Ben Sheppard  00:15

Hi, welcome to the TX – Tomorrow Explored podcast show. I’m Ben Sheppard, Managing Director of TX. In this podcast series we dive into Web3 technologies and their role in the emergence of data economies, with guests from some of the most forward-thinking companies from around the world. We talked about innovative ways of engineering data to create value and the next generation of Internet technology, including blockchain, decentralization, AI, and machine learning.

Ben Sheppard  00:49

Hello, everyone, and welcome to another episode of The TX – Tomorrow Explored podcast show. I’m Ben Sheppard, Managing Director at TX. We have a fantastic guest on the show this week. We have Michael Rose, who’s the Chief Strategy Officer at Arcadis Gen. Arcadis Gen is part of the Arcadis group of companies, which is a multidisciplinary engineering firm working all over the world. Mike, welcome to the show, great to have you on how are things?

Michael Rose  01:18

Thanks for having me. It’s a pleasure to be here. I know this has been a long time in the making. So, I thank you for your patience and persistence with us. You know, I think COVID did not make things easy for anyone and in particular, as we sort of stood up Arcadis Gen, you know, lots of turbulence in our market, in our organization, with our clients. So, yeah, really pleased to be here today.

Ben Sheppard  01:47

What’s the COVID situation like in the UK, now? Is everyone still locked down there or are you getting back out at all?

Michael Rose  01:56

I’ve been out a couple of times, but in case you can’t tell from the accent, I’m based up in the northwest of England. So, we’re under some quite severe restrictions at the moment, we really saw a high spike in numbers. So, thanks to Boris and his new tiered approach to COVID management. We’re sort of in the middle stage of lockdown. So, in order to cope with that people that you live with, it’s quite a sort of tough situation, really, but I think, you know, for me, from a business point of view that makes what we’re doing around technology, and sort of supporting our customers on a day-to-day basis even more important.

Ben Sheppard  02:39

Yeah, I got sent quite a funny meme actually a couple of days ago as a picture of the UK and there was a line through the Midlands, and in the top half, it basically said, buggered in the south we’re okay.

Michael Rose  02:55

I think we’ve evolved that now so that you know, we’re in a really good part of probably don’t want to go in there. We’re still all right there that. It is what it is. It’s difficult for everyone. I think it’s about just being kind to one another, isn’t it and patient, particularly for me as we go into this winter period, taking time for some mindfulness and sort of personal wellbeing rarely.

Ben Sheppard  03:25

Yeah, definitely. So, as you know, not many of our listeners probably will, but I used to work in the engineering world. I was at how CRO and CH term hail and Atkins and I’d worked with Arcadis in the Netherlands when I was working with rights water start and that which is the Dutch government, Arcadis Gen wasn’t around then. So, okay, this Gen is, I think, a relatively new subsidiary. Can you just tell me about the relationship between Arcadis Gen and Arcadis so that I can sort of understand how it fits in that big group of companies, because I think what globally you must have more than 70,000 people, I would have thought in Arcadis seven years, to that high.

Michael Rose  04:12

Maybe Peter and Roland would like us to have 70,000, but we’re about 30,000, globally. Predominantly served in sort of the Americas, Europe, Asia, and Middle East. Predominantly with kind of program, project management services, cost or commercial services, and design and engineering services. Arcadis there was that you say some sort of professional services-based organization really focused on infrastructure customers, utility customers, and also building customers. Over the last sort of four or five years, whilst I’ve been with them, they’ve been on their own digital transformation. Invest in an [Inaudible 05:01], lot in that people to upskill them to understand how to make best use of digital in providing those professional services and to also sort of obviously keep pace with the market. Probably about 18 months ago or so, the decision was taken to kind of bring together a number of capabilities that existed within the Arcadis family to focus down on creating a sort of a digital product-based business, that will be a platform for growth. For future innovations within the organization, we’re wanting to kind of pull together all of our experts software capabilities, knowledge of the asset lifecycle, to ultimately create products that could be consumed by organizations, asset owners, asset operators, that Arcadis traditionally couldn’t reach as a professional services organization, to really looking at how do we productize our intellectual property, our ability to use technology to deliver ultimately outcomes into the built environment, improve the quality of life. So, you imagined that kind of Arcadis our investment vehicle, we’re set up to kind of rapidly scale product-based revenues, deliver customer success, and ensure that kind of our customers can really make use of the data that they’ve got the knowledge that we bring, and technology to achieve their business outcomes.

