Bill Ruh, GE Digital
An interview with the Chief Executive Officer and founding executive of GE Digital
The following is a transcript of the audio available via the player above. The audio file is the definitive source.
Ali Tabibian: Welcome, welcome, welcome everyone to this episode of Tech, Cars, Machines. I'm your host Ali Tabibian, and you can read more about me and GTK partners, the producer of this podcast series at gtkpartners.com, and in the episode notes of course. It's baseball season, so let's use a baseball analogy. This episode is one of our first at-bats regarding technology [00:00:30] and machines, and we've already hit a two run homer. That's because we have two episodes for you with GE Digital.
Perhaps the most important entity bringing advanced technology to capital equipment. One episode, which we've already released, and you can listen to it right before this one, is with Eddie Amos, Chief Technical Officer of GE Digital. Today, we're hearing from the Chief Executive Officer of this unit, Bill Ruh, who has been there from the very beginning of the technology and machines trend. It [00:01:00] doesn't seem like an exaggeration to say that no one has been there earlier, longer, or on a bigger platform than our guest today Bill Ruh.
General Electric today is a purveyor of heavy capital equipment, such as power turbines, and jet engines, and that's why you heard the jet sound at the beginning of this episode. GE Digital is the company software unit, and it's mandated with infusing software smarts and networking concepts into the world of capital equipment. GE calls this concept the Industrial Internet. [00:01:30] You've probably heard it as IIOT, which stands for the Industrial Internet of Things.
GE pioneered this space in 2011, really several years ahead of others. In that year, GE announced a billion-dollar, five year initial investment in its digital effort, and has since spent about 1 1/2 billion dollars in related acquisitions for this unit. GE digital has always been headquartered in San Ramon about an hour northeast of San Francisco where I'm sitting right now, and it currently has about [00:02:00] or just under 2000 employees at that location.
What you'll hear from Bill today, is a really interesting and sweeping view on GE Digital's genesis story. You'll hear where the unit is today, and lessons on innovating within a large organization in general. Now remember, the type of capital equipment GE makes and GE digital addresses, is typically expensive, mission-critical, and has life safety implications. Therefore, this equipment has always been sensored [00:02:30] and connected typically in very expensive fashion before modern technologies were introduced, but really has been connected and sensored for many years.
Most of the focus in this space, is on the increasingly ambitious aspiration for what to do with all that data, rather than how to collect it and move it around. Without further ado, here's Bill Ruh.
Voiceover: Tech, cars, machines. Subscribe here, or at gtkpartners.com.
Ali Tabibian: Great, we're here today with Bill Ruh, [00:03:00] Chief Executive Officer of GE Digital out here at GE Digital's headquarters in San Ramon. Bill thank you very much for meeting with us here today, and taking the time.
Bill Ruh: Ali, it's great to be with you again.
Ali Tabibian: We really appreciate it, as some of our listeners know from our prior content and publications, Bill was kind enough a few years ago to be a speaker at one of our conference ... At our inaugural large conference actually, and then we've had representatives from GE at subsequent conferences thanks to really Bill's referrals to us, to those individuals. Bill [00:03:30] tell us about the genesis of GE Digital. Why did you come here to do what you started I think in 2011 if I'm not mistaken?
Bill Ruh: Yeah, it's over seven years now, so we're working towards that eighth year, and there's really two things that came about. I was at Cisco Systems, a great company, John Chambers was an amazing leader. We could see that rather than people getting connected, we were starting to see the early stages [00:04:00] of machines getting connected on the network, and all kinds of machines. More than more of the data was becoming this machine data.
I could see this trend was going to continue, and you saw the Internet of Things, but it really hadn't been figured out how you're going to make money at the Internet of Things. When I was contacted by a recruiter to go to GE, first of all I was shocked that GE was getting into software data and analytics, but made a ton of sense to me. [00:04:30] If you look at it, the vast majority of really interesting data is going to come off of interesting machines and interesting industries.
Oil and gas, power, transportation, the rail industry, healthcare. This is where the action's going to be. It's less interesting I'd say in the consumer thermostat, but you get the data coming off a jet aircraft engine, and it's amazing how this can be transformative [00:05:00] for these industries. At that time, you could see that GE was beginning to realize that data was going be the source of more value in their services business.
What I saw, is a company that was early enough, that could actually make a difference in the world. It was really hard to leave that Cisco environment, but I could see that coming to GE, we had a chance really to foundationally create this Industrial Internet [00:05:30] of Things, and really change the world. I think today, nobody asks me like they did back then, "Why are you doing it?" Everybody asks, "How are you doing it?"
I think that just represents that everybody's seeing the value and the change coming to the industrial world.
Ali Tabibian: That's really pretty interesting, how did you finally get your arms around the fact that it's going to be difficult for an industrial company to really embrace ... Not necessarily for the industrial company to embrace [00:06:00] the other, but the two worlds to come together. What convinced you that this was going to be a strong at bat?
Bill Ruh: Well, I think you have a company that has reinvented itself on many occasions over 125 years. I was felt GE was a place where great leaders were born educated, and change is built into the company. We could see the company going through change today. The second thing is GE had real [00:06:30] data. Look, a lot of companies got started, they have no data and no domain knowledge, and this company has data and domain knowledge.
The hard thing was getting them to understand the change in architecture. When I got here, people were Microsoft, .NET, SAP, the last generation architecture. Getting them to realize that today's best applications are cloud, mobile, Blockchain, Dev Ops, agile, [00:07:00] AI, machine learning, open source, big data. Those set of technologies are the ones that the winners are applying. The hard thing I knew, was one, getting them to understand this change, bringing in the talent.
The second hard change is getting everyone to think about and re-envision their business with this. The good news, is we've been able to go through a series of steps and get there, and now we pushed a lot of this out to the businesses. [00:07:30] Digital is a part of the DNA of the businesses, whether it's healthcare, aviation, transportation, they now own their digital future.
Ali Tabibian: Oh, interesting, interesting, we should go into that a little bit later. I do remember when one of the first times we met several years ago, I asked you, "What was most striking to you when you joined GE in terms of a cultural fit?" You said, "When industrial people say reliability and say safety, they mean something that somebody coming out of your typical [00:08:00] Silicon Valley background just doesn't ... It's not ingrained in them the way it is for somebody who puts planes in the air." Is that still one of the things you found out? What else was intriguing when you first came in and looked around at what you had?
Bill Ruh: I tell people all the time, the three most important things for any company, industrial, financial services, retail, to be able to compete in the future. Meaning with digital, because no company is going to be able [00:08:30] to compete without a very strong digital DNA background. We can talk automotive as a great example of that, but it's true of every industry. When you got here, the way you build these big machines, is the antithesis of how you build software.
Knowing your requirements, deep testing. The way you go about engineering is still more of a waterfall methodology, whereas as you well are aware, [00:09:00] the companies who are doing well in digital, they're Dev Ops and agile, and they talk about minimally viable products and pivoting. Well you don't pivot with putting a product into a utility, you have to make sure you deliver fully the capability. Your ability to change is limited, the thing about software, your ability to change is limitless, and so you just have to realize you're taking two different [00:09:30] cultures of development and popping them together.
You've got rationalize that, that can be very hard, but I'd say culture in general is one of the chief challenges of ... I gave you one example where development culture fits, but you can see where product culture fits, and financing fits. A good example on financing, is you capitalize when you build these big machines. It's opex when you're building software, if you're going to do it effectively.
[00:10:00] Talent is the second thing, look, I think you can't just take ... When you go to an industrial company, they think of IT, software, etc., is all fungible. They don't realize that an SAP programmer isn't going to be a cloud programmer. Getting the right talent was a big challenge for us in the beginning. Then I think the last thing's leadership. Look, you have to have leaders who believe and understand this transformation.
[00:10:30] They have to lead, so a great industrial firm that embraces digital, embraces the ability to mesh the culture, and be able to make that work. The ability to bring in new talent, digital natives, and then train their existing people, digital migrants, and make that come together. Then lastly, is able to have leaders who can envision what's made them successful in industrial products, but able to lead with new digital products.
[00:11:00] That is really hard to have people hold both of those things together in their minds simultaneously. I think if you go back to Clayton Christensen's, "Innovator's Dilemma," the inability to do those things simultaneously, is the innovator's dilemma. When you do in those three areas, that's the company that can make a foundational shift.
Ali Tabibian: One of the books on innovation that I've really been impressed with, it came out recently, was Ed Catmull's book called, "Innovation." Ed, [00:11:30] as you probably know is a local gentleman, ran Pixar for years and years and years, and now runs the joint Pixar Disney Studios. His point is, if you want innovation to happen, you have to essentially in the beginning silo it within the organization, and have it report directly to the top.
Otherwise, for very good, and very understandable, and very legitimate reasons, that unit will itself always wind up responding to that quarters exigencies of the operating units. GE Digital to me, seems like it was set up originally that way. [00:12:00] A little bit on its own, and reporting straight to the top. Was that coincidence, intentional, just by virtue of where you needed to find the talent?
Bill Ruh: No, I think that's exactly it. When it was set up under our prior CEO, Jeff Immelt, I think he incubated it in that way. You also have to have a plan for what you do over the long haul. Remember, we're approaching eight years, and if you think about it, we really went through three stages. The first you might call the strategy [00:12:30] and incubation stage, the talent stage. Where we incubated as a thing we really called the software COE (“Center of Excellence”).
We brought in talent from the outside, we worked with each of the businesses, and we did this advanced development with them, and helped them develop new kinds of services and tools and capabilities. We learned a lot about what our strategy should be, and was incubated at the top, and fostered at the top. As it got a little stronger, we went into [00:13:00] phase two. We pushed all of that talent and capability back to the businesses.
Sooner or later you’ve got to get it back to the core, and then we ended up moving and changing the software COE, where we ended up looking at hiring Chief Digital Officers really in each of the businesses. Now we were thinking about portfolios, and product lines, and bringing talent into the businesses, and was more about how you generate now [00:13:30] revenues, and you drive towards your digital product line. Now you're incubating that within your businesses.
The final stage was in 2015, we actually created GE Digital as a business, as well as a capability to help the businesses. We continue to foster them building their portfolios, while we built out a general capability, or product lines which we call Predix, that enabled us to sell not only to inside [00:14:00] the company, but outside the company, to enable everybody to be a digital industrial. We've gone through three stages, and now we're really going to our fourth stage, to where this is all matured.
The businesses have their own capability, we earn the right to sell to them every day. At the same time, we have our own base of business with industrials, and we're trying to change the world in that broader away. The thing is I would say, I think that book got it right. You've got to start and you've got to [00:14:30] nurture it, but sooner or later you've got to let it become part and parcel of what you do, otherwise you never fully realize all the value.
Ali Tabibian: Interesting, that one of the points the book made was also you know you're getting it right when the business units then start coming to you and saying, "Can I have some of that?"
Bill Ruh: Yeah.
Ali Tabibian: That's really the litmus test. Using a specific example, turbines, jet engines, whatever vertical you like, just [00:15:00] walk us through what is the scope of the GE Digital offering? To what equipment does it apply? Does it just do the turbine, and if not, how do you define the right perimeter for Predix to capture for the answer to be difference making?
Bill Ruh: What's interesting, is first ... Some of the naysayers about GE Digital will say, "Oh, they only operate on GE equipment." Well the reality is we love GE equipment first, and we do operate [00:15:30] on GE equipment first. We love our install base, but the reality is you cannot be in this business without doing everybody else's equipment. Whether you're selling to your install base, which has GE equipment and other things to build a power plant, or whether it's companies who do something outside of what GE does.
We built it so that we are first and foremost thinking, how do we win our install base? At the same time how do we win the world? When you think about an example, I'll give you two. One is let's go through an example [00:16:00] inside the company install base. Baker Hughes GE is the oil and gas component of GE. Great company, and they are building out a digital portfolio that's actually quite impressive. When you look at one great company like BP, British Petroleum, there, we've been working with them on some of their offshore oil rigs to look at how do you look across the equipment, [00:16:30] much of it GE, but end to end about an offshore oil rig call it 600 really critical pieces of machinery through the process, integrate all that data, and get a full view on the efficiency, the maintenance, the problems, everything around that, both from an individual machine view and a process view.
This is really game changing, because it allows you to monitor these offshore with real AI [00:17:00] and analytics on lots of data, and really think about making those systems more efficient. We can see you can drive 2% to 4% more efficiency off an offshore oil rig, because of technology like this. What's involved with that? Well there's a platform to pull data and connect machines. There's a product line called Asset Performance Management, which is looking at that data about each machine, allowing you to plan and optimize [00:17:30] its use, and how you maintain it.
Then on top of that, Baker Hughes GE has added a whole set of increased domain value application that's about an offshore oil rig. Another great example of a customer we have is Schindler Elevators. Different kind of product, but same thing. Connecting a million elevators globally, allowing the data to come off of it, doing predictive maintenance on that, becoming more efficient in delivery, and enabling [00:18:00] you to deal with problems before they occur, and drive more people through elevators in a building more efficiently.
