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Participants:
Brian Regan; Host
Colin Parris, Senior Vice President, Chief Technology Officer, GE Digital
Introduction:
Welcome to Innovation Matters, the podcast about technology innovation, why it matters, where it’s happening and how it’s changing our world. Each week we talk with industry leaders and experts about topics ranging from corporate innovation to digital transformation to artificial intelligence and beyond. Join us for a provocative conversation about the future of business technology and society.
Brian Regan:
Why does innovation matter? There are a limitless number of answers to that question with enlightening and fascinating stories behind each one. Welcome to Innovation Matters. I’m your host, Brian Regan and in this podcast, I sit down with innovation catalysts from all areas of business, technology and academia to get their perspectives and insights and guidance and occasionally cautionary tales.
My guest today considers himself a data scientist first and foremost, a perspective and as I learned a passion that serves him well in his current role. Colin Parris is Senior Vice President and Chief Technology Officer of GE Digital, the GE subsidiary focused on industrial automation software and services. His dual role is to push innovation in GE Digital’s products so they provide value to customers and to scale those products for GE’s internal use. It’s a big challenge and one that got exponentially larger with the arrival of COVID earlier this year. We started our conversation with Colin framing his view of innovation and why it matters to him as comprised of three components: adaptability, sustainability and creativity with the first two being about evolution and with creativity, of course, being all about revolution. I find this approach to innovation fascinating because Colin views it necessarily through the lens of a company the size, breadth and operational protocols of GE. It’s a sharp and incisive take. We also talked at length about the role of risk and innovation and digital transformation and for Colin, it’s all about identifying the measure of the risk awareness, ensuring you’re fully aware of your environment; advantages, knowing your strengths and weaknesses; and then scenario planning, building out multiple scenarios based on that awareness and the advantages you’ve identified. This led us to COVID and a discussion of one’s tolerance for failure and for many companies, that tolerance recalibrated dramatically in 2020. Colin gives a wonderfully illustrative example with GE Digital customer NYPA, the New York Power Authority who was prior to March looking to accelerate its digital transformation. As he explains, it’s all about mindset. What was envisioned as a months’ long transformation suddenly became five days and with it, a more adaptive mindset with people much more willing to fail a little to get a majority right under unprecedented circumstances. The notion of failure got transformed into something different. Now that we’re doing these things, what else can we try? We also talk about what Colin calls humble AI. I’ll only tease it here as a journey through the data that minimizes risk and encourages adoption. And of course, given his own mindset and purpose, we talk at length about the importance of data and innovation. Listen up here for a fascinating view on the capacity for an organization to suspend culture rather than change it, particularly in times of human chaos such as COVID in order to transform successfully. And finally, I was particularly attuned to Colin’s repeated emphasis on the importance of narrative. What is the story behind our process? An insight frequently overlooked or neglected and I would argue that imperils the organization on its own transformation journey. And with that, let’s get to it. Good afternoon, everyone and a big thank you to Colin Parris who is with me today on Innovation Matters. And Colin, thank you so much for joining me today on this very busy Thursday just before Labor Day.
Colin Parris:
Brian, it’s my pleasure and honor to be here. Thank you very much for having me.
Brian Regan:
So, I’m going to start with a question I typically open with and it sounds innocuous but let me throw it at you. Why does innovation matter to you and how do you build it into your own thinking and particularly perhaps in the context of an organization with the breadth and size and scope of GE?
Colin Parris:
I think that is a powerful question to ask. Let me give you my simplistic view of it. In terms of why it matters, I think we’re thinking about three things when we think about why innovation matters. One is adaptability, the other one is sustainability and then creativity. So, in many cases, what you find is innovation is always around because especially in an organization like GE you have current products and you have current customers that you have commitments to. So, what you want is the ability to adapt to the changing needs that they find with their customer sets, their end customer sets or the changing needs in the environment or the changing needs in government policy as it applies to them. So, that notion of adaptability is one you always have to have and innovation is a big driver of that. And the other thing is sustainability. So, the game here is that while I’m adapting, there’s certain processes I have in place, products I have in place, services that I have in place I need to sustain. But I always have to sustain them at lower cost points or I have to sustain them at faster deployment rates because they’re now presumed to be vital to the customer. And so, you have to have these things going but it’s got to cost less. It’s got to be done faster. That’s the sustainability. And then there’s the notion of creativity. This is about the new big step changes that are occurring. Right? For instance, in our world we see renewables as a whole new cadre of capabilities opening up the ability to decarbonize the planet, to keep us safe. How do you deal with that in terms of creativity? Or you see new business model solutions that are showing up. You’ve got to have revolutionary things in place to do that. So, the first two, adaptability and sustainability, are all about evolution. The creativity in a sense is more about revolution. You’re going to use all three in every case but in some cases more dominant. In a revolution, creativity is more dominant. In evolution, adaptability and sustainability are more dominant. So, that’s why I think innovation matters and it has to be the core of anything, especially something like GE that’s been around for a hundred and something years. There are things you have to be adaptable and sustain and then there are new areas they have to be creative around. So, it is part and parcel of what we do as a viable company.
