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In this Ecosystem Aces Podcast episode, Chip Rodgers is joined by Darren Blue Director, Industrial Ecosystem & Strategic Alliances at Intel
Darren has over 32 years of experience. His tenure with Intel spans over 25 years. He has donned different hats over the last two decades across Engineering, Financial Controller, Business Development, Industrial Ecosystem & Strategic Alliances. Currently he is working with Industrial partners and customers to deliver a worldwide ecosystem in the industrial market moving towards Industry 4.0.
Chip and Darren had a fantastic discussion surrounding technology’s integration into the industrial world.
Topics covered include:
Where do partners play a part in Intel’s strategy? 7:16
Different levels of deployment for different use cases - 9:23
How do you choose a partner for your business? - 14:21
How do you choose a partner for your business? - 14:21
What's IP ownership and what can be shared and not shared? - 17:13
How do you think about the go-to-market part of this process? - 24:01
What’s next with partners? - 28:47
Dealing with the challenges of partnering with other companies in the industry. - 30:53
Let’s get into the conversation!
Chip Rodgers 00:06
Hey, welcome back to another episode of ecosystem aces, I am Chip Rodgers, CMO at WorkSpan. Really excited to be joined today by Darren, happy to have you here Darren. Darren Blue is with Intel and director of industrial ecosystems and strategic alliances with Intel. So Darren, welcome.
Darren Blue 00:34
Hi, Chip. Glad to be here. Very interested in our conversation today. And hopefully, we can have some good conversations here.
Chip Rodgers 00:42
Yeah. Well,let’s introduce everyone to Darren a little bit. Darren has been with Intel for about 25 years now. He has an incredible career with Intel. And one of the things I thought was fascinating is Darren, your career, you started as an engineer for a number of years, I think five or six years and then went back and said, Hey, I want to get an MBA and sort of figure out this business, and move to the business side, and then got an MBA in finance.
The other thing that's interesting is that for a long time, you've been really sort of on the finance side, and controllers. You have been a controller of business units in areas within Intel and over the last, five, six years made another little bit of a transition into business strategy, and working with partners and all that. So, love to explore some of that, and hope to have some of that come out in our conversation.
Darren Blue 02:05
Sure. It's been an interesting journey with a company the size of Intel, that creates a lot of different opportunities, and has always looked at sort of business strategy, even in finance and monitor, move over more to, how do we enact some of that and move that forward with, as well as with some of our partner organizations that's part of what's necessary to make the ecosystem actually flourish. So that's really been an enjoyable part of my career, being able to do a couple of different things like that.
Chip Rodgers 02:41
That's awesome. Maybe we could start, Darren, let's talk a little bit about your current role. You are the Director of industrial ecosystems and strategic alliances, tell me a little bit about what you and your team are up to these days.
Darren Blue 03:01
So, if you look at what's happening in the industrial space now, we work primarily with manufacturers and energy producers, and the fourth industrial revolution has been going on. That is bringing some of the IT technologies merging into the OT world. And so as we work with those manufacturers. One of the key things that they tell us is, if they want to add some kind of functionality, they have to buy a specific device that does that. So as we look at how we move forward, what they want to be able to do is have some kind of infrastructure software that allows them to add software functionality to create whatever they're trying to do.
So now, I can deploy, I can manage through an IoT type of network, to be able to run my factory, to be able to collect my data, to be able to do a lot of different things. So it involves everything from data collection, to machine learning to video inspection, and quality control, and ultimately factory control.
So as you look forward towards an autonomous factory, closing the loop between that data extraction analyzation and then back into how do you actually make the factory do it something better, is what's working in and Intel has a good bit of credibility in that space, because obviously, we are a manufacturer ourself, and because of the type of manufacturing we do, we've had to implement some of these types of technologies ahead of any availability in the market.
So now as we look forward, we need partners and technology, both technology and business partners to move that overall industrial shift digital transformation forward.
Chip Rodgers 05:06
That's really interesting.
Darren Blue 05:09
That's what we're doing. Follow the coalition of the willing that helps move us along that path.
Chip Rodgers 05:16
Interesting. And something I just hadn't thought of, until you mentioned it just now. But Intel is a world class manufacturer, so you got so much experience in high tech, that doesn't get any more high tech. So, I suspect the technology you're applying on your own, factory floors, you're able to then take some of that IP, maybe you keep some for yourself. But then apply that to other customers.
