The A and R in re:MARS — AWS Automation and Robotics

On this episode of The Six Five – On The Road hosts Patrick Moorhead and Daniel Newman sit down with AWS’s GM of Industrial IoT and Edge Services, Michael MacKenzie, to talk all things automation and robotics, which is the A and R in re:MARS.

Their conversation covers the following:

  • The roadblocks customers face and ways IoT can help
  • How AI, IoT, and robotics can come together for better customer experience
  • AWS’s ability to help automate and streamline business practices
  • Other exciting announcements

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The M in re:MARS — AWS Machine Learning

The S in re:MARS — AWS Space & Satellite

The Six Five On the Road at Amazon re:MARS

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Patrick Moorhead: Hi, this is Pat Moorehead, and we are here for another Six Five On The Road. This time at Amazon re:MARS. I’m here with my incredible host, Daniel Newman. How are you, my friend?

Daniel Newman: Hey, it’s good to be here, Pat. Love Amazon re:MARS. Very cool event. I think if you watched any of the other ones, you’ve already heard me, so I’m not going to dive into that too much. But man, everything from machine learning to space, with a little automation and robotics in between. Now I know the great part is when it all comes together. And Michael, here we are to talk about how this whole Mars things comes together for the industrial IoT.

Michael McKenzie: You bet. Good to be here. Thanks.

Patrick Moorhead: Yes, we’ve got Michael McKenzie with AWS. Michael, I’m going to go ahead, let you do the quick introduction. I’m going to give you a big question. You ready?

Michael McKenzie: I’m ready.

Patrick Moorhead: The big question is the hardest one.

Michael McKenzie: Let’s do it.

Patrick Moorhead: It’s probably going to be the easiest one, actually. But it’s the one that, everybody’s going to want to know. Give us the backdrop. Give us a little bit about yourself, your role, title, when you joined AWS. What’d you do before you landed here?

Michael McKenzie: Yeah. Right on. Yeah. I’m Michael McKenzie, I’m currently the GM for industrial IoT in robotics at AWS. I joined AWS about three years ago, before that I was a VP for a large automation and energy management solution provider, building out IoT applications and industrial IoT specifically. Before that, I was actually a solution architect out in the field, did a lot of years connecting systems. Got great stories from that period of my life. But if you could hang from it in a harness, crawl underneath it to get muddy, to commission it, if you needed a hard hat and high vis jacket and boots, I was probably on the site commissioning and connecting those things up.

Patrick Moorhead:  So as my dad used to say, you were doing real work. Because he always says to me, he goes, “You’re on that video, you’re not doing real work.”

Michael McKenzie: That’s right.

Patrick Moorhead:  That sounds like now you’re doing the us work.

Michael McKenzie: Yeah. Now I’m pampered and it’s a great job.

Daniel Newman: Yeah. It’s incredible how this market is playing out. I think we’re all old enough to remember that it was M to M, right?

Michael McKenzie: Yes. Machine to machine.

Daniel Newman: Yeah. That was the big thing that was going on. And one of the challenges was that, A, there were no standards. There was really, I don’t know, a cornucopia of interfaces and data. We really weren’t doing much with the data. And then we came into this, oh, we’re going to do this IoT thing. It’s going to solve everything. We’re going to have standards, connectivity. We’ve made a lot of progress, and we should pat ourselves on the back for about three seconds. But listen, you talk with customers every day. And they’re telling you what their challenges are. What are they saying to you? The board doesn’t have a dictate that says, “Hey, I want cloud.” They’re not saying, “We need some IoT.” No, that’s not how they’re saying this. But what are their challenges right now?

Michael McKenzie: Yeah. Customers are looking for digital transformation, and they’re looking for business outcomes with this. IoT is one of the architectures that helps to augment that, and helps to enable some of that digital transformation. And you’re right, we’ve come a long way from the machine to machine days, and commissioning sites that had really big ambitions, but almost no way to actually get there. To today, where it’s actually relatively simple to throw on some peel and stick wireless sensors to augment an architecture or an aging facility, or anything like that. So we can gather a lot more data.