Brian Regan:
That’s wonderful. Let’s jump to creativity for a minute because it’s obviously a critical component of innovation. It’s usually one of the first things that companies think about in a sense shutting down in the midst of big disruptions and we’re certainly living in the biggest disruption probably ever from a U.S. perspective, from a global perspective. But creativity is risky and yet we know that companies who take the right risks really do emerge must much stronger. And you’ve talked about the need to encourage people particularly in these kinds of moments to come out of the mode of protection and get away from risk in favor of measured risk. What do you mean by that and how is that translating into what you’re talking to companies about today? And then by extension, where does the tolerance for risk come into that equation?
Colin Parris:
I think any discussion right now especially in the current environment around risk, any way you perceive it or measured risk, first starts with awareness. Right? In many cases, people presume risk as being anything that’s different. Right? I’m accustomed to a normal sequence of actions. I’m accustomed to a normal way things should flow. If something looks abnormal, then there is a huge amount of risk. Okay. While that may be true, now the discussion is is it abnormal for a short period of time or is it abnormal for a much longer period of time? And if in looking at that, those time scales, if you remain doing nothing and this changes for a long period of time, you incur an even greater risk by doing nothing. Right? So, here’s where you begin by looking at the awareness of the environment and that awareness gives you a measure of the kind of risk you’re thinking about. Right? So, I’m in a building and the building is burning. Is it risky to stay put or is it risky to move? Well, if I know the building will burn and eventually fall around me, I have to move. There is a lot less risk in getting out of there. There’s at least a path that makes sense that in staying in one place and hoping all of a sudden that the building suddenly stops being on fire. So, the first aspect of measured risk starts with having some measure of awareness. Then the second set of measured risk is all about the narrative that you have in the sense that as you see a situation, can I put that situation in light of my advantages? Right? So, for instance, quite a few people assume or some of the tools we’re using now, right, it’s risky in that well, clearly, we have something that’s impacting the global economy but in the same point of view, at this point in time in the narrative that’s happening right now, people are using more online tools. I have an online tool. Could that be used to my advantage right now? Well, the answer is yes. So, again, you’re measuring that advantage in light of that risk and it again becomes a measured risk. So, the awareness gets you a certain level of measure. The actual advantage you have gets you another way to measure. Now the next thing is what am I going to do? So, now that you know that you have the right awareness, you have the right set of advantages as you see fit and people have different advantages and different things. You may think that you are being hit in the most dramatic way now but you have to look through the advantages or relative advantages you have. And then the notion is what do I do? And that’s when the scenario planning plays out. That’s when you sit back and you say well, what if this risk goes to an extreme? The extreme could be last a long time or has this impact on your revenue or your profit. And you take four or five scenarios and you lay those out and what you usually come to find is that along that path, you quickly find that well, the difference between two or three scenarios for a small action, you could get a lot of gain or for a small action, you have a lot less risk. And now it becomes measured in the sense that well, I haven’t sat here and I’ve been frozen, hoping things change. I have gotten a sense of awareness in the right way. I’ve understood my advantages. I’ve understood a variety of actions I can take. Now I begin to plan a path forward and that’s the aspect. Every one of these has the notion of creativity in it. Right? Even trying to understand your advantages in light of that situation, it is a creative way that you tell that narrative not only to yourself but your team to get them thinking differently. Of defining your advantages, again, creativity as you begin to see it in certain lights, what it means to you. Finding the right scenarios, creativity there. So, there’s a lot of creativity as you think about this thing known as measured risk. The great thing about this is if you plan it and if you do it in times when risk is high, it becomes something that that becomes inbred in your company. And so, even in situations where you think the economy is going normally or growing, you still think in terms of risk. Is there a risk of a competitor showing up? Is there risk of an alternative solution showing up? Is there risk of me losing top people? And that mindset, that flywheel by which you look at risk begins to change and it changes your culture and things just begin to move at a little different place. Does that make sense?