Darren Blue 05:55
I mean, we're trying to take those learnings for sure. The things that we have are very specific to semiconductor manufacturing. And so we're trying to actually take some of those learnings and pass that along to the market, let those types of theories and use cases, and then even, to some extent, take things we learn externally, and can we bring back internal.
With any manufacturer, this transition is not a take some time, because when I have confidence, when I have testing, etc no one wants to have a blip in their factory or whatever. But everyone recognizes the need for this shift for the value it brings. And then even more recently, with the difficulties with labor, one of the use cases that we're seeing out there is how do I bring sort of a connected worker environment in so that I can train people faster. I have a lot of turnover, I have trouble getting people. So I need to be able to train them quickly and give them the most information. All those sorts of technologies have swirled around both internal and external to Intel for a number of years.
Where do partners play a part in Intel’s strategy
Chip Rodgers 07:16
Interesting. Your title is about ecosystems and alliances, and this podcast is all about ecosystems and alliances. Let’s talk a little bit about that. Where do partners play a part? How do you decide where you have something that Intel is going to deliver, versus bringing some technology or software from a partner?
Darren Blue 07:52
Sure, most of you are probably aware, most of our products are in the silicon space. So we're partnering with companies anywhere from like OEMs, like a Siemens or an ABB, people that just build devices and primarily build devices like a Dell or an Advantech, on any link to software providers, like a VMware Red Hat, even like a TT tech , which are smaller companies that do sort of similar things. And so we're trying to assist all of those companies in bringing solutions to the market.
And so with that, we have certain technologies in our hardware that can enable some of those types of solutions, as well as we've been providing some software in that space as well, to help accelerate this transition as much as possible. Because as you move that forward, it's good for Intel as well, because what happens is, as you are able to consolidate, so right now, like I said, there's a bunch of little devices. Ideally, you could start putting servers on the factory floor and run your entire factory off of, a server in the data center or the line wherever it happens to be.
So the consolidation of that compute power plays well for our products. So partnering with them to get those solutions into the marketplace is really important to us.
Different levels of deployment for different use cases
Chip Rodgers 09:23
Yeah. So the technology could be sort of very much at the endpoint, but there could also be some other technology in you mentioned servers on the shop floor. So are there different sort of stages or levels of where that technology is deployed based on the use case and the partner that might be involved .
Darren Blue 09:54
Yeah, absolutely Chip. I think there'll be a range of compute whether you know a lot of how fast the network is and how quickly something has to respond. There could be a need for a compute device right next to a particular machine or robot or whatever it is, because it needs to have a really quick response. Or if the response time can be a little bit slower, perhaps it is in the data center room, and then anywhere in between.
So for example, one of the things that we have done with one of the, and you can look this up on YouTube, or intel.com, with one of the German automotive manufacturers is, we did a use case where we extracted weld data while the weld was happening, they do a bunch of spot welds, and we're able to tell them in the first 20 milliseconds, whether that welds gonna be good or bad.
So a very, very high degree of accuracy. It was something where it's very difficult to have a human inspection to see whether that spot weld is good or bad. And so now we can provide that data to them. Ultimately, they'll know whether or not that body actually fits their specs or not. And we do 18 feeds at once through one single server. And that doesn't even sit right next to the robot. So, it depends again, on whether it's a direct link, or how the network plays out. But those are the kinds of things that are certainly possible.
And then, as we look towards the future, do you need to get faster? So if you're controlling the actual robot movement, you might need to be right there, as an example. But that's something where we deployed with the actual manufacturer, and then provided some of that information for other players to be able to deliver into the marketplace landing on an Intel silicon solution.
Chip Rodgers 12:00
Interesting, use case, where you're talking about, if you can tell if that weld is going to work within 20 milliseconds, how could you have an inspector? There's no way right, that sort of vision, human inspector could get that quickly, that accurate? And then you've got a quality piece of equipment that you're building?
Darren Blue 12:30
Eventually, right? The ideal state of that, and then we're not there yet is okay, if it's going bad. We detect that early enough, can you change the voltage, the current, whatever needs to be done to save it before the weld is, or something like that? And how valuable is that? So those are the kinds of things that we continue to look into, we've done a lot of work on, we're trying to do more work now envision you using a video camera to inspect different things. So it's been, we've done some early preliminary work on just if you're inspecting , let's say, a printed circuit board, are all the parts there?