The basics of this is a platform for connectivity and data. It's the ability to do the asset performance management. It's the ability to add your own value on top of it. Then we'll do it in other domains, not just on the machines. We do it for your field personnel in what we call field [00:18:30] services management. Now you're taking that same data, and you're organizing your field to be more efficient work. We've done it in the manufacturing facility with our MES and automation, where we've taken traditional MES and automation, but really turned it into a real time integrated capability.
Now we're adding a new product line we call OPM, Operations Performance Management, which ties all that together, and now lets you, the manager, [00:19:00] see cross let's say your whole power plant, your whole oil field, across any really large-scale system, and look at the operations of it, versus the individual machines. For us, these four areas, APM, Field Service Management, MES and automation, and OPM, they're all interrelated using that data and that IOT connectivity in the platform. That's what we think of as our Predix portfolio.
We've been [00:19:30] very successful in building our base of business in the last, since 2015 on this capability.
Ali Tabibian: Would I be accurate and to some extent thinking about the Predix system as essentially an integration layer for ... Sometimes you own the silos underneath ... The software silos underneath, but to some extent the value is in being able to integrate those various sources of data, and providing the analytics on top of it? Is that an accurate way [00:20:00] of thinking about where some of the value added is from?
Bill Ruh: Yeah, I think it's the way it is. I think, but there are really four things I'd have you think about for how we think about Predix. One is it's the platform that is that middleware layer, which has two things. The integration is one piece, but it also has this asset model, and at the cornerstone of it we have an asset model. The same way Salesforce has a customer model, or SAP has a financial model, we have an asset model. It's the platform to pull that data, manage [00:20:30] that asset.
The second thing Predix is, is a series of applications prebuilt, out-of-the-box capability, get you started, give you value on day one, either making your machines more efficient, your people more efficient, or your operations more efficient. The third thing, is an ecosystem. We've worked very hard to bring in partners into this, and so the partners are part really of our Predix product line. The last thing we think of as the architecture. Look, a lot of technology's been built on last generation architecture.
[00:21:00] This is built on cloud, mobile, edge, all of the AI analytics, big data, open source, Dev Ops, agile. It's built on a modern architecture that really is going to be the future of how these systems get built.
Ali Tabibian: I was looking at some of the recent interviews that were done with you, and the interviewer would still ask questions like, "Well, why don't people just build on top of AWS?" That's quite a bit of a misunderstanding of what AWS and some of their tools [00:21:30] are about.
Bill Ruh: Yeah.
Ali Tabibian: If you want value out of a system, boy you really have to be an expert in the workflow, and in the endpoint operations.
Bill Ruh: It's domain knowledge. Now, you know what's funny Ali? We've made mistakes, any startup makes mistakes. We blazed a trail for all of our competitors.
Ali Tabibian: That's right.
Bill Ruh: When you look at one of the ... For example the mistakes we made, look, we started and it was a time when people were building their own data centers, and were white labeling, and it all seemed like the right thing to do. [00:22:00] It wasn't crazy, because we saw the whole Silicon Valley movement towards that. What we discovered, is look the cloud wars are wars, the people who are going to win are going to have big war chests. After about a year of doing it, we realized that was probably not the right approach.
After 18 months we jettisoned that, we pivoted, and we said, "Okay, we're going to ride on the investments made by those leaders in that." You're absolutely right the way you described the question, but one [00:22:30] thing is we feel we're going to ride on the investments they're going to make. The other thing is look, they are not going to be the domain experts at every domain in the world. That's just not possible, so we are going to love the asset model, the industrial data, how you optimize machines. That is something that is unique, there is the opportunity for partnering really deeply with these guys and moving it forward.
I think what we've seen a lot of our customers actually, they took an Azure or [00:23:00] an AWS and they said, "Well, we'll do it ourselves." Connecting one machine is easy, connecting a million machines on a global basis, and getting really deep insight into the operation of the machine, that is really hard. We're focused on that, because that differentiates us. One thing we've learned, is we really are thoughtful now on that differentiation and focus and pushing things that are not out to the side and finding a great partner.
Ali Tabibian: A lot of the cloud providers are really outstanding ingestion [00:23:30] engines, and they have some utilities that are built on top of that. Really, in my industry, the funds that manage funds, when you really look at them, yes, they're supposed to get an investment return, but really they're asset accumulators.