But where customers are going now is, we went through that phase of, we drew devices on a whiteboard, we drew an arrow up to the cloud icon that was all drew. And we said, “There, IoT’s going to solve everything, and that’ll be our digital transformation.” And then we added on things like, “Hey, let’s do analytics and machine learning on this.” And that’s great, and that’s really advanced the art. To the point where we’re saying, “Okay, now let’s use all that insight. Let’s use that to close the loop back to the process and figure out what we can actually do with it.” And close that loop with an algorithm in the middle, or a human in the middle, however we want to do it. But to actually make change and take action is where they’re at now.

Patrick Moorhead:  Yeah. I think there’s a practicality that everybody wants to know, what is being done versus what is being said?

Michael McKenzie: Yes.

Patrick Moorhead:  And that’s been a big part of what I’ve enjoyed about re:MARS here, whether it’s been talking about real things happening in space, or the next iteration of conversational AI. And in this case, when it comes to IoT, and we talk to your partners at Nvidia and we were asking some questions and background about the big challenges. And it seems like the big challenge is that taking into practical at scale. For years we’ve been hearing about the promise of industrial IoT, the promise of industry four. You’re now, you’re not crawling around and in a harness, but you are flying around and seeing customers, at least now that COVID is slow and you’re able to get out there.

Talk about the use cases that you’re starting to see, the examples that are being deployed in the field that are maybe representative of some progress between the industry four I was talking about four books ago. Now [inaudible 00:05:27] at seven. Then I’m still at these events, I’m like, “Wait, we’re still talking about that.” What are we doing that’s new, unique and different that you can share?

Michael McKenzie: Yeah. Yeah. I think people have really figured out what the business outcomes are going to be. And so, where we used to talk about industry 4.0 as this complete game changer, absolute savior, and the total paradigm shift was going to happen. I think we are seeing progress towards that, but with very specific use cases. People are a little more pragmatic about it now where they’re saying, “I have energy and sustainability goals. How can I be more effective, more efficient with my energy, with my raw materials coming in? How do I monitor and track progress against that? And how do I close the loop back to the process to make that a reality?” Same for quality initiatives, same for some really cool stuff that we’re seeing lately with connected worker and connected robotics.

And what’s really cool to me, is some of the most interesting cases are changing the way we used to think about factory automation. We used to think about factory automation as being PLCs and drives, controlling motors and belts, and that was about it. Now with connected robotics and autonomous ground vehicles and robotic arms, things like that, we’re really seeing the entire concept of factory automation changing towards having these autonomous vehicles pick parts, carry heavy things from worker to worker rather than the worker having to go get them, or transport them between each other or relying on these other types of processes. It’s changing the way we think about factory automation. It’s changing the way we think about asset management in general across every sector.

Daniel Newman: Yeah. I’m going to editorialize a little bit here. Having grown up in all these different stages here, and then I look at the history of IT and what really got them moving. The plus is that we’re at least talking about reducing expenses, increasing revenue. I mean, that’s why businesses exist. We can talk about customer retention, but it all gets back to revenue or lowering expenses. And one of the reasons that it’s a little bit slower I think, is because particularly when it comes to brownfield environments, like a warehouse or like a factory, these are businesses that might have been in business for 100 years.

Michael McKenzie: Right.

Daniel Newman: There might have been a boiler that has been around for 50 years, that we found a way to get analog data out of it. But now we can put one of your modules on it and pre-predict. I don’t need a monthly checkup, I’m going to check on it when those parameters hit a certain level that I actually have to go out there. And that’s a cost saver.

Michael McKenzie: Absolutely.

Daniel Newman: And if you take that to the next step, you can even make your products better, and potentially even changing business models to where you’re not selling… There’s a company that I know that used to sell oxygen bottles, and now they sell oxygen as a service. They’ve instrumented this so much that they can make more money, get bigger commitment to their customers, and their customers love it. In a way, this is about CX and about getting back to that digital transformation piece. We’re here at MARS, which, it’s a lot. But I’m getting this sense, and particularly in the run up and everything I’ve seen, that you’re able to leverage every one of these. Maybe except for space, I don’t know. Maybe you can do that. But how are you leveraging all of these different areas so the sum of the parts is bigger than them standalone?

