These graphics are incredible.... Okay. We're moving into the third part of today's session. Just want to take a moment to thank you for being here. But to the hundreds of people that are online, thank you. Thank you for dialing in. I'm sorry we can't send you a cocktail, which we'll enjoy in the room. But next time, try to be here. You'll get the benefit of the cocktail right after we finish. And we're going to go down into the brass tacks of how does it actually work in the world where a real organization that has incredible delivery challenges, how do they think about this problem? And I couldn't be happier to introduce my good friend, Craig Evans. Craig works with Chevron, has more years of experience than I will say right now. But in short terms, Craig has been on the pointy end of projects for a long time. He's felt the pain that I once felt in a very early part of my career and done that in some far-flung parts of the world. Without me butchering anything, Craig, tell us a little bit about who you are, what you do, and how you got to where you are. I still feel the pain. So, yeah, so Craig Evans, I've been with Chevron for 35 years. It's okay. We can talk about that. I've worked exclusively in major capital projects globally. So pretty much anything from around a billion dollars up to $45 billion is typically where we do projects these days. We're not doing the bigger ones anymore. And Chevron, just for aware, Chevron's like a vertically integrated energy company. So on the oil and gas side, that means we go all the way from exploration all the way through to the refinery to sales at the service station, for example. And then we also are now heavily investing in the energy transition in the areas such as hydrogen, fluor ammonia, CCUS, and sort of a couple of other things in that space as well. We operate in about, or we don't operate. We have a presence in 180 countries globally. So we're pretty broad. We have an annual CapEx budget of around, let's say, I think it's about $14 to $16 billion as guidance at the moment per year. Tell me or tell us about some of the delivery challenges you go through. I think it's probably common for everyone in the industry. We know we're not doing a good enough job at the moment in delivering capital projects in that space. If you read any McKinsey, Deloitte report in that space, you'll probably see, you know, we don't hit cost, we don't hit schedule. A lot of the time we don't even hit operability and quality metrics. So something needs to be done differently in that space. In Chevron, our projects can be very complex. We need to bring materials and goods from global supply chains into one location. Those locations can be very remote. So there's a lot of inherent risk in that as well in that space. The other thing is then I think, I read the Stuart report about HS2 the other day. And when I was going through it, I was like, yep, I can relate to that. I can relate to that. I can relate to that. I can relate to that. What you guys might call government, we would call stakeholders. We have the same thing. We would have partners we have to align with and can sort of do stuff like that. So a lot of the stuff that was in there is nothing new than probably what a lot of us have seen in the past. But it still keeps happening again and again and again. So I think it's those kind of challenges that we need to find new ways of doing things in that space. And I think AI is one of those spaces. Chevron's taken a recent approach that we are leaning into AI heavily, not just in the capital project space, but across the enterprise. We've now got an enterprise AI group and I'm getting a lot of support from my leadership. You remember Tom the other day and how much support I'm getting from him at the moment. And all the way to our CEO as well in that space. So there's a lot of energy to find ways to use artificial intelligence to improve project outcomes. You've talked to me before about these two tenets to the core challenge that you have, predictability and competitiveness. Tell me a little bit about what this means inside the Chevron organization, what it means for you or the projects that you get to support. So if we don't do projects, we will go out of business. It's pretty simple. We have a natural resource that's non-replaceable. So you've got to keep doing projects again and again. New energies and any transition is different. So if we don't do projects, we can't pay our dividend and make a return to shareholders. So there's always going to be that tension in there. And at the end of the day, it's our shareholders who are really our customers, our custodians at the end of the day. So we need to do that. So to do that, to be competitive, our competitors, Exxon, Shell, Shell and all those, they're doing exactly the same thing. They're trying to be more competitive in their space. So it's like, how do we get ahead of that curve at times? How do we sort of move forward in that space to be, first of all, competitiveness? A lot of the time, I think I've seen in the past where schedules were taking too long. So what happens is we put more risk in or we put more buses in, but the results didn't really change it either though because we didn't change the root cause of the problem or we didn't have a good forecast around those kind of, what's really causing those and what's really risk. And