In this episode of Navigating Major Programmes, Riccardo Cosentino dives into an engaging conversation with AI visionary Dev Amratia, uncovering how AI is revolutionizing the infrastructure industry. Together, they explore the game-changing potential of harnessing data from over 760,000 global projects to forecast outcomes with precision, drastically improving risk management and efficiency. Dev reveals the untapped possibilities of AI, from smarter decision-making to reshaping the very way projects are delivered, offering insights that challenge traditional approaches and inspire a bold new future for infrastructure. "What if we had the experience of 760,000 projects between the two of us and then worked on a project, wouldn’t we be hundreds of times more effective than we currently are?" – Dev Amratia
In this episode of Navigating Major Programmes, Riccardo Cosentino dives into an engaging conversation with AI visionary Dev Amratia, uncovering how AI is revolutionizing the infrastructure industry. Together, they explore the game-changing potential of harnessing data from over 760,000 global projects to forecast outcomes with precision, drastically improving risk management and efficiency. Dev reveals the untapped possibilities of AI, from smarter decision-making to reshaping the very way projects are delivered, offering insights that challenge traditional approaches and inspire a bold new future for infrastructure.
"What if we had the experience of 760,000 projects between the two of us and then worked on a project, wouldn’t we be hundreds of times more effective than we currently are?" – Dev Amratia
Dev is the Co-Founder and CEO of nPlan, where he is at the forefront of rethinking how project outcomes are forecasted and addressing risk in the built environment. With an aerospace engineering background and 9 years of capital project management experience, he combines both technical and commercial expertise to tackle complex challenges. Dev also co-authored the UK Government's AI Review (2017) and is a Chartered Engineer (CEng) through the Royal Institute of Mechanical Engineers.
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[00:00:00] Riccardo Cosentino: You're listening to Navigating Major Programmes the podcast that aims to elevate the conversations happening in the infrastructure industry and inspire you to have a more efficient approach within it. I'm your host, Riccardo Cosentino. I bring over 20 years of major programme management experience. Most recently, I graduated from Oxford University's Saïd Business School, which shook my belief when it comes to navigating major programmes. Now it's time to shake yours. Join me in each episode as I press the industry experts about the complexity of major programme management, emerging digital trends, and the critical leadership required to approach these multibillion-dollar projects. Let's see where the conversation takes us.
Hello everyone. Welcome to a new episode on Navigating Major Programmes. I'm here today with Dev Amratia from nPlan, and we're going to be discussing a lot of things, but maybe before we get into that, how are you doing Dev?
[00:01:07] Dev Amratia: I'm good, Riccardo. Thanks for having me here.
[00:01:09] Riccardo Cosentino: Thanks for joining me on a Friday.
Hopefully you can have a productive conversation today, maybe for the listener. can you tell a little bit about yourself and a little bit about nPlan? I mean, I'm a big fan and if listeners have been following me on LinkedIn, they know that I'm always praising nPlan but maybe for those who are not following me on LinkedIn avidly, can you give a bit of an intro?
[00:01:30] Dev Amratia: And I personally appreciate that, Riccardo. So I'm Dev, I serve as the chief executive to nPlan. I'm also one of two founders of the company, and I come from a background in project delivery, so I spend just under a decade in the energy sector, working for Shell, in some god-awful parts of the world, delivering major capital projects for the group. I worked in upstream, midstream, and downstream projects. So I've seen quite a large variety of them in my short time at Shell. I then took a bit of a left step and I became an advisor to the UK government. I joined at a senior level and was advising the prime minister on AI and AI strategy which became that, I was the first author of the UK's National Strategy on AI. Which, back in 2016 and 17 was, you could call it, less popular than it is right now. And nPlan is a genesis of these two things coming together for me. I saw how hard it was to deliver large capital projects. I made a little guess that, it wasn't just me and it wasn't just Shell that were struggling.
And then I saw how powerful AI could be back then. nPlan, over the last seven years, has been focusing on solving some of these problems. Specifically, what nPlan does is we forecast the outcomes of large construction projects by learning how previous construction projects perform. So our AI system will read and understand data from just over 760,000 projects from around the world.
It's about $1.7 or $1.8 trillion of capital that has been deployed in the world. And the essence of that is saying, well, let's get an AI system to learn how projects perform. Just like how, Riccardo, you've got some experience. I've got a little bit of experience. Imagine two of us got together and tried to work on a project together.
We'd bring our collective experience together. But what if we had the experience of 760,000 projects between the two of us and then worked on a project, wouldn't we be hundreds of times more effective than we currently are? That's essentially the essence behind what we do at nPlan. It then gets served up, to answer three really simple questions that many of our clients ask us, how long is my project most likely to take? What could go wrong with it or what opportunities are there to improve it? And the third is perhaps the most important is what can I do about it? What should I do about it? In essence, you want to learn from the collection of thousands of projects to help you make the best decisions you can possibly make on your project.
So you're not alone. You're not making decisions on the back of a tiny handful of projects that you've seen in your little career. But you're learning from the world.