Ben Sheppard  06:39

Yeah, I mean, for me, having a lot of familiarity with that particular industry, when I look at Arcadis and what you guys have been up to over the last few years, I mean, you look like Trailblazers for engineering companies in terms of digitization, and really embracing all things around data. When Arcadis Gen was launched, I saw sort of the promotions of it coming out on LinkedIn, and I thought, wow, these guys, they’re really front runners on this, you know, you’re not seeing the other big engineering firms like Acalm or Jacobs doing this sort of thing, or at least it’s not visible to me. I thought, wow, this is a real stamp or marker that is saying this sort of direction of this company. Yeah, I remember, even when I was still at the engineering companies, there’s a lot of talk then of, you know, what will the future look like? You know, will Google be buying up the civil engineering companies and will be owned by big technology firms all over the world or will civil engineering firms spin up their own technology arms and there was always just this big question mark of which route is it going to go? Sort of seeing our Arcadis Gen launch, it was like, okay, there’s their roadmap, they are going to build the technology arm into Arcadis, they’re going to put it at the sort of DNA of the things that they do and by having these products, as opposed to just the professional consulting service, but actually having physical technology products, I thought that, wow, that’s really impressive, you know, they’re really going down that route. So, it will be interesting to see how your competitors change in the coming years with the ever-changing landscape and the way the world sort of embraces technology.

Michael Rose  08:28

Yeah. I mean, I think, for us the technology that, you know, we look to bring to market and what excites me about the opportunity, really a sort of a personal level, and being part of this leadership team at Chad was almost the ecosystem that we’re able to create. So, you know, we understand our USP, you know, it’s about deep asset knowledge, and how to apply that in particular sectors. Adding into that, you know, the use of ecosystem partners, whether that’s our relationship with an IBM technology stack, or some of our advanced analytics that we’ve bought from kind of academia and data science, kind of really sort of tipping all of that into a melting pot to create something that’s not specialist to sort of sit on a kind of a pedestal to be kind of admired and only accessible by the few, but really, to kind of democratize that use of technology in the built environment, who was really all ambition is and that’s what excited me about the opportunity to be able to make use of the data that we have make use of the data that customers have, harness it in the you know, the leading technologies for specific applications that’s the intent. That’s the way we’re going and really, I think, the gap that we saw in the market that perhaps the holders you know have not been able to fill in the sectors that we focus on. Connecting that technology across the entire lifecycle, whether it’s from the very awful kind of conceptual planning of a piece of infrastructure, whether it’s in how that piece of infrastructure is ultimately delivered, or whether it’s the operation of that, that infrastructure, all of those things are kind of interwoven into sort of our technology stack and product offer. You know, I think it’s that uniqueness that, you know, will help us apart from our competition.

Ben Sheppard  10:33

Absolutely. I mean, I think, it’s an absolutely critical role, you know, you get the traditional engineering services, you get the new technologies, particularly the data driven technologies and there needs to be that organization, that is the bridge that brings these things together. That’s what Arcadis Gen is doing. It’s not only providing technology products, but it’s providing education and the knowledge to these traditional clients and industry so that they can understand how to use it. That’s very similar to kind of what TX does with Web3 technology providers that want to take that advanced, decentralized or blockchain related technology and bring it to industry, which is why we originally got talking like how can Arcadis Gen and TX be the bridge between governments, you know, big traditional developers, and this really progressive technology that’s out there? How can we somehow work together to educate, you know, these different parties on this and make it so that it’s not just this world of wacky language that people don’t understand, and it sounds good, but absolutely no idea what any of this means, but actually break it down into those chunks, where it’s like, oh, now we get it. That’s what blockchain really is and that’s how you’d use this decentralized platform, and so on and so forth.