Brian Regan:
It absolutely does. That’s wonderful. And two things, I loved your introduction of the necessity of a narrative which is fascinating and second, your reference to time scale because that leads me to my next question which is all about digital transformation. And we all know that it is a journey and not a destination but many of us were saying that 2020 was going to be if not the year then certainly a strong year for digital transformation, particularly in manufacturing, for example. And you’ve said the digital transformation is always about two things: changing business dynamics and leveraging a mix of technologies to better deal with productivity requirements. COVID has certainly disrupted the former or at least it’s put companies on far different business paths than they were on in January. But relative to how companies now need to learn to do more with less and also how particularly in industrial applications, they’re having to do things from farther away than they once were doing them. How is that kind of informing what you’re doing, what you’re thinking and what you’re implementing?
Colin Parris:
Oh, I think it’s doing a lot in terms of that. I mean this is huge. Brian, can you still hear me?
Brian Regan:
I can.
Colin Parris:
Okay. Great. So, let me give you some examples. Right? So, first, I think about mindset. All right? So, let me give you an interesting one. So, NYPA, I spent a lot of time for NYPA. NYPA is the New York Power Authority. And when you talk to their CEO, last year, I had a discussion with the CEO who was really talking about the fact that he wanted to move into the digital transformation era a lot faster. And when he would talk to his team, the team would say that’s going to take you four or five years. That’s going to take you a significant amount of money. And then all of a sudden, COVID shows up and what happens then is they essentially go relatively digital in five days because nobody could be there. They got a lot of people out of the offices. They had a few people in command centers. But the entire North Americas of the New York Power Authority in terms of the integrated smart operations center, they quickly discovered that they had the capability to actually manage this with a lot less people using some of the tools that we have been working.
Brian Regan:
It radically altered their own time scale that they had originally, yeah.
Colin Parris:
But I mean that’s the two things that it was [inaudible 00:15:58] and it was even more interesting all about. It gave people the ability to actually fail because when you do it in five days, you’re not going to get 100% right. Then you’re going to get it 70%-80% right and that was okay. Why? We’re in a crisis. 80%, right, is tremendous. And so, now people stop worrying about well, is it my job at stake, is that my reputation at stake? I just need to get this right because we have something we have to deliver to our customers. We have to give them power in this critical situation. So, the notion of failure got transformed into listen, do the best you can do. I’m going to look at that glass being three-quarter full and not a quarter empty. So, that headset changed. Then the second thing that happened is that well, okay, now that we’re doing some of these things and we’ve tried it, what else can we try? What else can we try right now just to hedge that risk? Can I try a few new things? Right? Can I bring in remote capabilities? Can I do analysis in which I could project what potentially might happen? Things that you wouldn’t try before, you’re trying again and again, you’re trying with that release that I don’t have to look at failure. Let me give you another one that really blows my mind. This was actually as a result of COVID. This is Posoco. Posoco is the Indian great operator in India. So, because they were about to shut down for COVID, what happened is that the Prime Minister Modi said as a show of support, we’re going to go into this entire lockdown. We want everyone, about a billion people in India to actually turn off their lights for nine minutes and light candles. Now again, quickly, the grid operators came together and said no, it can’t be done. The instability will be huge. No way. But listen, the Prime Minister said that and he gave them essentially something like four days to get this done. They worked with us. Guess what? They did that. We thought, it was 31 gigawatts of power dropped off their grid. It was ridiculous in nine minutes and they brought it back up and it came back up. It took us a week to work that one. So, when you looked at that, it was another idea that you had to do something different. Failure, okay, we’re going to just deal with what we have and we’re going to move this forward. So, that tells me two things. The first is that right now because of the situation, every time you have this extreme points as we saw in COVID, you’re allowed to try new things. You’re allowed to look at failure in a different light. The narrative around failure is different. The narrative is all about trying. Are you adaptable enough? It’s not a narrative about where you feel and how do you measure that. Then the second thing is that the technology was put to the test. It was put to the test and in many cases, it showed the value. What that caused then after that is that boards began to say well, listen, we’re seeing this thing happen and as a result of that, maybe I need to make these investments. Maybe I need to understand what these investments would be for the future. And so, once more, what you’re seeing now is that this digital transformation is speeded up. The mindset around it has changed because of one in which you can understand that it’s not going to be perfect but you measure it and you move forward. There’s one of more experimentation that’s moving forward. So, this has been a really unique experience for us and I think also a very good one and that has shown us what we can do. In manufacturing, this was another thing that showed up, in many cases, services had to be changed and people saw the power of services. Not only do I manufacture the asset but also can I service the asset in these remote times and that ability to pull the data, predict the failure, do remote type of adjustments to control systems where you can keep it running for a little bit longer because you knew the tolerance is a little bit more. All of that again highlighted the power of services and the connected digital capabilities even with our manufacturing systems because then you had to actually, at least if you manufactured heavy systems, that’s what you were doing. You’re using your services to keep these things running and managing the services remotely. In some of the manufacturing plants that do customer package goods, what you find is that they have to suddenly flip. Can I create more toilet paper? Can I create more paper goods or more meat products? And again, what they found is that that digitized capability we could orchestrate quickly on those plans and do it was again advantageous and with less people. So, I think even though it was a trial for manufacturing, once more as I mentioned, there are advantages that you look at and in the right narrative, you see trends that you can grow on in the future and you take those measured risks, invest in those and drive those forward.