[00:04:12] Riccardo Cosentino: Very interesting. Yeah. And, yeah, I have to say, I mean, you've been doing AI since before AI was cool. That's why I'm such a big fan. I'm a big fan of big companies like yours that, I mean, now the hype is obviously LLS and chatGPT, LLMs, and, now everything is AI.
I think there's even two brushes that have AI capabilities, but big hype. But, I think it's fascinating how, this technology has been around for many years and it's, I mean, we shouldn't discard the hype because it's a good thing that we're now exploring and we're now exploring new things.
And, the hype can bring further innovations, but from your perspective,you think they, we're done with the hype? We still got a way to go when it comes to AI?
[00:04:56] Dev Amratia: Yeah, I think we're still scratching the surface of the hype. I think we are possibly towards the peak of the hype. However, we are still just scratching the surface of what AI can do. So hype is high, capabilities are still going up and they will go up faster than they're currently going. So if we separate hype from reality or hype from capability, hype is high, capability is still low, but it's going up very fast. The thing that is baffling to understand or believe is that if you think that the rate at which things are changing and improving is fast, It's only going to get faster now, and most people can't believe it, can't like fathom how could things get even faster and even better than they are into more mind bending things.
At nPlan, we have a phrase that we use internally, but I guess I'll share it here. Do what was considered magic. Right? Like, when ChatGPT first came out, I mean, people just thought it was magic. Like, how could this thing ever do something like this? And then later on, people, those that are technically inclined or inclined to understand these technologies, will understand everything is just science.
There's no real magic, just like there's no real magic in that magicians do, right? It's just, practice and science in the end that actually a lot of the AI technology we're seeing today and even the ones that will shortly be released is just science that is continuing to accelerate at a pace that we cannot forecast or fathom.
[00:06:33] Riccardo Cosentino: Yeah, and I think, I always like Sam Altman quotes, right, and I'm sure that it applies to your product too, that what today, even though it looks magical today, it's going to be the worst version of that product you'll ever going to see. It's only going to get better. So no, not only there are possibilities,
I mean, that's why we're only scratching the surface because we're just at the beginning. So if you think that what today is great. Well, I mean, there's, this is just the most basic version of what's possible.
[00:07:03] Dev Amratia: I agree.
[00:07:03] Riccardo Cosentino: So maybe like the podcast is, obviously, is about major programmes. Your tool is ripe to a major programme, a complex. And we need tools like schedules. We've always needed tools to help us manage the complexity of major programmes. Ultimately, our schedule is a Gantt chart. I mean, I always joke that I've learned recently that, the Gantt chart has been around since 1922 and, Mr. Gantt developed it. I didn't even know Mr. Gantt was a person. So the, the, we have tools that helps major programs. Your tool
turbocharge that. It allows a new dimension. Is that the market that your tool is trying to address? Is it the, the large, complex project or is a tool that is very versatile and can also be used for not so large projects?
[00:07:52] Dev Amratia: Yeah. So we see a direct correlation between complexity of the project and value the tool generates. The smallest projects that are using and plan today are 50,000 projects, which you could argue are not complex. And the largest projects that tool is forecasting today are hundreds of billions of dollars on a single investment.
So that's clearly a very big range. We do know that the projects that are at the top end of that are getting the most value out of it, but there's a really interesting phenomena that exists in smaller projects, which is the death by a thousand cuts problem on small projects. Mega projects have this very different dynamic where, the whole thing can just blow up and then it's one big explosion, however, small projects or portfolios of projects experience death by a thousand cuts.
So you could still end up in lots of CapEx, but split across, hundreds of thousands of projects. There's another kind of interesting case there where either your one project is so big and so complex that you can't fathom it yourself or as a team, or you're doing so many little projects that you can't fathom it across the portfolio.
Either of those scenarios are perfect for nPlan's algorithms, or algorithms in general to supercharge the project teams, where I guess the Venn diagram does not overlap there is small, single one off projects, such as house extensions or home builders, right? These projects are just not complex. The project manager probably knows enough about that project that the systems can't really unveil that much new information to someone like that. So those are probably outside of market in our world.
[00:09:32] Riccardo Cosentino: So again, maybe switching gear a little bit. I mean, obviously, the use of data to manage project is a key factor, or at least we believe is a key factor. Otherwise we wouldn't be here. You wouldn't be here because it's all about, it's all about managing the data because schedule data is data. And again, in my field, when we look in a major programmes. For me, my experience has been predominantly in public-private partnership, really complex, multistakeholder project with a lot of risk transfer and, at times not great tool to manage that risk transfer and almost, our experiences being that as contractors, as developers, we have taken on a lot of risk.
Believing that we were capable of managing that risk, and then, as it turns out, fast forward several years, we realized that not only we weren't capable of managing the risk, but we probably didn't even understand the risk to begin with. And so do you think that the market is now in a better place? And I don't want to say that we now have the tool to manage the risk. It's more about,
I think the tool rely, all of the tools rely on the data, right? And for us, in general, for the industry, even the procurement process that we have with these large public sector procurement. You end up bidding something, the absurdity is we would bid. a multi billion dollar fixed price contract on a 20 percent design.