Bill Ruh: Yes.
Ali Tabibian: The more assets they have under management, the more money they're making, whether or not they're making money for the investor. It's a little bit of the cloud environment there about ingestion, because that's what they really get paid for. If you do something interesting with it, then that's great.
Bill Ruh: You got it, you got it.
Ali Tabibian: It's actually a very fascinating similarity, where do you see GE [00:24:00] sitting now, GE Digital in particular amongst the other people who operate in this space? Some of them coming at it from the equipment angle, some of it from the software only angle.
Bill Ruh: We were fortunate that GE had first mover advantage, and defined the space. We had to travel through the jungle with no recipe book. Now our competitors all have our recipe books, so they can get access to people and things that we didn't have at that time. We invented all that, and [00:24:30] so when you think about where we've been, that investment's put us in a really great shape. A couple of key things, and I'll come back to the domain data.
We threw our acquisitions and by pulling this portfolio together, we now have a thousand plus Predix customers. We will do $500 million in Predix revenue in our applications and everything this year. We're seeing great growth on a global basis.
Ali Tabibian: You talked a little bit about partnering, there are a lot of people that I meet with that aren't [00:25:00] sure what they want to do in this space, because they're not sure what you want to do in this space. Eddie Amos in our last interview said, in a little while he's going to have a white paper on the subject. What would you like to share maybe at a higher than technical level, or maybe even at a technical level where you see cooperation with people, and where you'd welcome their input, that maybe they don't realize they would be welcome?
Bill Ruh: Well I came from a company that was probably one of the better partnering company's. Cisco has always been known for its ecosystem, its partnerships, its distribution. [00:25:30] Companies can do it, but the vast majority of companies aren't good at it. Certainly this wasn't a core competency within GE we started. You have to build an ecosystem, because you can't do it all yourself. The hard thing is getting that equilibrium or the right balance between you and your partner, so you both make money, and you both have your position to play.
Otherwise, you're stepping on each other, and it takes any [00:26:00] company a while to figure this out. Partnering is really hard, I would say a couple of things. One is you get exuberant about signing up partners, but you really have to figure out which ones are going to be you're investing in, and they're investing you. It's co-investment that matters, when I looked at it, it's those who are really committed to your product line, taking it to market, creating programs around it. Then you've got to be committed, [00:26:30] you've got to train them, you've got to be standing next to them at the conferences, etc.
The ecosystem has been one of the harder things to get set up. One that we are getting better at every day, and I look, about 15% of our revenue today in GE Digital goes through our partners. That was almost zero a couple of years ago, so we're excited about where we are, but we see opportunity to grow that. It grows quarter after quarter, [00:27:00] it keeps growing. At the same time a couple of years ago, our pull through of partners was probably ... Meaning we sold, but brought a partner in, was close to zero.
What we're seeing now, is we're between 12% and 15% now of bringing partners in. I think the number's probably 30% on both of these, is if you can get there, you're in really good shape. We're targeting to try to push everyone in that direction, but we've seen growth quarter after quarter. We're about halfway to where we'd say we feel really good, [00:27:30] but teams are working hard on it, but creating that, it took a while to get it going.
Ali Tabibian: There are a bunch of debates that have gone on in the Industrial IOT world over the years, and one of them has been edge versus Core. Where should the important stuff happen? I'm sure the answer is diffuse and case dependent, but in your view how have the answers to that debate evolved over the last several years?
Bill Ruh: Look, I think this is one of the more interesting opportunities [00:28:00] that only the industrial world has. The financial world, the retail world, the consumer world, the cloud is going to dominate more. That doesn't mean no edge, but it does mean that edge is not as important. When you're in the environment's we're in, real-time, reliability, etc., lots of data, there's going to be a lot more processing closer. The relationship with the control system's going to be very important.
You're going to connect the edge to the control systems. You're going to have a feedback loop. [00:28:30] You're going to bring data in, analyze it, create actions and scenarios, and then push it back, and do something with control systems, to continually optimize your system. That's the way the world's going to work, and when you look at it that way, it means that the edge compute isn't important. We also see, we live in a world where it's global, industrial is global.
You have data sovereignty, there are countries with laws and regulations that data can't leave the country. [00:29:00] You're not going to put a cloud in every country, they'll be some countries you've got to put on premise, edge-based, bigger capability. For our case we call that Predix Private Cloud. Putting a private cloud at an oil field, or in a customer's facility. The edge with private cloud compute, is probably going to play a bigger role in the industrial world, than it is in anyplace else.
