Hello, my name is Eoin O’Murchu and welcome to the Deloitte’s new podcast series Futureproofed. Today’s episode is all about the data analytics opportunity in the capital projects and real assets sectors and the emabling technologies that drive it.
I'm delighted to be joined by two guests who are leading experts in this field. Georgia Stillwell, manager of customer success in media for Alice Technologies, a construction optimization platform. Georgia has a background in construction and is a construction risk SME. And Mike White, a director in Deloitte’s Real Asset Advisory and Captal Projects Team, Mike specializes in technology and digital innovation across capital projects and infrastructure sectors and is an experienced construction professional.
Welcome to you both.
Mike White: Yeah, thanks Eoin.
Georgia Stillwell: Awesome. Thanks, Eoin.
So, let's jump in. Terminology such as AI or machine learning can be confusing and somewhat of a concept to some people. And Georgia, what does it mean in practice?
Georgia Stillwell: Yeah. So, I think I is not so much a futuristic concept as it's something that's around us everywhere today. I'm sure if you looked at some of the apps on your smartphone, they might have some AI running behind them. The example I like to think about is navigation apps, where you give it rules or parameters that might be a start and an end point and then even a mode of transport. What the AI is doing is looking at all the different routes to get you from A to B and optimizing for the fastest. So here at Alice we work in a similar way for projects, so you give it rules that might be the volumes of certain elements, equipment and essential logic and the AI does the rest. It's finding those routes from A to B or from the start to the end of the project.
So, there's been a lot of excitement and debate and discussion on some of the values that AI can bring, particularly as a disruptor. What are some of the immediate benefits that could be leveraged on large capital programs, for example?
Georgia Stillwell: Yeah, sure. So, on major projects, there are four key benefits that we like to find in Alice. So, the first is assurance. So a major projects there are thousands of details and computers and AI are well placed to spot those in feasibilities and undertake those calculations. The other is true optimization so with Alice we're trying to find the best way from A to B, and that can mean toggling with hundreds of variables to find the best solutions and also looking at scenarios that might not have been thought about at all. The other one is operational savings. So, exploring scenarios is far faster and more accurate with a computer and AI in your pocket and it can lead to data led decision making much easier. And of course, digital transformation. So, a lot of construction is based on experience and we hire for experience people. But by capturing some of those key means and methods and replicating those on projects after projects, we help bring about that transformation that many companies are looking for.
That's really interesting. Georgia. Mike, Georgia's touched on some themes there, such as assurance or maybe even digital capabilities. What role do you see Deloitte playing or using AI to disrupt major capital programs?
Mike White: Yeah. Thanks, Eoin. It absolutely has a role to play. When I think about what Deloitte wants to be in its vision, it wants to be a leading professional service advisor in Europe, the Middle East. We want to be working on the thorniest, most complex problems in infrastructure and therefore we know we have to partner with the likes of Alice or use tools that Alice offers. Ultimately that helps us validate our hypothesis quicker. When I think about the type of projects that we might support. So, improving the capability of a delivery function of, say, a rail program or assessing the efficiency of an asset managers delivery portfolio, we can use tech like Alice to arrive at a decision more quickly. But then we're able to therefore spend more time implementing, embedding, improving the organizations that deliver that program. There's a huge people element for us and therefore we can arrive quickly at what a decision or hypothesis might be to then work with the people to effect change, that ultimately gives better value to the client.
That's a really good scene setting. I think in terms of the problem statement or the benefits that I can address and the construction and asset management sectors as we know, are awash with data and information.
How can delivery and capital project organizations optimize their data and information opportunities to maybe drive better outcomes? Georgia, keen to get your view on that.
Georgia Stillwell: Yeah, sure. So, projects are faced with making decisions with sometimes no data, sometimes a lot of it, and trying to make the best data led decision. So, we can help with that and looking at that data in an organized way and an optimized way. And Alice, I think a lot of the clients that we work with are also alliances or joint ventures, and it's kind of a mixed data environment with some people having some others have not having any. And then again, we have this problem of a lot of knowledge and experience locked up with people and their expertises so I think we canleverage that and unlock it and bring it into Alice or other platforms so it can be leveraged and repeated.