Michael Rose  12:01

I think the other thing that we particularly focused on and learn as we’ve thought about the type of organization that we need to be to interact with, you know, blockchain organizations or cloud based organizations is the culture that exists, and y’all have seen this from your days, whether CH is, you know, engineering firms can stereotypically have a particular way of being, it doesn’t necessarily kind of indoctrinate, kind of rapid ways of experimenting, or innovating in a way in which this all technology sector as it has been accustomed to. So, a big part of what we’ve certainly focused on is creating the right type of culture, to kind of really underpin that bridge that you talk about, so that, you know, when sort of our conversation started, when the people that work with me, interact with their ecosystem, you know, we’re behaving and acting in a kind of a complementary way, bringing kind of balance and continuous learning and sort of particular sort of accountabilities and clarity to the conversation. So, I think that’s equally important, you know, it’s all very well kind of are in deep asset knowledge and being passionate about technology, but bringing the right type of individual to the conversation is kind of being really sort of fundamental. So, that’s how we sort of build the organization.

Ben Sheppard  13:30

Yeah, it reminds me of something that is quite funny that happened early in my career where one of those engineering firms that I worked for decided it needed to be more forward thinking. I remember being sat in a meeting room like, right, we need some of the more creative people in the company to come up with some really good ideas on how we can be, you know, front runners on this stuff. Yeah, let’s set up an innovation team and they can be the innovators. Like, you’ve got a pool of 6000 people, and you’ve decided those four are the ones that are going to innovate for the entire organization and that no one else’s opinion matters, because it’s those four that are the universe. It’s not anyone else.

Michael Rose  14:19

I have to say, I mean, I think that’s one of the things I admire about the organization that I’m in at Arcadis, more generally, you know, we very thoughtfully I think anyway, so I’ll set about, you know, a process of standardization across the sort of 30,000 people globally, looking at how [Inaudible 14:40], is kind of automated and ultimately getting to a point where we can sort of productize that capability, you know, means that everyone’s got a role and because that kind of our overall mission is shared. You know, I think everyone plays a role in innovation. Particularly, you know, when we think about some of the big complex challenges that society faces today that we contribute to, you know, for people that aren’t going to solve climate change.

Ben Sheppard  15:11

I can think of one that might ruin it, though. So, you know, obviously one of the reasons I reached out to you was because Arcadis Gen has this passion for data and TX, obviously, has a passion for data and we wanted to see how we could work together, we wanted to IDA on different possibilities in there. I obviously spoke to a number of people in your team, we had some fantastic calls. It was actually just to comment on that it was really positive as well, that you could see, whilst those people that you brought into the call came from different parts of the business, it was absolutely seamless and you could really see how the mission of Arcadis Gen was so clear to them all, because you’re all very much pulling in the same direction, but I don’t know how often you actually work together with those particular people, but it was a really seamless discussion in the way that it was led and it was really good to see that, but I could also see, okay, Arcadis Gen has the same passion for us as data. Could you just expand on that, and tell us some of the things that you’re up to in relation to data with Arcadis Gen, what are some of those, you know, trailblazing projects that you’re involved with?

Michael Rose  16:28

Yeah, sure. I guess a thank you, on behalf of the team, that you could see that sort of alignment, and that sort of shared common mission, I think, you know, we’ve got an incredibly diverse team, you know, lots of different backgrounds, but I think the way that sort of comes together is kind of part of our secret sauce. So, as I said, you know, a lot of what we do is focused on the utilities and infrastructure sector, and that is probably some of our best examples of using data. So, for instance, we’re working with a large water company and have been for a number of years in various guises to really help them understand what their asset risk is, where there are vulnerabilities that their customers face. Using both our knowledge of how assets perform, also, their data in terms of the way assets are degrading within that that sort of particular environment, to understand what investments are required to continue to provide service, not just kind of over the next couple years, but also way into the future, so that the services that they provide their customers are resilient. Working with that particular company, we’ve been able to optimize their investment plans, consistently to help them both outperform the expectations of the regulator, and the customer.