As I look at it, [00:29:30] I've come to a belief system, it'll be about 70% processing on the edge, and 30% on the cloud, which is really the opposite of what we see in a lot of industries. Who knows what it's finally going to be, but there's going to be more opportunity to compute on the edge. That's what our partners like HPE and Dell are doing ... They've got great products for edge compute, and we see that [00:30:00] there's hope for these companies to expand into these new markets.
Ali Tabibian: At the edge, especially when you're dealing with maybe non-GE equipment, let's just the simple case where there's a machine manufacturing PC controlling that machine. Is it your predilections that, that PC either be upgraded, or its software be upgraded, or that there be an intermediary device that [00:30:30] sits between the ... Which to some extent the manufacturing people like, at least they've got two pieces of equipment that they know aren't going to touch, but have an intermediary device which can do certain things, and pre-process the information for the ... Maybe shunt some of it to a cloud environment for analysis?
Bill Ruh: Look, all of the current environment and manufacturing facility, or any of these industrials, this proprietary hardware had its place in history. It will eventually get replaced, however, [00:31:00] we live in a time where you don't free willy architecture, meaning you have to connect and utilize those things. It's really going to be a separate set of boxes that will connect. Over time what's really going to happen, is we're going to go to virtualization of what today is a lot of hardware, will get virtualized into software, and then put on these boxes.
A lot of this PLM, and these controllers, etc., it's a combination of hardware, software. It used to be all hardware, but now hardware, software, and eventually [00:31:30] it's like network boxes. We already see that a lot of networking's becoming software defined networks. We're going to see software defined PLM's and all kinds of things in the future, but that's a while away. You're going to see today the multiple boxes, and then you'll see it transition gradually.
That's the beauty of the technology, is you don't have to go rip and replace. You can evolve, and we have customers [00:32:00] who we're getting up in factories. I recently had one large manufacturing consumer product, good company, we did a factory of theirs in three months. They had seen immediate impact, but that's because you could bring these things together in a way that allows you to evolve into it, versus a revolution.
Ali Tabibian: In the case where you said we did it in three months, maybe give our listeners a little bit of a sense of specifically what was being done? What pieces were being added or tweaked or touched?
Bill Ruh: [00:32:30] We were bringing the data off those existing systems, and running it through Predix, and essentially at the edge and in the cloud. What we're doing when you look at that, is that we're analyzing all the machines across the whole production line simultaneously, and the operations. Then we're adjusting this and giving guidance to the people. In the end, people aren't going away, what we're telling them is [00:33:00] this is where the inefficiency is in the system right now. This is where you might have some downtime coming.
What's happening is people are becoming very targeted in their actions. That's changing the nature of work.
Ali Tabibian: That's a fascinating answer to me in the sense that there's a lot of work that can be done that doesn't require touching the workflow or disintermediating the flow of data.
Bill Ruh: Yeah, I would just add one thing to that ...
Ali Tabibian: Please do.
Bill Ruh: It was an interesting you brought that up, one of the things I'd say that I think most people, because they haven't gotten [00:33:30] beyond the piloting or prototyping of AI, and big data into real systems, is one thing they're underestimating, is the culture shift that's going to take. What I've seen is at a number of the early systems we had, we put them out there, and you still had a person in the loop. When the person got the input back from this AI process, which actually was going to enhance the system, you still had [00:34:00] somebody had to do something.
Well it turned out if they just looked, and if it agreed with what they thought they did it, and if it disagreed, they did what they were going to do anyway, you end up with no efficiency gain. The cultural shift of teaching people how to use AI in their job, I think we can see that you see younger people are more comfortable getting machines telling them what to do, while traditional workers, who have always worked on intuition and experience, [00:34:30] have become less comfortable, because they haven't had to do that.
I think we're foundationally seeing one change in the world that we're going to have to get over. For the last hundred years, we've had people tell machines what to do. For the next hundred years, machines are going to tell people what to do. People are very uncomfortable with that shift, because they know how they've done it forever. Their experience tells them things, but what they can't understand, is the depth of the information, and all the nuances to that, which [00:35:00] is if they did understand, they would change the way they think.