Obviously, that's a very tedious thing for a person to do with a camera, we can do that very quickly, say that everything's there compared to the drawing, or it's this parts missing. And so we're actually trying to, we work with partners to deliver some of that, and we have some software that we'll be bringing to market as well in that space.
How do you choose a partner for your business?
Chip Rodgers 13:46
How do you think about partners in delivering, you're talking about a broad range of technology and applications and use cases and geographies? How do you think about where do you bring partners in? How do you pick which partners you want to work with? How are you sort of building those solutions together and bringing them to market? Talk a little bit if you could talk through some of that process.
Darren Blue 14:27
Yeah, we look at it from three key pieces. One, does their technology align with what we're trying to do? The second is, what's their business ability? How does that actually advance our business as well? And then third is, are they strategically aligned to us? Not necessarily, everyone has the same future vision as we do. And so, it's much easier for us to partner with somebody that has that.
So, we look at those three different things. Typically have some kind of objective or goal in a particular segment, be a manufacturing energy or federal or whatever it happens to be. And then, we've got obviously a plethora of companies that we work with, for silicon sales and things like that. Then is there someone in a list that makes sense for us to go?
Well, we need to start bringing more solutions to the market, because we see that in industrial, most of what gets sold to the person who's actually paying the final money is looking typically for some kind of solution. And so having an understanding of is that a solution that our partner wants to offer into the marketplace, or by themselves or with us or in front of us or behind us, whatever the case may be, is kind of how we look at it, and we have to re-evaluate that on a regular basis.
We talked to a lot of companies because of the multinational status of our business, but you're narrowing that down to try to get to a couple of the key people we want to need to work with, that's kind of the process we use. And, some of it even is okay, for example, if you know that use case comes from an end user, and they're like, Hey, we usually work with X, Y, and Z, can you partner with them. So sometimes we go out, sometimes it's dictated by the relationship that we have with the buyer. And they lead us to who we should go talk to.
Chip Rodgers 16:44
I guess it's a little bit of both sort of customer in and then and then at some point, maybe you're seeing a pattern of or you see, you say, Okay, well, this is really a great idea, a great solution for this customer, maybe it could apply to others. And then you think about broadening it into maybe creating a market around that.
What's IP ownership and what can be shared and not shared?
Darren Blue 17:15
One of the first things that has to be discussed is, what's the IP ownership and what can be shared and not shared after we're done. And that's part of whenever we're making these alliances that we've got to understand and the lawyers get involved, and make sure that we're all on the same page and start moving forward before some of the really deep technical discussions start happening.
So in most cases, it's not been too challenging. Mostly a lot of companies are trying to get their solution out there. That's what they need. And then, whether people take that solution that we developed, or they sort of piece together something they already have, it can go in both directions. So, as you look at the artificial intelligence world, and who owns the data, those kind of thing, I think we're those at the moment are kind of going one by one, but there's going to have to be some sort of commonality as we look forward and to make it go faster. And I wish I had the answer to that question.
Chip Rodgers 18:37
Yeah, really interesting. Just recent discussions with the chat GPT, kind of exploded on the scene in the last couple of weeks. And people are talking about exactly that, where does who owns the data that gets collected as people are asking questions, and coming up with answers and getting feedback into it.
Darren Blue 19:07
Yeah, and I think even as you move forward in industrial, and you look at the deployment of machines and service level agreements, and things like that, and how that might evolve over time, and sort of the model was, jet engines are sold on a use case, like a usage basis, you might see robots be sold like that in the future.
You've got to have, I think, there's probably in a business, the splitting of the data stream, like what can go to the let's say, the robotics manufacturer versus the company that actually owns the robot or leases the robot or whatever it is, and even just, pieces of data that can be split is an interesting conundrum in and of itself.
Chip Rodgers 19:59
What are some of the future applications around, especially in the industrial space?
