mean where do we go with this you know what is the next step in this whole debate and that brings me to the what I call the era of digital ethics. Everything that we do today about data analytics artificial intelligence cloud computing has an ethical component values understand this is by KPMG and other companies like KPMG are very heavily talking about what does it mean for human life for our culture and for work right. I mean let's take the wrapper off we're going to have a lot less jobs if this works. If the large company wants to have less jobs less employees that's the nature of the beast. What do we do with this there will be new jobs. Yes. So you can be a drone operator rather than a taxi driver. But still you know whether we go what's the next step in this chain. So now just a few days ago the major tech company six of them announced this new collaboration called the partnership on AI to benefit people and society. Now that's an interesting term. Who would have thought about six tech companies having a partnership to benefit people and society. It's kind of interesting to see that this is the whole idea of saying what we do has responsibility. Finally it actually changes the world what we do. And here's a key question. We have to we have to actually put human flourishing first because a customer that likes you is happy is the best customer you can expect. Ultimately the goal of business is not efficiency. That's just one of the goals of the CFO. The goal of business is customer happiness. And then there's a question of control. When we talk about all these things you know what about our sovereignty sovereignty whatever that's a difficult word you understand what I'm saying. Who is mission control for humanity? Where is that data kept? This is a very important question. There is no doubt we can do all these fabulous things and we can do stuff like solve cancer and dying. Increase longevity solve climate change fix the energy crisis have abundant energy we can do all these things but how do we figure out who's in charge of this? This is a very important question. Who is mission control for humanity? Is it going to be Silicon Valley or even worse China? Or the two together? So when we talk about data analytics we have to think about examples like this one. This is the first example that's been a case a trial in the US that says if we have to release prisoners that are out on bail who decides if they get released or not because they may actually do it again. So the judges went and did this and their percentage was pretty good so they are 18.6% failed and came back or were repeat offenders but the computer failed better. The computer actually made better decisions on if people should be released or not than the judges. Well in this trial I don't know who ran the trial I'm not going to talk about that what do you believe what is better to do? Well you would say it depends that's not that black or white this case automated driving I think automated vehicles and autonomous driving will be in all cities all major cities around the world it will be a long time before in the German freeway in Bavaria we can drive in an automated car because there's a substantial difference in how we drive and why we drive and what the purpose of driving is in fact just now the German government yesterday announced that Tesla should no longer use the word autopilot because it's not it's not an autopilot call it a assisted pilot and this is the problem with data right this is actually a fantastic tool I mean I've been in a Tesla with autopilot it rocks right it's fantastic but it's not driving by itself I'm not turning the seat around and eating a hamburger well if I did I would probably not be here to show you this right it's called judgment erosion judgment erosion happens when you do things based on data that isn't actually so and that I think is a problem for example that was pointed out in the Guardian last week this psychologist pointed out that when the algorithms are getting better making decisions people often stop working to get better we actually take a seat back and let the algorithm take over it's called automation bias and so what we need to do with data analytics we have to think about well how do we go in the future that's not going to be like this right where we think of this as being a solution I call this machine thinking or even worse worm-holing finding a shortcut through space and time that's like saying I don't actually have to build a relationship with a person I can just marry a robot believe it or not there's people proposing this as well so this whole idea of saying what should not be automated that's a key question when we think about data analytics we have to think about what should be automated because there are some things that are not automated like these minor things on this side here you cannot automate trust of the customer you can destroy it by automation and you can further it by automation that's completely different trust is not generated with a mouse click but it can be helped with a mouse click it can be increased or destroyed with a mouse click so those human things are becoming much more important in fact I would say anything that can be automated will be that is an assumption I think that's completely clear let's say anything that can be digitized automated virtualized or robotized will be that's digital Darwinism that is going to happen the flip side is also true anything that cannot be automated becomes extremely valuable and that's us hopefully you in this room your kids some people like to say that basically if you can describe what you're doing for work then chances are you'll be automated this is I'm faring pretty well here because I can never describe what I do to my kids they're like you know you make money telling people about the future that's a crazy idea so this is an interesting interesting angle I think that we're pursuing here on this conversation but I like to say that every great algorithm needs a great algorithm a great human thing so when you use data analytics to make your business better you also have to think of a purpose and your brand and what you're saying in the context of all these things and rather than firing everyone that is now being replaced by algorithms maybe you could put them to good use somewhere else to create the algorithms the human part I work with a very large international chain of retailers and what they do is they say we don't need people at the cash register but now we're going to use the same people to do customer service advisory in the store right or online we train them to do something different to create more value I'll finish with this quote and then we'll have some questions very important quote from Alvin Toffler that talks about our future says in dealing with the future it is far more important to be imaginative than to be right to say that here this would be interesting angle we don't really like imagination in Germany so much we think about this this is kind of a dangerous thing productivity that's better but this is really important for us I mean look at this chart the World Economic Forum says what is the future of our skills it's totally obvious of course complex problem solving isn't always on top of that list but there's a bunch of stuff sorry that's new sorry about this people management emotional intelligence remember when the time when emotions were a total no-no at work emotions can't be emotions don't exist at work and now we're talking about emotional intelligence that is the prime new thing on this chart is emotional intelligence and of course creativity so here's the bottom line now when we think about this what is your choice are you going to be on team robot or on team human here's the choice that you're going to make there ultimately using data and being exponential with analytics is a clear requirement for the future at what point does it overlap with being human or not being human here's a great list of these things that I enhanced or diminished by my Frank Frank Diana he's talking about all the things that technology enhances and all the things that it diminishes and we have to think about what is the ultimate middle ground and I think that there's some CIOs here among you I think CIOs may very soon need to become chief understanding officers not chief information officers this is not really about information this is about understanding it's about this thing that makes us human so as a summary data analytics and intelligence and humanity that's our future the founder of the World Economic Forum says we must put human beings at the center of our systems now this means the center of DNA is human flourishing is put the human inside and to find the balance between the two I think that's ultimately the best value that we can generate Albert Einstein said years ago already computers are incredibly fast accurate and stupid well they're no longer stupid but okay human beings are incredibly slow, inaccurate and brilliant to which I would add expensive and together they are powerful beyond imagination I think that's the ultimate destination of data analytics that we want to get to thanks very much for listening and hope you join the rest of the day