then we were actually missing probably a lot of the time. what the real risks were. We get a critical path. We get myopically focused on that without understanding sort of what is the sort of sphere around. There's other things such as Al was showing before driving paths versus critical paths is a prime example of that. So in that space, I think that's where, you know, and then when it comes to the predictability part. So when we get to financial investment decision, you know, we essentially, we as project people make a commitment to the corporation. So we will deliver that on budget, on time and we'll hit that production. So we're, as project people, we're making a commitment, we're going to go take that hill. When we don't do that, it impacts, you know, our investment returns. It impacts our reputation. You know, it impacts people wanting to actually come and do business with us or letting us into new countries as well. So we have to, like, from a very macro perspective, we need to be able to sort of do that. The other part of it from a predictability point of view is when you have, like, a swirl on a project or churn on a project and you keep changing things, that, like, causes more problems typically. And then you start to move people from one thing to another and you start to get out of sequence at times. And that just keeps rolling and rolling and rolling. So if we can get ahead of it and get more upfront understanding of what the risk is and then make sure that, you know, we're delivering to that plan and that schedule over time, I think that's a real opportunity there. to improve our predictability. And there's always going to be a tension between competitiveness and predictability. But I do believe that, you know, you can have both in that space. Yeah, I agree. You've had a journey with AI. Talk us through that experience. Talk us through what the pitfalls were and give us some wins as well. Yeah. So I originally started, like, 30 years ago, back in project control. So I always had an affiliation with data. I was always a very data-driven person. So I really started to get into that. We were doing a project and this was back in 2017. We were doing a project and we had this system where we could track every day what work got done, what work didn't get done and why it wasn't getting done. And what we were able to do is go, hey, well, weather's impacting us every single day. And it was either cold or hot. So people used to deal with cold or cold or hot. We did some analysis. It was like, no, it's the wind. I can't lift when the wind speed is more than 11 meters a second. So then we found out we had a data set of the weather in this location to be measured every 30 seconds for the last 25 years. So we were able to use our data scientists to go, oh, hey, run this and then tell us what it's doing. Well, what it told us is when the wind comes from the south-southeast, you are not going to be able to lift or from this direction to this direction. What we did was something really simple. We put windy.com on people's computers. It said when you plan the work the next day, have a look at the forecast, right? If it's coming from this direction, don't plan to do lifts that day or don't plan to do lifts in the afternoon. We had time of day. We saw a 60% reduction from just that in the impact of wind on our work. Something as simple as that. So the data was telling you how to do it. So then that got me interested into some other areas where we could sort of improve overall project outcomes. So we started off with doing, we had to do dewatering all over this massive oil field. So trenches and foundations. Things like that. So what we did was we used AI, so good old and algorithm, and to route our water trucks every day. We had a limited number of water trucks and had to go to different locations. So we were actually able to give the drivers, here's your route for the day. Here's your route for the day versus them randomly going sort of where they wanted. So that sparked more interest. And then I went off and did some study in 2019 around AI. And then it just sort of rolled from there. We started to get into some other areas of tech as well. So in my role for the last, I've been in this role since 2021, 2022. I do get a bit of leeway. I've got a bit of white space to play in at times. And I've got this interest in that. And I also think it takes people who understand what we do to go find the tech as well, or go find the solutions. If you don't understand the business problem, it's like, oh, here's a great new shiny toy, go use this. It's not going to work around that. So I think combining the two with that. Now, I have other people that have been really good. in the background to help me in that space. So that I don't have to worry about the tech stuff at times, and they can go do the responsible AI checks and the information risk management checks and all that for me in that space. Now, the pitfalls. What is AI? What is not AI? We go out there a lot. And I've seen a lot of AI tools that are really optimization engines, not AI. So understanding that at times, I think there's a big, everyone wants the word AI to whatever they're doing at the moment. And it's just a lot of it's not what we've seen out there. I think. How do you decipher this? Because take that optimization point, right? It could quite well be that