Darren Blue 20:14
There probably are two that are bubbling up to the top most. As of late, I think there's a lot of this video inspection that's happening. I'm not even going to count that as one of the two because that's well underway, the two that are kind of in the future that I see more of are these automated mobile robots. So essentially, you're putting a robot that can travel around to different places in the factory and do different things. Not necessarily walking like we would envision a human but on, on a cart that moves around. That's one
And then second, would be this concept that we talked about a little earlier, where there's at least in say, North America, and Europe, it's challenging to keep enough employees now on the factory floor to keep them running the way that the manufacturers would like. So how do you deal with that kind of turnover? How do you keep them informed and trained as much as possible? So how do I get now that I'm starting to extract data in various ways? How do I get that data to messaging where it needs to be?
It's not just, I can't just send random data to someone and hope they know what to do, it's got to be, okay, this is based upon what's happening, this is what we expect, we need you to go do choice A or choice B. And we're recommending choice A, but you need to inspect these things first. So, it needs to not just be data, but it's got to be actually messaging.
And so sorting through how you take machine learning and AI information, and communicate that to the people that need to be able to utilize it is some work that needs to happen here to solve that problem. And obviously, it's not simple, it takes a lot of thought and work to be able to make that happen.
Chip Rodgers 22:25
And I'm sure you're testing this because you've come up with messages that might sound right but maybe they're not being received the right way? So things could go awry.
Darren Blue 22:45
Yeah and that's probably a step, an interim step. So as you figure out it's almost the same as the autonomous car development, the self-driving cars, you've got a driver there, who is obviously very experienced and trying to teach the computer and then tell you those algorithms need to get better and better so that they're feeding the right information back as necessary. And then eventually does the candidate actually take care of some of it, at least by itself?
So you can't in either of those cases, no one's ready to pull the human out of the middle. But, you know, that's part of the learning curve.
Chip Rodgers 23:38
Yeah. And not crash the car.
Darren Blue 23:42
Yeah, right.That's another IoT application that I'm not involved in, but you can certainly see the similarities between that and a robot that's supposed to figure out what I need to go do my own maintenance, or I need to fix this thing, or whatever it happens to be.
How do you think about the go-to-market part of this process?
Chip Rodgers 24:00
As you're working with partners maybe it's sort of a customer demand or at some market that you're thinking of going after, and then you build some solutions. Can the product teams get together and say, you're going to create this part, we're going to create that part, here's how it's going to be integrated. You've got something that's ready to go to market.
How is Intel and how do you think about that go to market part? So whether it's okay, let's build a marketing plan. Well, let's train and enable the sales teams caught up in co-selling, talk a little bit about how you and your team and Intel more broadly, think about that process?
Darren Blue 24:53
Sure. And I'll talk more obviously, the industrial and IOT space then the broad Intel . It usually starts with, we've got a use case, for example, if you've got a video inspection of a printed circuit board looking for all the resistors and transistors and stuff there. We've got someone who says, well, we need probably multiple people in industrial that can use that. We will talk to a couple of people that we think can provide that solution and provide some technical support. What the product specs might need to look like, together, a couple of them may develop it, and then we'll put it into, we would call it a market ready solution that would be available in our catalog as well. And we would then go do some joint marketing with that company once it gets through. And also we provide some support in that way. And then our Salesforce has access to that catalog as well.
So that if there happened to be a customer, who says, Oh, we're trying to find something that does this, they have the ability to look that up and go, Well, we've worked with these three companies, and they are providing something similar to the market, and maybe take a look at that. So really, it will work. When we put somebody on this path, we work with them to develop that solution. Make sure that it works properly and gets into our intel catalog. So our sales force has access to it. And we'll do some potentially joint marketing with them to make sure that it gets visibility into the marketplace.
Chip Rodgers 26:46
Interesting. It sounds like then there's some enablement as well, as a part of that process, like getting your sales team. Would your partner sales team be involved as well and sort of connecting the sales teams to co-sell together?
Darren Blue 27:10
Yeah, absolutely. Our partner can certainly request, hey, we've got this customer, we need your help with the use case, or closing the or whatever it happens to be, we're very supportive of that. And want to make sure that the Intel solutions are sold through the market.
So yeah, it could be, obviously, joint marketing material, or it could be we're on site on campus, whatever it is, with their customer trying to help close that deal
Chip Rodgers 27:46
So broad range of potential engagement.
Darren Blue 27:55
Absolutely, the enabling programs we have, this market programs, all of that has been in place for a little while, obviously continues to expand as the compute functionality gets dispersed more and more around the world.The compute intelligence that continues to be deployed continues to increase. And as people find new ways to utilize that functionality, it keeps going forward. And we've even scratched the surface on what we can do with AI and manufacturing. So, the belief is, that's gonna be a big growth area.