Also, to ultimately offer reduced bills to society and also improved shareholder return. Working with that customer, most recently, one of the things I’m certainly most proud of is the way we begin to incorporate real time data into our work with them and not just kind of their real time data, but real time data from multiple sources. So, bringing in weather information, to help contextualize what’s going on in the environment and the assets. We mix that weather data with our asset models, and their maintenance schedules to really begin to understand demand in the system. So, that they’re able to predict over the course of the next week, what water they need to produce. By giving them that ability to use or machine learning to predict water demand. That gives them a huge number of benefits in terms of protecting valuable water resources. Making sure water is in the right place, at the right time for the right type of customers and ultimately, that’s then enabling them to be efficient and effective, not just to manage demand in the short term, but also to manage demand over the longer term. That project in particular kind of came out of, I guess a number of lessons learned rarely in the way we’ve seen the environment change, whether that’s been the sort of the most spoken about kind of beast from these, the UK suffered a couple of winters ago when everything froze, and our systems were proven not to be particularly resilient on land, you know, remarkably, we got, you know, an incredible solar and kind of, you know, drought triggers were being hit all over the place, to really thinking about, well, what have we learned and then how can we use data from a variety of different sources to solve those complex problems? I think it’s a great example of when you can bring technology to life with asset knowledge to ultimately deliver, you know, an essential, absolutely critical work service.

Ben Sheppard  20:24

I think that’s a great example of where, you know, having access to different data streams to help inform the way a project should be executed is a really good one. And, you know, obviously stream has this data marketplace, it has data streams from a variety of different products that aren’t necessarily related to one another, but actually, when you’re building up a project, like the one you just discussed, sometimes it’s not the data that’s directly in front of you that you need, it’s actually the other variables that are out there that might affect what it is you’re trying to deliver as a result of that project. So, weather data is a great example of that. You know, it might be that you could also start to use if it was a particular tourist hotspot, you might actually want travel data, you might want hotel bookings data so that you can see, okay, the demand is likely to increase by this based on the number of bookings that have been made rainfall, according to the weather data is down, we then have a deficit, how can we manage that situation? You know, by having access to those different data streams, that’s when you can really deploy some fantastic machine learning and AI models to get better prediction. I think that probably lends itself well to this bit of a rise in what we’re seeing around data marketplaces. Yeah, okay, we’ve seen some at government level at local authority level that haven’t necessarily taken off yet, but I don’t think it’s not because they’re effective, I think it’s more because people are still getting used to this new paradigm of AI and machine learning type models, and that project is as a fantastic example of that.

Michael Rose  22:11

Yeah, I think, you know, you talk about the paradigm of AI have been one of our missions, in particular has been to make AI and machine learning kind of accessible and applicable to our customers. You know, it’s much been spoken about, and certainly, you know, I’ve seen during the course of my kind of time in industry, obviously, the exponential increase in data, you know, from a whole myriad of different sources, whether it’s sensors, whether it’s prediction, you know, whatever it happens to be, and really what we’ve looked to do is to find ways of deploying AI on very specific problems that our customers face, so that they can have confidence that the way the technologies working is ultimately going to deliver the outcome, the improved service, because we’re quite often talking about safety critical systems, whether that’s, you know, the maintenance regime that needs to be applied to a particular type of train or the track that the train operates on the work that we do, you know, as a kind of a fundamental bearing on public safety. So, we’ve got to make sure that the AI that’s deployed and the data that you used is robust, is assured, and, you know, he’s going to work all the time.

Ben Sheppard  23:35

Yeah, those models need a lot of data before they become robust and if you’ve got, you know, critical things, like you just said, that are relying on that Michael, train safety, then, you know, you really need access to plenty of data so that you can sort of identify the issues in that model. So, yeah, okay, that’s pretty interesting. Do you have any other examples of AI or machine learning projects that you’re doing in Arcadis?