you need something to help you with an optimization problem, which is a big problem to solve for, right? But how do you sift through this? Because I just came back from a conference, and the number one complaint I heard is, bloody every company, nowadays is an AI company. Right? I say this as AI day on the walls. Help us understand how you sift through this, and matching the business problem to the technical solution. So I think part of my job as well is to deploy like operations science, operations research on our projects. So we use simulation in that. We use a lot of discrete simulation in applying that to projects, like understanding flows and that. So I had an understanding what simulation was versus sort of what A was. I think I may have sort of fallen into that in a way. I think sometimes you've got to sort of have the right people asking the right questions and diving under the hood in that space. So I think that's good. What were some of the other pitfalls? I can give you a win. If you're going to go and do something, and you've got a technology group in your organization, leverage that technology group. So we have Chevron Technology Ventures in Chevron. I work in the capital projects. We really partnered on the work we've done with Mplan and sort of some other things as well. And that really took a workload off me from the responsible AI, information risk management, getting support from that business line as well for things. So it was very much a partnership in that space as well. So I shout out to Louisa Reid on that because she's been sort of instrumental in getting us to where we are now. So let's come to Alan. Why did you choose to work with us? Because Alan used to take me to coffee in Paddington all the time. It would solve world hunger. No, no, no. Well, it's good coffee because I'm Australian. So I think we started going through a process of just understanding what Mplan did through that. And I think, first of all, whether you go out there, you have to understand the skills of your own company and the skills of another company. Chevron's not an AI company. We tend to think we can go do everything and things are homegrown. So I think it's also understanding what you are and you aren't in that space. We do use technology. We use a lot of high technology in-house development. We use a lot of drilling, a lot of the subsurface analysis stuff, but not in the AI space. There tends to be at times, which is in Chevron, I've seen it elsewhere. If it wasn't built here, it's no good kind of thing. Which is not how I've been looking at things around that. The other thing is it was very data-driven. So if you go to a scheduled risk assessment, typically, and everyone's in a room in there, I think it's plus or minus this and that. There's a lot of opinion based on history. There's not a lot of data around it at times. So there was a clear gap, even in Chevron, in us curating our data from the history. So you guys had the data set already there, and now we're starting to curate that data set as well. I think one of the other aspects of it as well is it's just like going out and buying, whether it be equipment or materials to go build a project, we're leaning into supplier-led solutions. So the same kind of thing here. It's a supplier-led solution as well in that space. I think NPLAN also came with a background in projects as well. So you've done projects that people, you know, Taylor, we're working with some projects and stuff like that. So I think having that, you know, it's a combination of having someone like Alan and yourself and others in the team to say, yep, okay, we understand. the business problem at times. I'd love for you to share your experiences with NPLAN. You once told me working with NPLAN is like working with an onion. Peeling an onion. Peeling an onion. In my head, I thought that means the deeper you cut, the more you cut, the more you cry. I don't cry when I cut onions. I don't think that would be it. No. Hopefully we don't think you cry. Tell us a bit about some of the experiences you've had. Yeah, okay. So you see a lot of the stuff that you get demoed up here, the insights and all that. And that was all, we saw that and that was really good when we were sort of seeing that stuff. I think we did the same thing that Alan showed you before. We uploaded schedules, we uploaded terms and conditions of contracts and said, according to the delays in the contract, who's responsible for these delays? Kind of thing. And it came back and told us, you know, owner, contractor, stuff like that. So I didn't turn that around the other way. Like, I've got a tender, right? What I'm about to send out, I get a tender submission. It was like, okay, here's a tender submission. What contract clauses should I change to protect company? So, sorry, contractors, protect company from the cost of delay. And it came back and said, here's the clauses, here's the recommended language as well that you could use in that. So that was sort of, it may not be the exact right language that comes out of it, but then also, at least it's pointing you in the right direction in that space. We were playing around with it the other day when we were doing some work over here and we have a hazard wheel. So, like a safety wheel, it says, you know, there's 10 different sort of significant hazards, pressure, chemicals, gravity, electrical, mechanical, pressure. I'm