Michael McKenzie: Yeah, absolutely. Well, I’d go back to what you were editorializing is, it’s reducing costs, it’s increasing revenue. But it’s also improving worker health and safety, which is a big driver for some of the companies that we’re working with. And when you look at things, the way the machines have evolved and the way that this technology has evolved, we don’t necessarily have crews going machine to machine, or even analyzing dashboards for predictive maintenance anymore. We have machines reaching out, SMS-ing somebody saying, “I think I know my own problem. Here’s what it is. You should come fix me and bring this type of part.” So we’ve actually gone even further. And why have we done that? You mentioned space. We actually learn a lot from being in space, because we learn about truly disconnected edge scenarios.

Those then can be applied to things like ships at sea here on earth. We can apply them to critical manufacturing that can’t be without a connection, or without that inference engine running in the edge for more than five to 15 minutes. So we actually start to learn a lot from being in space. And we learn a lot from the advances that we have with machine learning and AI, that we can then apply to grouping concepts like digital twin. And this is where it starts to get really exciting, because we have all of this promise of IoT data that we can gather from all the different sensors that we have, and equipments. We have robotics out there that we can control. And when we bridge all this together into something like a digital twin, where we’re also adding simulations and what if scenarios. And figuring out, what are all the possible or probable outcomes from me making a small tweak here? I could immediately see results and understand, am I optimizing, or am I hurting myself? And that some of the whole coming together is really what becomes powerful about all this technology.

Daniel Newman: And the definition of synergy, the whole is greater than sum of its parts.

Michael McKenzie: Yeah.

Daniel Newman: I’m going to throw that out there because I remembered it and I felt super quick.

Patrick Moorhead: Michael, these events tend to come with some announcements.

Michael McKenzie: Sure.

Patrick Moorhead: We’re not a show that people just get to beat their chest, but we do like to give you a platform and we can talk a little bit about it. What’s the news? Spill.

Michael McKenzie: Yeah. In the last day we launched a new product called IoT ExpressLink. I think we announced that at Reinvent as a preview, it’s now in GA. And it actually just won best in show award for embedded design at Embedded World this week.

Patrick Moorhead: Congratulations.

Michael McKenzie: So that’s pretty cool.

Patrick Moorhead: And for everyone out there that doesn’t know what IoT ExpressLink is?

Michael McKenzie: Yeah. It’s a module that we’re working with partners and a series of libraries to enable embedded developers to treat cloud development the same way they would treat any embedded development. So they can listen for IOs on pins, and they can put IOs on pins. We handle all the networking, all the complexity of identity management, everything else behind the scenes to make them into cloud developers without a lot of effort. That’s a pretty cool one. We’ve also just launched Digital Twin in GA, that was just a month ago. We’ve seen a lot of really cool stuff coming out of our customers already. [inaudible 00:12:43] So many interesting things. Yeah.

Daniel Newman: … interested people are in Digital Twin?

Michael McKenzie: It is.

Daniel Newman: Sometimes it’s part of our job to separate the wheat from the chaff, and the digital twin part seemed like, I don’t want to say it was obvious. But the ability to manage and visualize things in the physical world, in the virtual world, it’s incredible. And it’s really one of the first true VR and AR use cases that actually has some legs.

Patrick Moorhead: Yeah. I think, actually, double down on that and say, it’s the maybe most pragmatic metaverse application. When you think about, it is going to be the industrial applications, we mentioned Nvidia. But the omniverse, the whole idea of autonomous simulated, replicated, synthetic data. This coming together to say, “Hey, we can develop faster. We can make safety happen faster. We can test faster. And we can do it all basically while we sleep.”

Now, again, there’s some work that has to be done. If you talk to the data scientists they’re going to be like, “Don’t make it sound like you can automate my job.” But at the same time, as we make that synthetic data more and more invaluable, eventually it’s like, we build the building before we ever break ground. We know how it’s going to work.

Daniel Newman: I’m sorry, I interrupted. I got [inaudible 00:14:02] all excited on GA of Digital Twin [inaudible 00:14:04]-

Patrick Moorhead: Wait, are there more? [inaudible 00:14:06] are there more announcements?

Michael McKenzie: No, not that I can think of. No, Digital Twin is exciting. I mean, you talk about the metaverse now, take the combination of Digital Twin that is live and operational combined with some synthetic data, things like that. But also combined then with autonomous robots out there, we could today start to combine these technologies into reducing human harm when we have to send them into difficult situations. Let’s say a nuclear disaster, something like that. We could have autonomous vehicles, we could have robotics out there doing things while we’re collecting live data from the site, and using Digital Twin to control it all. And we can do that today, that’s extremely exciting for me.