Michael Rose  24:02

So, one product that we just launched, and I guess it said, that kind of simple end of AI is around kind of how to handle organizations optimize their investment portfolios. One of the things that we have seen is the typical way organizations can make choices around their investment, you know, has over relied on spreadsheets literally. Perhaps it’s even relied on, you know, he shouts loudest or she shouts loudest, to sort of make the right investment decisions, but I think, you know, particularly in a kind of a post COVID world, you know, with resources under pressure, whether that’s, you know, people resource or financial resource boards want more confidence that they’re making the right decisions, and that the money that they’re spending is ultimately pointed at the right business outcome. So, we just launched a really rapidly deployable product, it’s called the Universal visual optimizer and that really helped organizations take the data that they’ve got, and understand the benefits, that the investments that they want to make, will deliver, and start to kind of run scenarios around there, using some sort of AI algorithms to continuously optimize the decision making process, really simple to deploy literally takes kind of a matter of days to deploy, where it’s kind of historically, that type of technologies take months, if not years to kind of get embedded into organizations. Then by being able to run scenarios and visualize those for kind of non-technical people, it becomes really obvious and apparent, what the right sort of mix of projects are and ultimately, then, you know, allows them to move into delivery and kind of get the supply chain going and we’ve seen that deployed, you know, with particularly good effects in a number of different settings, as organizations that kind of come out of this horrible lockdown situations that they found themselves in. They’ve literally been spending, no Capex for, you know, whether it’s a period of, you know, six weeks or six months, you know, they’re like, well, how do I now achieve the outcomes and the obligations that I committed to having last kind of, you know, 6-12 months of the year, while is the right way to kind of re-mobilize my organization. So, I think that’s been a real sort of pertinent use of AI in response to COVID. That’s a technology that we’re deploying to the cloud, again, to sort of rapid deployment.

Ben Sheppard  26:51

Yeah, I mean, information at your fingertips, so that you can make those critical sorts of business decisions is really important. We’re in a slightly different world at the moment where it’s not as easy to get out and undo the Capex activities as it was, or even the apex activities as it was. So, that’s going to have a major impact on different assets, particularly those that needed care and attention, perhaps more rapidly than others. So, there’s obviously consequences to those delays, and then becomes the decision of, I guess, in the worst case, do we replace the piece of infrastructure entirely or you know, do we just continue to maintain this thing for a bit longer, and until it needs to be replaced? So, I can see that there’s a lot of opportunity for optimizing your investment decision just on that. I guess that lends itself to the whole life costing process really doesn’t it that you sort of undertake when you’re putting these infrastructure projects together and presumably, if you’re using your universal visual optimizer, I remembered it, not a catchy name, I might add but…

Michael Rose  28:12

I will take this feedback back to Mak.

Ben Sheppard  28:14

But if you could actually take the data that has gone through, that model and look at trends on similar assets in the past that can actually then help you inform how you might build something in the future and you can build that into your whole life costing process when you’re actually going for a new piece of infrastructure as well. So, I can see benefits, not just in the delivery phase, but actually forecasting for the future as well.

Michael Rose  28:42

100%. I think, you know, BIM provides a tremendous platform to kind of link all of that through the lifecycle as one of the things that we’ve looked to take advantage of, and really kind of title, that whole kind of decision making process from the very sort of strategic decisions that our organization makes through to those kind of tactical decisions that it makes kind of in month in year, ultimately, that into the sort of the day to day operational phase, and really link all of that data together to make as you say, those kind of optimized whole life decisions every step of the journey, because decisions that you make up front ultimately have a significant bearing on the total cost of ownership. So, Gen’s mission and Gen’s kind of product families that is ultimately all kind of orchestrated around driving to sort of the lowest total cost of ownership.

Ben Sheppard  29:39

Yeah. Just thinking about the example, you gave earlier about, you know, bringing different datasets in the water example that we gave and how that might inform how the project is built. I mean, given the size of Arcadis, you sit on an absolute wealth sort of information across so many different disciplines that actually just unlocking that data internally, you could build some amazing models off of that, because you’ve got decades of experience, but obviously, it’s not as easy to do as what I’m simply saying here, but just to unlock all of that internally and bring it into some sort of data science team, I’d imagine that, you know, there’s a wealth of models that can be built off of that and I guess that’s part of what Arcadis Gen is doing, isn’t it?

Michael Rose  30:32

Yeah, for sure. A lot of what we bring to market, those data models like you say, you know, I think our secret sauce around them is yes, we have our particular point of view on them and they are built on, you know, 130 years of kind of engineering heritage and knowledge and, you know, all sorts of elements of the built environment, but our ability to kind of listen to the customer empathize with their situation and consume their data into our models, ultimately gives them the confidence that the outputs from those models are reflective of their circumstance. So, yes, we come with a, you know, pre-configured point of view around how an asset deteriorates, what performance is capable of delivering, but ultimately, you know, y’all have seen this, you know, people need to be convinced that it works within that particular setting and whilst, you know, I certainly hold a view that, one piece of water network is largely similar to another, they’re not entirely the same. So, being able to understand what variables matter, and use the right types of data ultimately ensures that the outcomes that we deliver are sort of optimized for that particular geography or that particular sort of market segment.

Ben Sheppard  32:01

Having lived in a few countries around the world, I can say hand on heart, the tap water in Finland is by far the best tap water I’ve drank. It’s like drinking Evian, it’s that good. When I first got here, I said to my bottle this and just started selling it to the UK…

Michael Rose  32:24

Well, I guess, you know, to that point, and that’s why, optimization is so important, there are parts of the UK, that would probably snap your hand off for that role, you know, given the constraints situations that they find themselves in, and you have some of the challenges that they’re now facing off to sort of the macro challenges about, you know, how to trade water across China geographic boundaries, you know, is certainly a big trend that I’m seeing to companies happy to collaborate together in a way that they’ve never done before. You know, and that, ultimately, again, is kind of underpinned by reliable data. I wonder, you know, in that particular instance, you know, what blockchain could do to sort of accelerate the collaboration and exchange of information, as the sort of particularly big geographic monopoly water companies look to work together in new ways to meet kind of overall societal challenges.

Ben Sheppard  33:34

Yeah, you know, it would be. It will make for a interesting use case. I mean, I guess a blockchain in that instance, one way you could use it is for capturing the quality of the water, obviously, in an immutable ledger, so that you really are confident that the pollutants in the water are at a safe level, that as it’s been transported, it hasn’t been affected in any way. So, you can have smart contracts, measuring the quality of the water as it’s going through that transportation activity to make sure that it’s not getting affected by any pollutants along the way and it’s been handled in the right way and yeah, I think blockchain lends itself well to that. And, yeah, it could open up some interesting opportunities, actually.

Michael Rose  34:26

I would definitely agree with you on that. I think sort of the how blockchain can manage not water quality, [Inaudible 3:34]. You know, it’s definitely a kind of an area of exploration. You know, not just in developed countries, but also, you know, the developing world to make sure that everyone ultimately benefits from sources of safe, reliable drinking water.

Ben Sheppard  34:50

Yeah, I’m friends with a gentleman that works in the water industry in the Philippines, and a few years back now, so I’m sure it’s changed now but I remember saying to him when I first got there, so can I drink the water out of the taps in the Philippines? He’s like, well, it’s clean. Okay, where’s this going? And he’s like, it’s too clean. I was like, what do you mean? It’s too clean, there’s too much chlorine in it and actually, it will make you sick because of the level of cleaning substances, we’ve put it in water. Okay, so I won’t get sick from it being dirty, I get sick from it being too clean. So, yeah, I mean a huge opportunity for blockchain to make sure that the right level of you know, cleansing has been, it’s taking place on the water as well, before people are consuming it.

Michael Rose  35:44

But I guess that leads me to perhaps another application for blockchain in the water sector and I guess that’s all around kind of transparent supply chains and thinking about, you know, how blockchain could be used to really understand and keep track of, you know, everything that ultimately goes into getting that water to your tap, whether it is, you know, chemicals for cleaning, or whether it is the products, the fixtures and fittings that make up the system that gets the water to your tap or to front door. I think is for me, perhaps one of the most exciting opportunities about kind of opening up transparency and giving kind of all of the actors in that supply chain visibility of how they ultimately contribute to a particular kind of critical source of a human source demand.

Ben Sheppard  36:42

Yeah, it becomes like a community driven thing then as well, doesn’t it which is also great. So, okay, Michael, it’s been great having you on the pod, loved chatting to you and I hope we can get you on here again, because I know Arcadis Gen is up to lots of other exciting stuff that I’m keen to talk about again in future. So, hey, have a good day and yeah, thanks for coming on.

Michael Rose  37:10

Cheers Ben. Thanks for having me and yeah, we’ve got some really exciting stuff coming up over the later part of this year. So, if anyone’s enjoyed what I had to say, check out our website, there’ll be loads going on on there and also reach out I’d love to hear from you.

Ben Sheppard  37:26

Yeah, absolutely and if you want to buy some water from me, let me know. I will send some over in a package. If you enjoyed listening, don’t forget to rate, and subscribe to our podcast and please do share your feedback with us. Thanks for joining the TX podcast.