What’s next with partners?
Chip Rodgers 28:45
I could see that. Wow. What's next with partners? We talked a little bit about some of the applications are there. Are you expanding and some of the, the partners that you're working with, or certain kinds of partners that you're looking for? Maybe it's AI? Or where do you see expansion in the partner?
Darren Blue 29:15
I think the next what has happened thus far is still pretty point focused on I have this one problem, let me do that. Or I have this other problem. Let me deploy something. And so they've been sort of pretty focused. While we've seen now, in the next stage of this market transition is there the end users are looking for some kind of infrastructure or data fabric, if you will, so that now I can connect all these things together.
So for example, if you're in an automotive manufacturing environment, let's say, and you're putting together the body. And that data can be utilized by the paint shop if they need to understand, well, what happened here? And why is that? Why am I having paint issues or something like that? So instead of having some of the individual point solutions, once they start getting those in place, they're like, well, I need to be able to share this data across my entire network, or my entire factory, or even a factory
that's a different location. How do I do that? And so I think some of that is what's you're starting to see now.
So they want to move to that virtualized environment that we talked about initially, where I want to be able to deploy a software function, as opposed to having to buy a device. So I think that's the next stage. That's starting to happen now.
Dealing with the challenges of partnering with other companies in the industry
Chip Rodgers 30:53
Yeah. Just broader intelligence across the different rather than isolated use cases. It's sort of almost a holistic view of the application plus the data and how do they all impact each other?
Darren Blue 31:15
Absolutely and you're starting to see like IOT companies moving into this space. So all the cloud service providers Amazon and Azure, are moving closer and closer to the factory. Companies like Red Hat and VMware, we work with and they're adapting kind of some of their data center applications, can they provide that onto the factory floor.
So the IT departments in these organizations have familiarity with some of those types of functionality. How do they hit so they're trying to move that into the factory and work with the manufacturing orgs to be able to utilize that for their needs as well. So it's interesting, not only it's, the challenges is as much cultural as it is technological and those kinds of discussions, I'm sure.
Chip Rodgers 32:09
I can see that because you're talking about the whole industrial thing. It's like, you think of manufacturing, and it's kind of dealing with atoms Versus bits. And now how do you bring it into the world of virtualization and everything digitized?
Well, this has been fantastic. Any thoughts or words of wisdom to our audience in terms of thinking about partnering? And your unique background starting in finance and risk management is an interesting one, anything that you'd want to share with our audience for making partnering work?
Darren Blue 33:10
Yeah from just a partnering perspective, where we've been where I personally have been the most successful is when our strategies are completely aligned for where we see the future going. And then it's a win-win for both of us as we move forward. Whenever there's a lopsided piece of that relationships, that makes it a little difficult, structures a little different.
So working through that, and how to make that profitable for everyone involved is good. I think where this space is moving forward, and the value is coming out. There'll be a lot of benefit to all the industrial companies and as we move forward in that space.
Chip Rodgers 34:04
Great advice. You're right, if you have a partnership where there isn't a pretty good balance then get one partner that's chomping at the bit and ready to go and the other is kind of like, it's not gonna want to see the both sides making the investment. So that's terrific.
Darren Blue 34:32
Yeah, and in technology, one of the challenges is sometimes our partners are also our competitors in other spaces. So that's always a challenge. I may think it's ultimately we work with areas where we work best together and move everything forward.
Chip Rodgers 34:55
Good, fantastic. Darren, thank you for sharing your thoughts today. I really appreciate you joining and really interesting applications in the industrial market and you have your hands full and a lot on your plate.
Darren Blue 35:17
Thank you very much for a good chat and I enjoyed being here for a good discussion and I look forward to seeing your feedback.
Chip Rodgers 35:27
Awesome, great.. Well with that I think we'll sign off and say goodbye. Thank you all for joining another episode of Ecosystem Aces. I'm Chip Rodgers for Darren Blue. Thanks, everybody for joining. Thanks Daren.
To contact the host, Chip Rodgers, with topic ideas, suggest a guest, or join the conversation about modern partnering, he can be reached on Twitter, LinkedIn, or send Chip an email at: firstname.lastname@example.org