not going to catch all of them. But so we thought, oh, I wonder if we took the hazard wheel and then asked Barry a question and said, hey, what, so we use gravity. And we said, okay, in the schedule, what are the activities that could be impacted by gravity in the next three months? And it came up with a list of not just the lifting activities, but also the backfilling activities for trenches, which didn't sort of dawn on me straight away that, you know, obviously gravity is going to fill in a trench for you. But at the same time, like, okay, this is, I actually showed that to our HES guy this morning in a kickoff meeting. He's like, right, I want my HES analyst in with this team at the moment. Because even, like, for me, a lot of the time is there's, there's the opportunity here to protect the craft worker at times. At the end of the day, like, they're the people actually building the things for us. And to me, it's, like, very important that we, you know, make a safe environment for them. On the schedule studio side, I've done similar stuff to what the Odie and Alan showed you. I've gone down to as simple as, like, advanced work packaging, uploading a single installation work pack. That was it, just with the description. I get a schedule. That gives me, like, a standard process. Then I've gone as far as uploading fish bones for systems completion. And what was the other? What's the fish bone? Fish bone. So it's a diagram that shows an order of, order of completion of various systems, whether it be an instrument, air system, whether it be, like, nitrogen system, electrical, etc. So there's an order and a sequence that has to happen. And then this was for some Brownfields work that was going to be done in a turnaround on a large LNG plant. So there was three, and there was three different turnarounds. So the schedule studio came up and identified the three different turnarounds, and then when it was going to do those turnarounds, and all the work they had to do pre and post it. The other one that I thought was really cool the other day is, so we were running another schedule in schedule studio, but previously we'd uploaded documentation that, a lot of us, we all have phase gate processes to go through governance. So we have a thing called IID, which is Initial Investment Decision, and then we have FID, Final Investment Decision. So we uploaded some other information that had nothing to do with that, that was not mentioned in that documentation. Schedule studio had already learned that when looking at a Chevron schedule, I need to have IID and FID included in the schedule that are auto-generated coming out of that. And I'm like, okay, that's either really freaky, Alan's working in the background, or it actually has stuff. We've been learning from your days. Yeah, so I think that one, and then, yeah, I think there's some of the things like Peely Young, and then like, even in prep... Sorry to interrupt. There was one story you told me about a project in Australia where there was two scopes of work, and the reason... Tell me, I think you know the story. I won't ruin it for you. No, no, no, you can still tell me about it. Yeah, so we're doing a project, the project that we were doing the proof of concept on last year, this year. So it's on northwestern Australia, it's a pretty remote environment. It's in a cyclone alley kind of environment. So they had three cyclone events hit them this year. They actually didn't hit them, but they had the rain event afterwards. So 180 millimetres of rain in one day, they get typically 300 millimetres a year. So they were very much impacted by that. So the team was then going, okay, should I go and work in this area? So that's the area A, or should I go work in area B, or should I work in both areas to try and recover schedule? So we were able to actually run the scenarios, run the mitigations, and figure out, okay, which area is going to give me the... Is going to help me recover the most out of that schedule? I think even in related to that, we even did the same thing around, if I reduce my electrical resources by 20%, because we uploaded the resources, what would the impact to the outcome of the schedule? Because we just had some... We lost some electrical resources, the contractor had. So it was like, it came back and said, yep, 20% reduction would be a 25% increase in duration, three to four weeks would be the impact. Then we're going, oh, let's do a trade-off. Do I need backfill resources or electrical resources? So we said 10% reduction in both. What's the impact? It came back and said, your backfill resources are your more critical group, so therefore, probably not reduce that number. It's okay to reduce the other number at the end of the day. You know what? That story is really special. One of your leaders told me, I have 31 years of project experience, and I now feel, I now believe, that my experience is anecdotal in relative terms, right? What that, what, you take that, and take the example about deciding whether you do scope A or scope B or scope A and B combined. When I was a young project engineer, the way we made that decision is you kind of look around the room, find either the most experienced or the highest, the highest paid person or the person with the loudest voice, and you do what they say. It's like, what did they make their