Daniel Newman: And what’s interesting too is this the social aspect of the digital twin. It’s funny, everything is easy once you’ve figured it out and you’re just thinking, oh, of course everybody knew this. But there’s also, I found, some solace in having a human in the middle. Let’s say the robot is doing something that it’s not supposed to be doing. And if you weren’t connected as a digital twin and couldn’t see what it was doing, you couldn’t do that. And I think we’ll never really… Never say never, but there’s no perfection. Heck, we saw Hal in 2001, there was no human in the middle to do something with that. I think that is another reason why I think people are really into this, because a lot of the brownfield companies, they’re not as comfortable.

They didn’t start, they weren’t born in the cloud. In fact, odds are they were born when electronics were vacuum tubes, or punch cards.

Patrick Moorhead: When you were 20?

Daniel Newman: Exactly. That’s a good one. That’s a good one.

Patrick Moorhead: Thanks.

Daniel Newman: I have the hair though.

Patrick Moorhead:  Yeah, yeah.

Daniel Newman: No, but comparatively.

Patrick Moorhead: No. It’s one of these things that just fascinates me about this here. Final question here I have to ask you, how do we accelerate this? How do we accelerate your… Not your business, but the market in general of the industrial IoT? We’re doing so many good things. We’re back to where we started, hey, let’s pat ourselves in the back from M to M, here we are, IoT. We tried to go horizontal again, that didn’t work. [inaudible 00:16:37] Then we went vertical and we’re like, well, that’s what we did in M to M. How do we get this going even faster?

Michael McKenzie: I think we have to go back to the concepts that work. And you mentioned some of the longer standing companies and some of the technologies they’ve tried over the years. I think the reason things like digital twin resonates so well is they’ve been using 3D images in SCADA systems for so long, so digital Twain isn’t that much of a leap. And when you have something like that coming through that feels familiar, but is also so much more powerful for remote access, it starts to shade that boundary of, do I care if it’s in the cloud or on-premise?

I don’t, because I have remote access, I have a familiar interface. And I can do the same things I’ve always wanted to do, but now I can do them from my kids’ baseball game. I think the way we make this all accelerate is by making it simpler, by making sure that anybody can build a twin, anybody can put a sensor on a piece of equipment and start gathering that data without a lot of effort. The more simple we can make this, the faster we get results. The faster we get results, the faster we accelerate these businesses.

Patrick Moorhead: Yeah. I’d love to see a lot more case studies out there. And I think sometimes, listen, I’ve had a lot of different jobs, product management, product marketing, corporate strategy. But when it comes down to case studies, don’t even have to cite the name of the company. I think what people want to know is, hey, a top 10 manufacturer had this problem. They needed to reduce their cost or increase their revenue. It’s totally blind and nobody knows about it. That’s how we’re going to get people on this. That’s where we get the board of directors talking about this. And the ELTs really… Because sometimes they’re so focused on things like supply chain and they’re trying to run a business, that all this extra stuff, it’s not computing. They don’t know they can even do this stuff. Even though we all do and we live in this, but we’re kind of not normal.

Daniel Newman: They largely want to be able to read off the problem and say, “All right, just put all this stuff together and make it better.” Most boards don’t invest in tech. And we don’t buy technology to solve tech problems, we buy technology to solve business problems. And so I think what you’re really trying to say here, to Michael, which we need to let him go. Is put all this tech together and let’s start telling that story of outcomes so that the world can hear that this next generation of IoT isn’t still this fairytale. It shouldn’t be like space, this is a lot more practical.

And by the way, a lot of what we’re hearing about space is getting really practical. Michael, we would love to keep chatting, maybe have you come back. Let’s talk about this again next year at re:MARS, once again.

Michael McKenzie: Great.

Daniel Newman: Thank you so much for joining us here-

Michael McKenzie: Thank you.

Daniel Newman: … for this Six Five On the Road at Amazon re:MARS. A lot of fun. You smile a lot, and I like that. And by the way, it’s good for camera. Stay happy, stay smiley.

Michael McKenzie: Right on.

Daniel Newman: Keep connecting the world. We appreciate you. And we’ll have you back soon.

Michael McKenzie: Thank you both.

Patrick Moorhead:  Thanks everybody.

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.


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