Brian Regan:
That’s fantastic. And you referenced obviously data which is really fundamental to what you’re doing in helping industries find and deliver new business outcomes. Does that lead us to the topic of humble AI relative to how to understand how to use data in AI properly, when to scale it, when not to scale? It is that accurate?
Colin Parris:
Yeah. Quite so, quite so. This is in fact well put, Brian. If you think about our journey, the one in which we’re talking about the use of data, right, and then obviously the subsequent use. So, there are three journeys with data. First of all, can I get enough data to just visualize my state? So, a lot of people spend the time collecting the data and putting it up on BI or Tableau worksheets. So, just visualizing the designer state. Then after that, you want to figure out can I predict some of the things to get ahead of? Can I predict an early warning with any kind of problem I have with a possible asset out there? Can I predict an early warning of a fluctuation in my process in a plant? So, that’s the prediction. And then it’s optimization. How do I optimize different things? Can I actually optimize the use of electricity in my plan, less electricity while delivering the same yield? Can I optimize the use of raw materials? So, these are the ways to think about data. But as you think about them, you get to a point where these models that give you the capability to do prediction and optimization, a lot of these models are built on analytics and AI as we know them. But the challenge you have is that you have humans and humans are always worried every time you bring a model in place, what is the challenge I have to the risk to my business? How do I think about my business risk by having a model make a decision rather than a human? Because for whatever reason, we think well, I know these humans and these groups of humans have done it for years. I know them well. They’re going to make the right choices. We have that view. Whereas the model, oh, man, it’s math, it’s a combination of things I don’t know. I mean can I put my faith in it?
So, what we do now is we have this thing called humble AI and here’s what happens. Based upon the data you get, a model is only as good or useful as the data you collect. So, say I’m a wind turbine, I’ll give you that wind turbine example, and I collect a lot of data and the wind blows at a certain speed. Maybe the wind blows anywhere between five and ten meters per second and I collect a lot of data during summer time when it’s blowing between seven and eight. And so, the model I build in will actually be more accurate during the summer time because it’s built on data I used then. So, now I have that model. It’s actually quite good between seven and eight meters per second wind speed. Now winter time, the wind blows a lot, maybe in the situation, it blows a lot more. It’s nine to ten meters per second. That model won’t be as accurate. So, what humble AI says is that based upon the zone of competency of your data can I actually then understand that zone of competency and use the AI model because the AI model will be better. When I go outside that zone so between seven and eight meters per second, use my digital twin of the wind algorithm, outside of that nine to ten meters per second, go back to what I was doing before. I may have had an old deterministic model that I’ve used before. Go back and use the regular algorithms you have and then collect more data so that the AI systems become smart. So, the key of humble AI is that it knows when it’s right. It knows the right zone of competency. When it’s in that zone competency, it works. When it’s outside, it defers to whatever you think is best and it collects data and learns. So, it gets better and better. So, maybe next year in that winter season, it has enough data between the seven to ten meters per second so that you have the best working model.
Now what that does is two things. The first thing it does is that it reduces your business risk because you’re actually just using the model in the zone where it has the most competency. The second thing it does, it increases business adoption. When you explain that to many customers and the customers and the chief engineers, most important, see that, they say to themselves you know what? That makes sense. I understand what’s happening because yeah, that’s true. Within that zone, the wind is blowing this way. I can, in fact, run a couple simulations I have. I can even test it on a few systems. So, you both reduce business risk and you increase business adoption. And whenever you do that with the technology, what happens is that that technology gets more and more appreciated and it’s going to be put on more wind turbines, it’s going to be put into play. So, humble AI is really about a journey through the use of data in a way that minimizes business risk and encourages business adoption and we find it works well with the customers we have.