And then we were trying to estimate a fixed price based on that. I mean, the lack of data to even make those decisions is very apparent. So do you think as an industry and with companies like yours, we are now better positioned to have the data to make better decision upfront and during the execution?
[00:11:22] Dev Amratia: I do. Let me explain why. What you're asking about is change. Do I believe that change is on the horizon, or imminent, or currently happening? Change is a function of three things in my head. It is a function of insight, motivation, and capability. So do we have the information to change? Do we have the motivation to change?
And are we capable of changing? If you have all three things, by the way, this equation kind of works for any change you want, right? Not just industry change. Even if I want to change how I dress my clothes, I have to be insightful about how to dress and motivated to dress and then capable of dressing.
Luckily, the third is in place. With industry, the first part is like, do we have the insights in order for this to become real? So I think this is, this then divides further into two. One is we have this abysmal performance rating most organizations will fail to make a profit, let alone fail to break even.
Operate on margins that are razor thin, I'm not saying anything new or novel anymore. And so there is this like general acceptance that continuing in the way we're currently going is non feasible. But then the other side of that insight equation is, do you then actually know how much risk you're taking?
Right? It's like, if you know that the position is really bad and you have to get out of it, then do you have the insight to know what you need to move to? This is the harder part. This is of course, where data in general or nPlan, to be specific, can help, right? It's like to take that example you gave earlier, you're bidding on a billion dollar project with 20 percent design on a fixed price.
I could tell you how you should bid that project actually right now. You should bid that for a billion dollars, plus or minus $900 million, right? And then you're, you might be okay. I'm not guaranteeing it either, but you might be okay with that, with those kinds of uncertainty bandwidths, not a billion plus or minus 10 or plus or minus 20%.
That's ludicrous. I think even the statistics will tell you that it's a billion plus or minus 3 billion. You can't go minus 3 billion, but you know, you could go plus 3 billion if you want. So insight. The next part is like. Whether we believe we're motivated to change, I think we're seeing pockets of this now become emergent, either through leadership that realizes they have no choice but to do something different, or in grassroots movements where you see project managers of their own saying, I don't want to just award some contract to some, as a client side saying, I don't want to award a contract to a contractor I know is full of bullshit.
I would like to do this in a more rigorous adult way where I can have a conversation with my contractor and say, Mrs. Contractor, I would like us to both analyze what we believe the risk is in this contract and fairly apportion that risk. That doesn't mean I take all the risk. That doesn't mean you take all the risk, which is, what we often see simple contracting dynamics play out as it's either you or it's either me, but we don't know how to apportion.
Apportionment is hard, and the reason it's hard is because it prerequires a detailed understanding. So if you have detailed understanding, you can make apportionment. And that goes into the final part of the change equation, which is capability. Are we capable of making this change? This is a debate that has long existed.
Like who instigates change? Is it the client? Is it the contractor? Is it the government? Is it the bank? Is it the insurer? Is it the technology company? And the answer is like, it's everyone and no one, which is unhelpful, but we do see examples where when things do work, there is, if not a moral imperative, a strategic advantage to sharing.
So there is a project that I'm not even allowed to name, which is the sadness of this, that has used nPlan for three years now. They realigned their contracts. They had excellent performance. The contractor is working away with fantastic margins on their book. The client is walking away with an asset that was delivered well within the margins of expectation three years into the, from when they actually started four years, a year before we came on board.
And I can't tell you about it now. I think both sides, the contractor and the client have missed a trick because not only does this narrative help the client with further fundraising, whether it's private or public fundraising, everyone needs money to do work. And it also helps the contractor know that they are an organization that is capable of meeting the word that they put in these bids, which I, if I was the client or the funder, I would put extraordinary premiums on these things.
How many contractors in the world do you know that have the data that can back up with independent assurance that they are capable of meeting targets on a consistent basis? That's gold, isn't it? Anyway, the more positive way of saying that is like the crusaders of change are the ones who are capable and willing to talk and share this change.
And through that, you will see others listen. The best influencer to a project leader is a project leader. Not a government, not a bank, right?
[00:16:37] Riccardo Cosentino: Yeah, I mean, you touched you, you mentioned it's,we've been in this industry for a long time. It is a unique industry, right? I mean, the construction infrastructure industry, it's very different from many others.
The way the supply chain structure is different. It's a project-based industry. project teams move around, so the knowledge retention is difficult, even teams that not just individual teams, but company make up teams to deliver a certain project for form and change. it is very unique, and I think it needs to be that way because of the nature of the things that we deliver, but we, as a, as an industry, we haven't quite cracked the nut on how to be successful and I always, I mean, I think it was one of my, one of my professor, I think it was a little bit, how he presented all of the reports from 1994 from Egan to Farmer and when you read them they all say the same thing and we're talking about so, 1994 to 2024, that's 30 years.
And if you look at the recommendation of this report, they all recommend the same thing over and over again. And we have not been able to adopt that. And so I'm hopeful now.
[00:17:56] