You just can’t lose… with Chris Boos. Time for an AI reality check


There aren’t too many people you can listen to today where you feel all those sticky layers of hype just fall away from your brain, as this guy actually knows what he’s talking about and (as we English love to put it) he just doesn’t mince his words. So, after a terrific meeting with Hans-Christian (Chris) Boos, Founder, and CEO of leading AI platform vendor arago, I pinned him down to share some of his views with the HfS crowd…

Phil Fersht (Founder and CEO, HfS Research): Chris – you’ve been a terrific guy who adds so much energy and colour to the intelligent automation industry… but can you shed a little light on your story?  How did you find yourself setting up the business in 1995?  Was the focus on intelligent automation back then?  I thought we were all going nuts about ebusiness!

Chris Boos (Founder and CEO, Arago):  Phil – I originally wanted to do AI research at a university and then I saw how slow academic research is today with the way it is financed. I chose to do it inside a company instead. We could control the pace there. We setup arago to research general AI and my belief has always been that general AI is all about automation. If it is intelligence – even the quite boring artificial version – I guess you could say that smart automation was my goal, then.

Most people are surprised about the research phase. But if you look at most people who are doing significant work in AI they all plan or have done a roughly 20-year research phase. The one thing that is special about arago is that we financed it ourselves. We split the company down the middle, half was doing research and the other half was doing projects to get the money. That way we did not only get to do basic work on AI and make money to finance the work, but we also had a testbed for all our components in real businesses. A brilliant idea I cannot take credit for, it was my uncle who founded arago together with me who came up with the model. It worked out really well for us. We did pure research from 1995-2008, then used our own toolset to start automating IT operations and slowly turn it into a product and do some deployments till 2014, then scaled automating IT operations on top of more static approaches while collecting a dataset that is descriptive of all kinds of companies in all kinds of industries and 2017 we finally started applying AI generally in other industries and processes than IT.

By the way, 1995 was before the e-business boom started. I remember we did the first online banking on the web in Europe then and the page said, “your browser should support tables”. Can you still remember these days?

Did you ever expect to be where you are today? 

Absolutely not. I am still being surprised every day. If you had asked me about feeding animals with an AI a year ago, I would have looked at you like you just tole me the aliens had landed. Now we are feeding animals with an AI. This is what is so absolutely fantastic about the industry, there is a new frontier to be pushed further every day.

Fortunately makes up for all the crap you have to hear because everything that has the slightest bit of math inside is called AI these days. I would like to reverse that saying: It is only called AI as long as it does not work. As soon as it works, it gets a real name like “facial recognition” ????

So you’ve been talking about some very real and honest stuff regarding machine reasoning… what’s this all about?  

In the area of AI, we have made one mistake since 1954 now. Whenever we found a new algorithm or were finally able to actually apply an algorithm we found a long time ago, we declared this algorithm to be the one solution to everything. This one-size-fits-all approach is not only stupid but lead to AI winters which were periods in research and commerce when no one would touch AI with surgical gloves – except for the crazy ones of course; did I mention 1995 was during such an AI winter? We are making exactly the same mistake again, by declaring deep-learning equivalent to AI. One algorithm will not be a solution for everything, it is a solution for a defined set of problems. This means it will fail miserably at other problems and also have clear limitations in the scope it was originally built for. Let’s stick with machine learning for a bit. The clear limitation is data. At some point, there will not be enough data to describe “now”.

I believe there are three basic sets of algorithms to be considered when building a general AI:

  1. Machine learning.  The ability to learn to recognize patterns and associate positive actions with them. This is like evolution, everything that behaves favorably survives, everything else dies. Adding a temporal memory to this system was what started deep learning with long-term-short-term-memory networks. But there are many other learning algorithms.
    In biology, we call this “instinct” and all species have them.
  2. Natural Language Processing.  Now this is where it gets tricky, because language has so much compression. Think of how many different pictures you can imagine on the 3-byte input of “cat”. Your implicit knowledge of context and an internal argument narrows what you understand when you hear “cat” in a conversation down to a very likely correct interpretation. Machines, unfortunately, do not understand anything, and they also lack all the context. This is why NLP is still one of the hardest parts of AI. There is no way to “learn” the meaning of language through machine learning (yet), especially because the context, is so volatile. This is why the hype around chat-bots has lead to a lot of disappointed customers. They work well if you can predict the dialog with a high degree of certainty, for example, if you offer a telephone line where people can call in sick you know that there are only so many ways to say “I am not feeling well” and the only result you want out of the dialog is “when will you be back”. But all other more general cases are very difficult. This is why you have to change your language structure quite drastically if you want Alexa, Google Assistant et al to do anything for you. There are a lot of very advanced algorithms in this area which are mainly very advanced statistics to create probable context, probable synonym, probable XYZ and then math this to a pre-determined understanding structure. These are the least self-reliant algorithms in the AI family.
    In biology, language was the single differentiating factor that made us as a species outperformed everything else on the planet. We no longer had to go through long cycles of observation as well as trial and error to have only the ones with the right solution to a problem survive. We could simply tell each other “if you see a Tiger, run away” and no evolutionary iteration was needed. This is a huge advantage which machines are completely missing.
  3. Machine Reasoning.  The one you were actually asking about. This was how AI started. The idea was to make a logical argument to find a solution to a given problem. The first attempt was to use decision trees to “write down the one and only answer for every situation”. This does obviously not work, because the more interesting a problem is the more different ways of reaching a solution there are. The industry moved from decision trees to decision graphs. Then we found out that logic does not govern the world and that ambiguity, contradictions, overlapping information, wrong information and unexpected events have a huge influence on how to really solve a problem. The type of algorithms that create a solution by outputting a step-by-step execution instruction for a more complex task by choosing the best step to take out of an existing pool of options and then the next and so on are called machine reasoning. The limitations of these algorithms used to be in the knowledge base because the maintenance effort of such knowledge bases grew exponentially and the benefit grew polynomially.
    In the world of biology, this is called “imagination” of if we want to be less philosophic the ability to simulate a bit of the future in our heads to make the right choices of what to do in order to reach a defined goal.

Looking at only one algorithm set to solve all problems in the world seems dumb, yet if you read about AI, it seems that machine learning has become synonymous with AI. This literally guarantees a bursting bubble once the limits of data availability are reached. My prediction is early 2019.

We have set out to combine these algorithms to produce a single engine with a single data pool to mitigate these problems and this is why we started in IT automation and are expanding to more and more complex automation across all kind of different industries.

And you’ve been quite poignant regarding your views on AI actually substituting human intelligence and how unrealistic a “singularity” is – can you share some of your candid thoughts here with our readers?  

The entire debate about singularity does not make sense, Phil. We pretend that simply by rebuilding the electrical part of the brain we get a self-conscious self-reliant entity, why should that happen? If you build the skeleton of a dinosaur you don’t get a dinosaur either.

Ok, to put this down in numbers. A large neural network used in deep learning has about a million nodes today. Is uses up the power of half a powerplant. An average human brain has 84 billion neurons and uses 20 Watts. According to Moor’s law, we can achieve rebuilding this by 2019 and I am one of the guys who believes that Moor’s law will hold. Yet, that is not all there is to the brain. The brain also has a chemical system creating a literally infinite number of configuration of the brain’s 84 billion neurons. Infinite because the chemical system is completely analogue. And then for good measure, there are a lot of well-reviewed research papers arguing that the brain also must have a quantum mechanical system injecting probabilities. So there are two entire dimensions we are missing before we can really reproduce a brain-like structure.

And even if we could… We don’t understand or know what consciousness and self-awareness are, do we. So how do we think we can build it? Is “by accident” really a good explanation? “Build the field and it will come” is definitely not the answer here. This is why all the talk about killer AIs and ethical machines is far too early. I am not saying there will never be a super-intelligent AI, but not in the new future.

That does not mean that current AI technologies cannot outperform us at tasks we have already mastered. Tasks that we as humans already have the experience for and thus tasks we can transfer to the machine. But why would we mind? A crane is outperforming my weight-lifting ability everyday and I think that is perfect, I have absolutely no desire to become a crane, do you?

What will AI truly evolve into over the next 10-15 years, based on your experience of the last two decades?  Is there any real reason why change will accelerate so fast?  Are just getting caught up in our own hype?

What will happen is that automation leaves the constraints of standardization and consolidation. With AI systems based on today’s tech, we can automate tasks, even if they only occur once and even if they have never been posed like this before.

I think I was a bit too abstract here. I believe that AI will make any process that we have mastered and that is not entirely based on language autonomous. Machines will most likely do 80% of what we are doing today. Which means that our established companies get a fair opportunity to catch up with the tech giants. This is why I believe that we need RPA as a transition technology, because it basically puts an API to everything that there is in the corporate world. On top of that, we can use AI to automate almost everything allowing every enterprise the wiggle-room to actually evolve

So what’s your advice to business and IT professionals today, Chris – how can we advance our career as this intelligent automation revolution takes hold?

I think in IT we are in a unique position. What click-data was for commerce, IT ops data is for the enterprise. IT ops data describe everything a company is doing and thus forms the foundation of applying the next generation of automation and autonomy.

The only thing we as IT professionals really have to do is open our minds. If we do so, we can revolutionize much of the business and not be the “laggards” who are slowing everything down as we were in the ecommerce revolution.  You know I am German, so I get to be blunt: I think we have to “grow a pair” and take on the risk of automating everything from IT, otherwise business will do it for us and then who needs IT?

And finally… if you were made the Emperor of AI for one week and you could make one change to mankind, what would it be, Chris?

Mankind? That is too big for me… It would have nothing to do with AI, I would force people to think rationally for at least 50% of the day instead of 0.5, but let’s not go that far or people will think I am a cynic.

Let’s say I was made king of AI in the enterprise world for one day. I would decree to stop every POC, POV, Pilot, or whatever other terms you can find for trying to be half-pregnant and force people to start doing things in production right away. There simply will not be enough speed if we keep on “trying”. As master Yoda said, “Do or do not, there is no try”. We really need to adopt this behavior pattern.

Thanks for your time today, Chris.  Am looking forward to sharing this discussion with our community.


(Cross-posted @ Horses for Sources)

Otis Elevator CIO: Modern apps and IoT for digital transformation

Video: CIO interview Digital Transformation, IoT, and Otis Elevator (CXOTalk #292)


We all know the brand name Otis Elevator, but I bet you did not know these facts:

  • Annual revenue: $12 billion
  • Founded in 1853: 165 years old
  • Employees: 65,000
  • Field service mechanics: 33,000
  • Number of elevators in service: Over two million
  • Number of users every day: Over two billion (and that’s not a typo)

The Otis Elevator Company was started by Elisha Otis, who invented the first safety elevator. NPR shares the story:

Otis designed the first safe elevator when he needed to lift heavy building materials, while converting a sawmill into a factory in Yonkers, New York. He made toothed wooden guide rails to fit into opposite sides of the elevator shaft, and fitted a spring to the top of the elevator, running the hoisting cables through it. The cables still guided the elevator up and down, but if they broke, the release of tension would throw the spring mechanism outward into the notches, preventing the cabin from falling.

Today, Otis is a subsidiary of United Technologies.

I spoke with the chief information officer of the Otis Elevator Company to learn about the company’s digital transformation. Marcus Galafassi, the company’s CIO, was my guest on episode 292 of the CXOTalk series of conversations with the world’s top innovators.

This episode offers an inside look into the hidden world of elevators and the people who build and service them.

From my perspective, Otis is addressing three challenges that make digital transformation hard:

  • The first challenge lies in driving change through its globally distributed network of 33,000 field service technicians. As you read below, check out the comments from Marcus on the composition of modern change management.
  • Second, given the company’s age and history, changing established ways of thinking is also a major issue.
  • Third, customers expect Otis elevators and escalators to be safe, high-availability machines. The sheer size of the company’s worldwide installed base creates ongoing pressure to ensure safety and reliability without screwups.

The company’s digital transformation is a fascinating story that involves modern technologies such as sensors, internet of things, and even building interfaces between Amazon’s Alexa and elevators.

Watch our conversation in the video embedded above and read the complete transcript. Below are edited excerpts from the transcript.

And, if you want to learn whether the “close door” button on elevators works, then read on!

As CIO, you develop systems that affect the product directly?

Marcus Galafassi: Yes. For example, the apps. We have done a lot of apps for our mechanics. These apps, they are apps that are linked to the customer experience.

That’s the problem we have, the customer experience. We go into a service in a contract that we have with the customer. The customer doesn’t see that I came there, and a frequent question was, “Did you come here and do what was supposed to be done? I didn’t get any feedback.”

We just launched a very simple digital tool using text as a basis. I’ll tell you; the feedback we got was very simple. The feedback we got was great.

Again, when you do apps like these or also to our mechanics that can improve the right quality or can improve efficiency in the field like searching parts that I needed to order, this is a great opportunity where digital is beyond what I would call in a traditional company or the CIO boundary. We are doing things that go beyond our organization.

How does digital create opportunities to go beyond the traditional CIO role?

Marcus Galafassi: Sensor data is one thing that is addressing a lot of opportunities for us. In these two million units, from a contract standpoint that you have in the globe, we more than 300,000 connected today. We are harvesting this data from a sensors standpoint.

I think you talked about pushing the “close door” button, right? I bring the example of this “close door” push button. One of the major issues we do have in elevators is doors. Why? Because people are rushing in the day and try to arrive and then see the door closing. They try to hold the door. Then holding the door, again, affects the mechanisms we have, I mean the components we have in the door.

Sensors, we have put sensors in the doors, and you can understand that, throughout the whole data history, how much can you predict on this kind of sensor information, and how can we anticipate services to be maintained? Again, sensors providing this kind of data is very rich. If you look at doors, again as I said, 60 perceent of unexpected services are caused by this type of failure. If you can bridge the technology, the digital IoT capability along with the data analytics, it’s a very powerful technology for us.

We have done more than that. In our deployment for these 33,000 techs, we have now 17,000 done. We have established a very strong network in terms of change management. We have more than 1,000 people that know how to support the technicians in the field. They know how to help them to sort the problems out because, again, mobile for me is easy but, from a technician that has worked 30 years in the industry, it’s quite a challenge.

Again, that’s an opportunity, I would call it. The technology has been evolved. I think the CIO has an opportunity and has to take this and move forward.

That’s what we did two and a half years ago. We established a very nice strategy to transform and support our digital folks in the field. Then, of course, we’re evolving for IoT moving forward and clean up the baseline, our backend in the company as well.

What were the business reasons for these technology changes?

Marcus Galafassi: We have done a lot of assessments in Europe, mainly because Europe is our mature market from a services standpoint. Again, most of the comments and what matters from a customer’s standpoint is information. How can I make the information available to our customers, and how are they taking that information and, of course, helping them to drive?

I have a situation, for example. It’s a true case where a customer told us, “Look; I have one person that walks every day in the morning, in the afternoon, in the night.”

I ask, “Why? Why have this? To do what?”

“Just to walk in every level of my building and check if the elevator is running.”

So, we have a customer that’s telling you, “I have one person from my building walking every single day, every time, to check if the elevator is up and running.”

Again, if you can connect the elevator, get the sensors, predict if it’s going to be broken, or even more as we showed in the Shanghai Expo, we have what you call a customer view. We can show, in a dashboard, all the units that belong to the customer. Actually, the name of this is called the Campus View.

Instead of having the person walk and seeing if the elevator is down or is up and running, he can see the dashboard, and he can see it on his mobile, so think about it. Again, that’s the way that you address customer experience. That’s the way that you address the communication that was missing in the past. That’s what the customers are looking for.

You want to simplify the customer’s relationship to the elevator and its components?

Marcus Galafassi: We really appreciate technology. But, these 300,000 units, I told you some of them are still connected in an old-fashioned way. I would say ISDNs or corporate-based telephone lines. They’re still getting the sensors data.

But, when you look at these technologies and you see the data around the technology, we can still apply, I would say, analytics on top. We can still apply data patterns around that, and you can still have information helping us and us helping the customer. Definitely, one of the major points that we have done in some of the apps that we have built around is to improve the right quality.

In the past, we used to have a technician come if a Motorola broke, as I call it, you know, then connect a cable and hooking the cable into the control board. OK. Now we have a wireless dongle, and I can connect even in the lobby without even touching the control board in the elevator machine area. That’s one thing.

It improves safety as well, by the way. I don’t need to have a technician downloading all the data associated with failures or potential failures. I don’t need to touch the equipment. That’s my point.

All of this is in the cloud. We started cloud four years ago here at UTC, our parent company. We are the pioneers of the cloud in our business. Again, I think that’s the powerful information in the cloud at the hand of our technicians and at the hand of our customers. I think that’s pretty much our vision.

What agile approaches do you use?

Marcus Galafassi: That’s a great question. We started two years ago, Agile methodology. We learned a lot. It’s not easy coming from a traditional waterfall into an Agile method. I mean the learning process, what it means to split into scrum teams. How can you put it all together?

I think the great news on this is, again, the technology has evolved a lot. I was a COBOL programmer. Don’t ask me when I was. It was a long time ago. I remember I was sitting along with the other guy asking, “Can you do a report like that?” Then two weeks after, “Oh, by the way, I missed this.”

Again, it was kind of, you come; you ask me what you want. I think I know what you want. Then I do something. After, you just realize it doesn’t work.

Waterfall is over, from my point of view.

I think we can still use some waterfall technology or process to help us address, I’d call it, traditional IT systems. Some ERPs still require that. But, what we did, we actually have a very strong process with our parent company, UTC, and it is across all our business units. It’s called ACE, achieving competitive excellence.

ACE has one of the tools called passport review boards. It sounds very bureaucratic, and sometimes it can be bureaucratic, as long as you want to. But, what we have done now, we have merged some of the key fundamentals and what is a PRB, as you call it here are, at the end of the day, milestones. I need to have a minimal design. I need to have a gateway. After the design is done, what’s my build? After my build is done, what is my outcome that goes for production?

What did was merge the key gateways along with Agile to make sure that we would be fast with a sprint approach, creating scrum teams, but also controlling the quality throughout the process. These four apps, I think, work very well. We did eight apps in one year, pretty much. We deployed 17,000 phones, 17,000 mechanics, with an ecosystem using iPhone, and our MDM is AirWatch, pretty much at the same time, a year and a half. It was very, very aggressive, and we see the methodology for helping you, like DevOps or Agile. That’s crucial.

If you want to go digital and you want to do it fast, you need to have, of course, some change management along with your customers, internal or external. Again, external, we are having a lot of experience bringing some key customers to attend some of the sessions and providing feedback in the design. I think that’s the evolution.

Another important thing, we started this pretty much alone. A year ago, UTC, UTC Digital — UTXD, as we call it — has launched an accelerator in Brooklyn where some of these capacities were not present there at all. We started, again, an hour away from a notice elevator standpoint, but the capacities like design thinking, ideation, incubation, product management, not project management, product management and, of course, analytics. We are creating these capacities inside the house. You can go and show off the results outside, but I don’t think it’s healthy.

If you want to just really turn your company digital, you need to have this kind of capacity. You need to have a design and thinking process. You need to have Agile, DevOps. You need to have product managers taking care about the product evolution, the digital product evolution throughout the period. And, of course, in AI, analytics are components that can help you improve and enhance the product. Again, it’s a mix of methodology, capabilities that you need to have in-house, and you need to foster this more and more and more in our IT organization.

How do you change an organization that is 165-years old and so well established?

Marcus Galafassi: It’s, not all about technology. Technology is great, but it’s technology; it’s people; it’s process.

I think, what you have done, we addressed a very strong champions network to help us achieve goals. I’m talking to the apps aspect now. We have 1,000 champions across the world. I think these people have been trained. These people have been fundamental to help us turn this. I call it adoption because it’s really adoption.

You can give a very nice phone to a technician, and he can YouTube. He can do this; he can do that. But, at the end of the day, you want them to use the apps and make his life more efficient, the customer’s more communicated, and the experience better.

The key fundamental point is if you go to this technology and if it’s 165-year-old company or if it’s a six-month old company, it has to have a change management concept in place because, without that, the technology itself is not going to help you. People have to adopt. If people don’t adapt and adopt, then you have a problem.

I’ve got another example that our service leader gave me, and I was very happy about it because we deployed along with these 1,000 champions in our change management network. A lot of communicates decided on our Yammer setup.

There was a guy. I don’t remember his name. A mechanic, he was having a problem. It’s like China time zone back to California time zone. The guy was typing in Yammer. He said, “I have a problem, specific problem with this setup in this machine with this control board,” and so on and so forth. A guy overseas, he answered and said, “Look. I had the same problem. By the way, I did this.”

Think about the power of this. This is a true change management. [Laughter.] It’s a tool that is across the board. People are talking to each other virtually. They don’t know each other face-to-face. They never met each other face-to-face. But again, that’s, for me, the power of the technology. But, we had to create this network because, if we don’t create the network, if we don’t create the right groups, we just can create, as they call it, a little bit of confusion, you know. We need to use the right technology to help and evolve the newsletter to type it once in a true, digital communication.

Modern change management means collaboration?

Marcus Galafassi: Yes. Pure collaboration.

Pure collaboration and you have done some tooling as well, some tools where our mechanics if they have a problem, can call an expert. They can Skype themselves. They can show on the phone where the problem is, and someone is going to help them to address the problem.

Before it was someone trying to call someone. We are digitizing all of our technical information, which is the basis for searching parts and everything else. In the past, we used to have binders in the trunk, and you go back to the trunk in the car. You open it up and see what’s the part I need.

Definitely, technology helps change management, but we need to organize that and structure that properly and have the right champion network, have the right expectations how to use the technology, what can be done, what cannot be done, [and] create the collaboration. Collaboration is key. I think the technology helps, but if you don’t have structure, then it becomes another app that is not going to be used.

What metrics do you use to evaluate digital strategy?

Marcus Galafassi: You have to address adoption. Let’s talk about adoption metrics.

We have adoption metrics in terms of usage of the apps. We have a process also to collect, in our incubator, ideas that ties, again, with the KPIs that we have established.

I think one of the main concerns, along with the change management, is how can you drive adoption? Adoption, again, is relative. It depends on the person’s experience, the person’s expectation, but we have set up very nice KPIs around how the apps and the usage of the apps have been, on a monthly basis, collected.

We have a process in place. We have improved it with a lot of design thinking and improved the UX, the experience. We are trying to combine some of the apps to have the right business flow or process flow from a mechanic’s standpoint.

We needed to map personas. We needed to map our ecosystem where these personas are going to use these and for what purpose. Usually, I mean traditionally, we have not done it in the past. So, I need to put myself in the shoes of a mechanic, and I need to in a hoistway, which has no cellular communication at all.

When you build up an app and think, “Oh, an app is great,” oh, but there is no offline capability. Okay, I think it’s a big mistake, right? You are in a building, in a hoistway, completely closed, and there is no network coverage. You cannot build up an app thinking that the app is great if there is no offline capability.

We need to have, again, the persona. You need to put yourself in their shoes and have the right empathy to make the right process, the right tool, the right app for them.

When you design a product, and I talk about an elevator, we need to think in that perspective as well. How can we integrate the sensors? How does that sensor go into the cloud? What are the AI opportunities we have around that? Can I put Alexa to call the elevator? Yes, of course, you can, and we did it, by the way. You’re more than welcome to come here and visit us in our Bristol Tower.

Bristol Tower is in Connecticut, in Bristol, and it is the highest tower in North America. We have a prototype of that we show to customers. They love that. Again, these kinds of ideas, that’s what you want as a spirit, as a digital company.

How are you using the Internet of Things and why?

Marcus Galafassi: I mentioned before the Campus View, which is one of the customer dashboards that we have presented in the Shanghai Expo. The Campus View gives you how healthy the customer fleet, the elevators, are in their respective building.

You can see the available red and green dots. Why it’s red is because we can have an elevator or the unit down, shut down. Also, you can see, and you click. It can start to turn green to yellow, yellow to red, and you can move the time. The time means I can move ahead a little bit and see in three months that yellow becomes red. That’s what we can see. Of course, the customer has the information available.

We are using sensor data. We are feeding off this data in our cloud. We have done a condition-based maintenance to predict using, again, doors as 60 percent of our call-backs, as you call it, or unexpected visits are driven by doors problems. We have done very well, along with engineering and our research center here, prediction in how these patterns of failures could happen and then how they would happen. We can anticipate that failure. That’s what addresses not only deficiency in our site but, also, we prevent to have a unit in a shut-down mode, which is the worst thing that can happen to our customers. Customers need our elevators 100 percent of the time up and running. That’s pretty much the whole business value we see with IoT in our case.

Are the close door buttons on elevators real or fake?

Marcus Galafassi: If the customers ask you to shut down, we’re going to shut down the button. [Laughter.] That’s a customer request.

[When people force open an elevator door that is closing,] the whole mechanism [of the closing door] (sic) have to move back. If you have extra strength, it’s even worse. This completely interrupts the momentum of closing the door, so we don’t like it [and can cause the doors to require service].

It can be perceived as a fake button. But, I let you judge that. [Laughter.]

CXOTalk offers in-depth conversations with the world’s top innovators. Be sure to watch our many episodes!


(Cross-posted @ ZDNet | Beyond IT Failure)