decision on? And so someone with 31 years of experience up in the field to say, I think my experience is kind of anecdotal now. Why don't we see what a system that has trained on that quantity of the data might say to us, right? And that shift in culture is really, really interesting to see through. Talk us through a little bit about that cultural shift and how you see the year, the months and years ahead, right? What do you think, how do you see that playing out? Yeah. Just on that one before we move on to that one. That's my boss. The other one there was, I think what I was going to talk about is, you know, typically in construction, I would say most construction people, I want to work on everything, you know, and I just want more resources. Yeah. And that is not the answer most of the time, right? And it's like being able to have information and have a different lens is really good with that. So where are we going? Where are we going in the future? Where are we going with culture? Well, the change of culture. I think people are, I think we're getting into an era now where people are more acceptive of AI. I think at times, like I find the live language models, chat GPT a bit sort of great. Everyone's into it now, but it's like they've opened up people's eyes and lenses to different things. So I think there's a benefit to both in that as well. I think there needs to be a very much a shift back to the data, the how do I improve outcomes, getting the insights, you know, using the data to actually sort of improve outcomes and improve that competitiveness at the end of the day. So that's the challenge in that culture part. I think one of the things in my own career or last few years particularly is you can bring people to change, but you can't make them change at the end of the day. So it's being able to understand that when, you know, like I go into a room sometimes and you'll see the person with their arms crossed like this and you'll see the person who's all engaging. I'll just talk to the person that's engaging. You show a success, in this case, and then, you know, the others will follow at times. So it's also about how you approach the organization in that space. Yeah. Let's cut forward. What is the future of AI to then project delivery look like for Chevron? If I knew that would be on the beach in Mauritius. We didn't want to do that. I think, so a couple of things there. I think there's going to be a, what do I say, a lot of automation. So we can already do that now. I think as soon as we can start connecting like engineering data sets to construction data sets to historical information, I think things like, you know, installation work packs for work planning. I think, you know, that'll just automatically develop. You won't need people to do that. So it won't be a bunch of, you know, contractors do that. I think things like the efficiency things that are just reporting and stuff like that, that's all going to be there in the future. I think some of the big stuff in that space is like when you're selecting opportunities in those early phases. So at the moment, you know, we might do like five or six different concepts. And do a little bit of engineering around them. And then it's like, okay, we want to pick that one. I think that, and I see the text probably there, that I can now do hundreds or thousands of iterations of different scenarios. And I'm talking, is it four wells, three wells, two wells? Do I want to do real lay, S lay or J lay pipelines? Just different pipeline methodologies that you do. And all those have an impact to your schedule and your cost outcomes and your actually engineering sequence. So I think we're going to see a time where we can run the engineering iteratively in AI. We're going to be able to run the scenarios of those different outcomes. And you saw things like schedule studio. Like at the moment, you know, we can run, you know, Leonie did it in a nice year on 10 different schedules or something. Right? I could have 10 different options. I could do that a lot faster in that space. So I think that's where we're going to see the efficiency gains. I think the one gap that I do see that I don't think has really been thought about a lot is when we start going downstream to the construction site. how we're going to improve that aspect of it. A lot of what we're talking to is, you know, back in the home office or in places like that. I think there's opportunities in that space with intelligent monitoring, having AI, you know, analyse what work was done today, what resources I have. OK, here's your work plan tomorrow kind of thing, you know. So you become a lot more efficient in that space and they're not going from one spot to another spot because they don't have that, which increases risk. There's a lot of opportunity in that space. What do you think the hardest thing will be in making that happen? So I think there's going to be a couple of things. I think who's going to pay for it? So if you think about it at the moment. So you have contractors and owners. And the margins in contracting are very thin, I know that. But we're also in a margin business because we sell a commodity. So ours is a margin as well. And you'll typically see our refining margins are not very good typically. Our upstream margins are typically better. But there needs to be, I think, that's going to be one of