Brian Regan:
That’s wonderful. And it’s interesting you mentioned wind turbines because I frequently think of them as a great avatar for the increasing value of data and in an environment like a wind turbine farm, all of the stakeholders who increasingly want access to that information for different purposes. So, as innovation continues to drive better and better information through data, particularly as you’re describing it like AI, in a wind turbine environment, you might have the manufacturer, the municipality and other stakeholders who all see value in that data that is being aggregated off of that device and that collection of devices. And it’s increasingly posing new challenges, innovation posing challenges in how, who, when and where is that kind of data used and for what purposes. Just interested to know if you have any perspective on that?
Colin Parris:
No, I think you’re quite right. So, there’s a couple things there when you think about the fact that there are many different stakeholders or shareholders who can use the data in a variety of different ways and the challenges of using the right data. Right? So, it all depends upon the point of view of your reference. So, for instance, there are situations in which you have wind farms that are localized in the region and because the wind farms themselves are subject to the wind which is highly viewable, you have to couple them with other systems like gas powered systems that are more sustainable. Right? Because you just apply the fuel, quickly you can dispatch, in minutes you have the ability to dispatch a supply of electricity whenever the wind begins to dip and the same thing if you have solar. Right? There is occasionally cloud cover or it’s not a sunny day. Now if you look at that then, you look at that as a small ecosystem, right, it is a benefit to all if some data is shared. Right? If I can actually understand the profiles of the wind at certain points in time and what could be generated and the profiles of solar and what could be generated especially if that solar is on solar panels on the top of people’s houses and I have a gas turbine and I could understand when it might be used by sharing that information in a collective way, there is significant benefit because now what you can do is you can optimize it in such a way that maybe I don’t need to pay for a huge gas or wind or solar installation because I can actually see how I can balance between the three and I can utilize that knowledge. It’s better for the entire community. Right?
The other thing you worry about is that more and more people are buying these electric vehicles. And so, the minute that you buy an electric vehicle and you bring it into your house and you plug it in, it’s going to consume some electricity. Right? So, again and you’re never too sure when that’s going to occur. So, the more of the information you share, if all of a sudden in a community in a year, you have 400-500 electric vehicles show up, if it’s a small community, there’s an impact on it. And so, again, sharing the information could allow you to better deal with the situation especially if you want to avoid brown outs or other challenges. So, I think there’s a way to think about that. Sharing the information with the manufacturers also helps them understand a lot earlier where are the points of pressure or the points of failure in the wind turbines? Maybe what I’m looking at is based upon the loads I see. I’m detecting certain damage with the blades. Well, why are these things happening? Maybe I’m not pitching them correctly because I’m trying to collect too much energy at this point in time. Maybe there are things with the wind or there was a lot of storms and lightning strikes in the region. And if I could compare the demand, that full demand cycle versus what I’m trying to supply, maybe I can build more reliable grades at a lower cost so that the cost of electricity doesn’t go up for the neighborhood. So, there’s ways that that data comes back and helps the manufacturer as well.
Brian Regan:
Absolutely. And that’s very much the enlightened approach that I think all stakeholders need to pursue as this becomes more and more prevalent across industries.
Colin Parris:
Yes. Yeah. I know why some people are concerned about privacy as to what information they’re generating in their homes and everything else. And so, I think the trade-off for them is what’s the price of that? All right? If you want to protect that privacy, there’s the price and again, it’s the willingness of the person entirely if you want to protect that and you pay a certain amount more. That’s okay too. But that’s the trade-off you’ve got to make.
Brian Regan:
Absolutely. Let me ask you about culture and building the right innovation culture because I know you’ve spoken about that and in fact, I think you talk about three components, awareness, learning and resiliency if I’m correct. But I’m fascinated to understand in an organization like GE with its history and pedigree and legacy but with you very much focused on digital transformation, on how data can impact industries to deliver business outcomes, how does that drive and govern and shape the culture you’re building within your organization?