Mike White: Yeah, I completely agree with you there, Georgia. I think they fall into two camps, the organizations that deal with this. So, they either aren't capturing the right data or they're capturing so much data they don't know what to do with it. So, the short answer to your question, Owen, are they making use of it? No. Can they leverage it? More of it? Absolutely. A lot of times with our work, we will spend a fair bit of time with clients to help them and assist them capture the right data and have a window into that data to help them make decisions. We spend a therefore a lot of time structuring that data, ensuring the data quality is as best as it can be and showing it in the right way. But from my understanding, Alice is a bit different. It doesn't quite work like that. Georgia.
Georgia Stillwell: Yeah, so any data clients have is useful, but they don't always need data to get started in Alice. We can help them transcribe some of the experience they have into methods and use and build from there and add data as we go, which I think is a strength because data poverty with some organizations is an issue, but it doesn't stop them from being able to optimize their projects.
Yeah, I think I think that's some really interesting points you brought out there, guys. And I think one of the the obvious challenges in the construction and real estate sectors are that the the data itself is comparatively less developed or maybe less optimized as it would be in other sectors. And Mike, why do you think that is?
Mike White: Yeah, it's interesting. One, thing I think the pace of the construction industry or capital programs is relatively slow. It takes some major programs to ultimately set the standard for the industry. And therefore, when we think about how we apply AI or other technologies, actually they're outpacing the pace of the programs themselves. They're outpacing the industry. It also probably takes a number of projects to demonstrate their lessons learned. So, they have to show a benefits case before the next program truly adopts that technology or that tool. And therefore, it ultimately takes time for that to wash through the industry. But when I look at the supply chain, actually, they're in a great position to capitalize on that. They have the ability to apply AI and other data led tools to drive insight for their clients, the program that they're working for. As a result, they can ultimately improve their margins whilst offering better value to their clients.
Mike, is there an optimum time for interventions to be made in a capital project programme to optimise data?
Mike White: Well, certainly at the start. I mean, you lay the foundations which enables you to reap the benefits and rewards earlier on. Ultimately, though, they go through life cycle changes. So, as they pivot from business case, design, delivery systems. In terms of operational readiness, they ultimately are going to have to change the way in which they use their data or use tools like Alice to make decisions around their next transition or their next delivery cycle. There's a there's a huge thing about complexity as well. These programs increase with complexity over time, and there's a degree of unknowns when we're when we're looking at the construction of these major programs. I see the use of AI tools and the likes of Alice to help us understand those risks better so that you can effectively plan earlier on.
Yeah. Georgia, what's your view?
Georgia Stillwell: Yeah, I totally agree, Mike. The long timeframes, the project life cycle. Also having multiple stakeholders and parties involved at different stages and the complexity all add into the data seen being less developed on projects. For me, complexity is a huge factor. I mean, only in the last kind of tens of years have we got design going from paper into computer, going from computer to automated to parametric and then generative for design. And technologies like Alice are building on that. But a lot of the foundations have been coming step by step. And the timeframe is such an important one. Many of the projects that we're working on are spanning ten years. It's kind of hard to think of anything that you maintain continually for ten years. Whether that's collecting data or not. So, tools and technology can really help us in that space.
Client and Tier one organizations tuning in to this will want to know how they can capitalize on the benefits of AI. Georgia, what steps should establish companies in their construction and other relevant sectors be taking to reap its benefits?
Georgia Stillwell: Yeah, sure. Oh, and I'll give a real-life example of a client that recently won a bid for a bridge project. And what they did in Alice is first assure their baseline. So, using a computer to calculate the duration of that project. And what we found was although they'd planned to do some of the work concurrently, they hadn't budgeted those additional resources, that additional equipment and a computer is pretty black and white about that. If you don't have that equipment, you can't do it in that timeframe. So, we're able to find and feasibilities in the baseline and then figure out the best way to bring it back on track. Once that was done. We're looking at pure optimization. So, helping Tier one contractors improve their profit margin by reducing overall duration overhead costs. And with, as I mentioned earlier, the many variables you can play with in construction, construction optimization and finding that as something that people can do really easily in Alice. For example, people had ideas, they thought, let's just add more equipment. But what we found was adding more equipment, adding more crews didn't make the difference. It was actually having one specific crew working longer hours that really made the difference. And I think there's an element of adopting early. So, some of the clients working with Alice, they totally get it. They think we need to double down on this now and commit before everyone's using it. There's definitely a competitive advantage to be one for clients and Tier one contractors and organizations adopting tools - sooner rather than later.
Yeah. Thanks, Georgia.
Mike, how can organisations in real asset space really get on top of this? Is there is there a way in?
Mike White: Yeah, I think the stance I take is if you or your organization doesn't understand it, then I guess quickly find someone that does, that’s the overarching message. How you do that? Well guess I guess that's you goes on go on a bit of discovery about where in the market is best suited to partner with or to work with. I know Deloitte have had some great success working in partnership with a number of tier ones. Our capability can complement each other and often clients are looking to bring the best of each organization. Ultimately, my advice is go out and find the organization that does understand this because as Georgia says, there's a competitive advantage to this, and that's absolutely what the rest of the industry is looking to do.
I think you’ve both set the scene really well of the benefits that AI and its supporting tech can bring. I'm keen to get your view, Georgia, on what you think the future of projects look like and the role AI will play in that going-forward?
Georgia Stillwell: Yeah, sure. So, I think one thing we'll see in the future is building or simulating perhaps these projects many, many times before they’re built. If you think of your navigation apps today, you're using AI to look at how you get from here to the bus stop. We can definitely leverage that for projects and I think it will be normal in the future. On top of this, I guess my personal view and not Alice's is that I would love to see a more open source type of project where you can kind of plug and play designs, build on top of generic information, and then add your value add and what your specific company can bring rather than always starting from scratch.
Mike White: I was thinking of it from the point of view of how these large infrastructure programs, how their organization might need to change to reflect how the technology is applied. So, for one program controls absolutely is going to be changing. Automation is going to be defining optimum delivery, schedule risks and costs, and that's an obvious target. So what does that means in terms of the shape of the organization, the skills that exist within program controls? Well, I think that's going to be evolving as it's leveraged more. The large asset managers, in my head I'm sort of thinking of network rail in the UK as a key, key example. They have comparatively to the big major infrastructure programs, they've got a lot more fragmented, smaller portfolio of work. It's each project is still fairly sizable in its own right, but there's many of them. So actually, I see the way AI could play into optimizing their planned maintenance and program of works being a key, key target and key area for benefit. I think as well the mindset of these organizations is going to is going to change. It's construction and infrastructure is a risk averse industry. And the use of AI, as Georgia says, to sort of simulate and simulate and simulate at an early stage quite rapidly. So that you can leverage and understand the opportunity is going to be a big shift in mindset. So that shifts them from a risk focused mindset to an opportunity driven mindset and that's going to have a big shift in the way in which these projects are delivered.
You both have outlined a lot of opportunities and benefits that I can bring across the life cycle of construction programs. I'm keen to get one final thought from you both on what capabilities do you see being integral to the delivery of major capital programs going forward from an AI and a tech perspective?
Georgia Stillwell: Yeah, from my side, it's construction experience and the mindset to try it and then make up your mind. I think none of these AI products exist without human judgment overlaid on top of them. So, there's still a huge space for construction experts and they can spend more time on high value add part of their job and let the AI take care of kind of more of the mundane or the crunching work that needs to go on. So being able to understand AI and leverage it into existing workflows is going to be a key skill going forward.
Mike White: I think the role of a CTO, a chief technology officer or equivalent in major programs is going to have a significant voice. It's either that or your typical program director is going to have to have another string to their bow in terms of their capabilities, in understanding what are the risks in using it and how they can benefit their program. Major programs have quite a complex capability matrix. They obviously, as we said before, they go through different lifecycles. That capability that exists within the organization changes over time, but there's going to be a consistent thread. You're going to need data architects, system architects, solution architects potentially to make sure you can leverage this volume of data and use it in the right way. And so those skills are going to be an area of growth in these major programs I see going forward. And then finally, there are a variety of organizations that can support the implementation of these technologies. And then as a result, they start to upskill the organization that harness and use it day to day. So, it's important to get started sooner. The sooner you use them, the quicker the benefits can be realized and the quicker you can upskill both your organization and your program, but also the wider industry.
I think that's a really exciting note to leave it on. Um, my thanks to your both Georgia and Mike for joining today for their insights and fascinating perspectives. It's fair to say that I is a really interesting topic with a lot of untapped potential, and I think we may have to come back to this one in the future.
If you enjoyed this episode, please do take a moment to like share and subscribe. Keep an eye out for future episodes and topics such as digital transformation, delivery, excellence, and other exciting, interesting themes in the capital projects and real assets space.
If you want to know more about Deloitte, please search Deloitte Real Estate Advisory Online, where we have a host of blogs, for you to explore. Please also check out Alice technologies.com for more insights on construction optimization.
So until next time, thank you all for listening.