the barriers of where it is and getting, I think, whether it be a contractor's adopted end plan and getting the owner to look at it or getting an owner to look at it, getting the contractor to start using it. I think, you know, whether it be end plan or other digital technologies, I think that collaboration and how that's going to work together needs to improve in that space. I think the other barrier, I think, is just people and capacity and resources. Like at times, you know, I have a day job. That's not sort of all of this. And, you know, and then on projects, you're already busy, you're already flat out. You know, you don't have time to stop and bring on the new tool or implement a new system around that kind of thing. So I think, you know, whether it be contractors or owners or just employers and different, like giving people a little bit of white space at times to say, hey, I want you to go and sort of try something. Like everything we've looked at hasn't worked. We've failed at a couple of things and said, right, stop, next thing kind of thing. But none of us have sort of said, oh, that was a waste of money or that was a waste of time. It's like we've learned from it. How would you feel if I turned Enplan off for you at Chevron? I'd cry. The onion would get in. The onion would start. But what would be the effect? I think it would be a really lost opportunity to trial something. I think the momentum that we've currently got is strong. Like it's coming from our CFO chairman. Like, I want to see something. Right? So, you know, it's coming down from that. I think, you know, we're about to do this rollout to go to the next phase. We're going to incorporate about 10 major capital projects, one to two portfolios. It's going to cover about $7 billion worth of capital spent in that scope of work. So we're going to be doing that coming up now. I think the other part of there is, so what would you think Chevron did that? The other example I wanted to give you is just talk about like the momentum as well. So I had a kickoff meeting with a team in Australia this morning. And, you know, like, who attended it? I had the project manager, the supply chain manager, the HSE manager, the assurance interface and air manager, the supply chain manager, the construction manager, and the project controls manager, and one planner. So I'm getting the support from the leadership. And they wanted to be there. Like, I got the email two weeks ago saying, Craig, when can we have the kickoff? From the PM. So I think we've got that momentum. We've got that pull. And once you get that, if you turned it off, that pull would stop. Yeah. Last question. And we'll see if there's any coming in from the audience. What are you most excited about in the journey that we're on right now? What are you most excited to see become reality? And this is where my product team will hate me, because this is where you get to ask for something. And it's now being recorded. Look, I think, well, I guess maybe go back to what I thought was going to be my, I thought I might come back to that one. So I'm most excited to see new use cases. So it's back to that onion again. Like seeing people on projects teams, how they can apply AI, like end plan, you know, into their daily jobs and how it gets incorporated into their daily jobs. I think, you know, when I hear something like, and this is what sort of may be happy as well, when we're doing some schedule studio stuff, the very senior planner in Australia came up to me and said, hey, this allows a planner to plan, not schedule. And I think that was a really important sort of moment that we keep sort of writing down and we put it in quotes and stuff like that. Like what the owner showed is great. You know, it might be perfect, but it allows the planner to just, okay, go sit down with the documents, go sit down with the construction people and start going, okay, does this look right and stuff like that. Not go through that admin. Oh, yes, thanks, so thank you. Cool. fancy. I'll ask your audience. No, no, no. You'll have to repeat the question for everyone on the way. So the question was, is there any structured training to bring people on this journey? And also for the company, I guess, in general as well from AI. Generally in Chevron, we do have some structured training about AI. And there are certain what we call AI champions in the enterprise. I'm one of them. So we have that. Particularly with NPLAN, we have NPLAN Academy that we've actually structured for, particularly for Chevron people, because we already do schedule risk assessments and are very strong in that space. So we don't need to learn about schedule risk assessments. What we do need to learn about is how AI works, how you can use NPLAN. And that's sort of a part of that as well. And then even with the kickoff meeting this morning, there was a, here's Chevron's view of it at the beginning. It was very much joint as well. So Chevron only changed leadership within this as well. It's not NPLAN, but that's where we sort of joined together in that. So in that, it was like, here's what Chevron's expectations are. Here's the objectives. Here's the behaviours we expect from you. So it's very clear about that. And then Taylor then got into the NPLAN. Here's the AI. Here's the various different, here's how it works and that kind of thing. So even part of the kickoff