Colin Parris:
Well, I think it has a tremendous impact. I mean culture is one of those things that without it, I don’t think you can get the level of success you need and I think that it’s a key factor here. So, the two things I think about. I think you’re right. I mean I think a lot about levels of awareness, learning and resiliency but I also think about the practical because those are the foundations. But practically, how do you get it done in a company as big as GE and in which you have a lot of different experiences? And I think the two things that I keep in my mind, one is the notion of tying the business results or the operational results with this legacy of data. So, for instance, we’re doing this thing now called Lean Plus Digital.
Brian Regan:
Right.
Colin Parris:
And why are we doing that? Because we sit here and we think about the fact that a lot of the assets we have, we have a significant amount of aviation assets. [one-tenth? 00:33:20] of the power in the world is being created by actually assets we have, 16,000 scans per second in terms of our healthcare. You think about those assets and those assets in service. When you look at that, that means you have a lot of data. The data exists. All right? What you want to do is find ways to harness that data so that you can provide the best service to a customer. That service could be operationally. I mean certain operational parameters. But it also can financially affect GE’s profits and obviously, the profits or the revenues of the customer as well. So, what you want to do is when you think about Lean, Lean is all about how do I actually remove waste and removing waste helps both you and the customer. Lean is also about improving value. So, if I think about that, then you say Lean is based on what? It’s based upon KPIs, key performance indicators and data metrics. It’s based upon value stream maps. How do you get a value stream map? You look at the process and figure out where value is and you numerically figure that out in terms of the time it takes to do things or the quantity of inventory you use. All based on data.
Well, that same data is the foundation of what I love in digital transformation because that data is used to analyze problems, to give you a solution. That data can be used in the Lean process to be part of the action plan that gives you digital counter measures. That digital counter measure could be embedded in a solution in standard work for Lean. So, the Lean surfaces the data and the data digital techniques, the digital transformation is perfect to take that data and transform it so that you make the process better. And again, Lean is all about process transformation, business process transformation and at the same time, you are doing a digital transformation inside the business process transformation. So, what occurs is better business. So, the minute you get to that point, right away the GE culture looks at it and says oh, I see the value now. The value is and I can talk about warranty costs, I can talk about inventory cost, I can talk about performance that I can give to the customer. So, because the data and the actions of the data with the digital transformations are tied firmly into a process whether it be your services process, whether it be your contract process, whether it be your product development process, that combination of Lean and digital makes sense. So, I look to find those things that combine together that makes it easy for the culture to understand and accept it, especially since we are focused on again rebuilding GE to focus it where it should be focused. So, that’s one aspect of it.
The other aspect that I learn a lot about is when you have one culture, it’s really hard to totally change it all at one time. But what you can do though is you can suspend it and usually human beings suspend their culture in times of chaos or opportunity. In COVID, the notion was well, no, no, no, we’re going to stay at home. Our culture was not I’ve got to be working. I’ve got to be in there. I’ve got to meet. I’ve got to see my sports. We suspended that. We said no, stop. We had to do it. So, in terms of opportunities even startups, in terms of opportunity, it doesn’t matter what role you play. I’m an engineering guy. If they say go sell, you go sell. I’m a sales guy. If they say well, we have a service problem, you go fix it. In terms of chaos and opportunity, you suspend your culture and you do what it takes to work. At that point in time when you’re doing what it takes to work, if you see the value then, then that slips in and that also becomes part of the cultural heritage you keep. So, if you combine what we’re doing with Lean and digital in which we’re focused on what we need to do operationally and financially and then you look at what’s going on right now with COVID and this economic, there’s an opportunity here for the culture itself to be suspended and to actually now evaluate other things it should do and bring those things in as germane and as core to what it’s doing. And I think those two things come together in powerful ways. The third thing I’ll add is that Larry has come in and has been very clear about the things we focus on. Very, very clear about the transparency and the humility that we have to bring and the customer focus. And I think those are the ways that you get it to happen pragmatically.
Brian Regan:
That’s fantastic. Well, as I said at the top, Colin, I could spend all day talking to you and you’ve certainly cemented the fact that we could pursue any number of avenues on innovation and captivate an audience all day.
Colin Parris:
Brian, it’s been a delight for me. So, anytime you want to have a discussion, I would love to do that. Plus, I think the forum you have is that you can spread this word to others and I think when others hear it, they’ll create their stories as well. So, I think you have a much more important role than I have here.
Brian Regan:
Well, I thank you for that and I thank you again for your time, Colin, and for joining us today on Innovation Matters.
Colin Parris:
It has been my pleasure. Thank you very much for having me.
Conclusion:
Innovation Matters is a production of Actual Agency, helping businesses communicate in a changing world. More at www.Actual.Agency.