Because they don't, they don't have the full context that these AI and analytics have. As a result, I think we're not seeing all of the power come out of this, and this idea of a culture shift and the nature of work is going to have to shift as machines tell people what to do.
Ali Tabibian: The benefits of that cooperation, that way of thinking are evidenced as quite spectacular if you look at the airline industry. Who would've thought [00:35:30] you could move something at 600 miles an hour around the world, 30,000 flights in the air at any given time, with essentially 100% safety?
Bill Ruh: Yeah.
Ali Tabibian: Nobody would've guessed that was possible, but a lot of it is because the pilots have learned how to work with the machines.
Bill Ruh: That's a good industry to talk about that, that they've learned to work. Again, it's a disciplined industry who make sure the people get good training. There's a lot to be learned from that, and they make sure their [00:36:00] pilots are trained, and that's a place you can look at, respect, and figure out what you can learn from.
Ali Tabibian: Up to you what's really exciting, or something that people are not quite clear on it being around the corner maybe even a couple of years, or 10 years down the line?
Bill Ruh: Yeah, it's not really ... None of this is going to come as a surprise here to the folks who are listening. Certainly Blockchain in this is going to have a huge impact. The reason [00:36:30] is again, when you talk about the automation and industrial setting and guarantees and being able to track and trace and know what's been done, Blockchain provides a really interesting environment to not just connect machines, but to optimize the operations, and make sure you have full control over those operations and insight.
This is one that will get added into this Industrial Internet of things. That's probably nearer-term, longer-term, I don't think we've figured out how AR and [00:37:00] VR fit in this. A lot of great experiments, there are always really sexy things to go look at. The reality of it is that we have yet to figure how to make it work in a dirty, dangerous, dull environment. You still have people in hardhats, you've got grease, you've got all kinds of other problems that make it a little more difficult to utilize these tools today.
We're going to get there, but it's probably a more of a 10 year. Blockchain, next couple of years, AR, [00:37:30] VR, 10 years. When I look at some of the others that I think are going to be game changing, additive manufacturing has the potential to be game changing, because you may upend the whole supply chain. You may move a part, rather than you order a part, you order a design, and it's printed at your shop, and that's how you get a part.
Again, I think 10 years from now that is a huge opportunity. Those are a couple [00:38:00] of the things that I see that we have. The one thing everybody's got to realize, when I started this journey eight years ago, it was all right, it was going to be cloud, mobile, big data. Then it was cloud, mobile, big data, open source, Dev Ops, agile. Then suddenly you had to add on all kinds of analytics, machine learning, and AI, and cybersecurity. If you look at this, the one thing that's changing, is it used [00:38:30] to be one technology shift.
What's really happening now, is we've got an and technology shift occurring. These things build on each other, and when you think about the complexity, if you haven't got all the things I just mentioned right, the idea that you're going to get AR, VR and use it, fantasy. You've got to get those other building blocks right. We've got to the world we always wanted, building blocks, and the ability ... [00:39:00] Containers are helping us, Docker, Kubernetes, and things like that.
These are bringing technologies that are allowing us to do things we could never imagine before. The winning team isn't going to be the one that buys the best technology, it's going to be the one that has the right people to integrate the technology. When you talk about integration, it's a talent issue. There is only so much talent in all of those areas. You better have talent in all those areas, or you better [00:39:30] have a partner who is talent that you don't have to make this work.
That to me is the challenge that every industrial company faces, is how do you get access to the right talent and the right technology with the right partner? You can't get it all, and that's what's going to change. If you do, Blockchain, AR, VR, all the additive technologies are going to be easy to add on, but until you got the first set right, you cannot get the second set right.
Ali Tabibian: It seems like the key ingredient going [00:40:00] back to something you said below in knowing which talent you need, it has to be that domain knowledge in the end, right? Is that fundamentally the single most important filter everything has to go through to make sure that the outcome is relevant?
Bill Ruh: Look, the winners are going to have the best domain data and the best domain knowledge in this digital space. Look, over time, the domain data may be able to replace the domain knowledge, let's face it. Machine learning and AI in the next 20 years [00:40:30] is going to go places we can't imagine. Over time, whoever has the most domain data, I believe will really be the ones to be the most successful. Today, without domain knowledge, you can't really plan in this.
10 years from now, you'll start to see that power shift.
Ali Tabibian: Bill, and we appreciate the time you spent with us very, very much.
Bill Ruh: Ali, it's great to be with you again.
Jet engine: Jet engine.
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