is a little bit of training and introduction in it. But then that's followed up with, they'll get an email tomorrow saying, start your training. Repeat the question. Yeah. Do I think it's going to change the shape of the organisation? I hope not, because we've just gone through a massive re-org and it's still going on. Maybe overstepping here, but I do think it will change the shape of the organisation in that you have far less people either between the contractor and the owner, and they're doing, and they're doing, and they're doing, and they're doing, I'm going to label it grunt work, right? And they're being elevated to more valuable work. Hopefully they all get pay rises. But the shape of that organisation is very different. An organisation that has X percent of people doing grunt work versus one that doesn't will look, feel and operate very differently. I just want, you mentioned something that perhaps some people don't know what that is. Craig mentioned NPLAN Academy. NPLAN Academy is actually, an online training course that anyone can do. Sharefront have a bit of a tailored version because of some of the things they've already fed in from their own organisation. But if anyone wants to actually just go through NPLAN Academy, learn how the AI system works, learn how schedule risk assessments work, learn how to do all of those things, learn how to do the things that Craig was saying he did, right? Like Craig very quickly blurted out some examples of things he did. You can learn how to do all of that yourself through NPLAN Academy. You can go to our website and type NPLAN Academy and sign up from there. One more question from the audience, if that's okay. At the back there. Thank you. So the question was, do we need a new form of contract to take a bet between owners and contractors to take advantage of AI? I was actually just thinking about that when Dev was just talking about that then. So I think there's, I think the model is going to change in a way. The compensation model is going to have to change. If, you know, like I have, part of it I didn't add in what's my vision. It's like, I won't be getting a weekly report or a monthly report or anything like that from a contractor anymore. I'll just get access to their data and generate my own. Because that way it's unbiased and I don't get the good news story every week around that. Now, that's going to take a whole bunch of work away from the current compensation model. in that contract. So there is going to be a change in that space of how that conversation is going to happen. I think some people have probably heard about, I think it's IDP or IDP2. It's a sort of contracting methodology. I think I can see things that helping out, bringing in, it's more of a joint collaborative approach to contracting. I think that would help in adoption of the AI space. And then it's understanding where the tool fits in your organization or where the AI fits in your organization. Like we see it fitting in both Chevron, and in our contractors as well. So whatever I do, so we're going to run like our contractor schedules. I'm just going to give the contractors the results. Here you go. This is what we're seeing. I'm not going to have that transparency issue around that. I don't want to keep it like that. I'm very strong on, you know, having the whole team having information and like that. And one of our ideas with using NPLAN is we're going to give access restricted to certain things so they can't do certain things, but access so they can go in and see the information in NPLAN. They can go and ask Barry questions relating to their own job as well. So eventually I'd like to move that to a given the contractor's access, but I'm not there yet in the phase where I'm at at the moment. Yeah. I think everyone will have heard about the value of collaboration in contracting, right? Like it's been spoken about, I don't know, from before I was born. And the reason we still speak about it is because it's this thing that everyone knows we have to do, but we don't have the means and mechanism to do it. And no, the idea of just use a contract to do it is not enough. So the bits that we're excited about seeing through are how do you have that adult conversation? Craig asked the question about risk, right? And risk is an adult conversation between the client and the contractor about who should own what part in what quantum and with what action, right? Not I own all the risk or you own all the risk and then you kick each other when it doesn't pan out in the way you thought it would. That's actually one of our six project management principles is correct allocation of risk between the right parties. Great idea. Super hard to do, right? With that, we're going to wrap up today. Thank you so much to everyone who joined us online. I appreciate you being with us. If you've asked us questions online, we will come back to you. We'll write to you. We'll email you with your answers. For those in the room, we're not going anywhere. Craig is not going anywhere. Come and ask us questions. There are lots of people from Endplan. You'll know we're someone from Endplan because we've got something like this on our T-shirts. Come and ask us whatever question you'd like. We're very open. People are happy to share all our trade secrets with you as well. Thank you once again. I appreciate your time. Thank you.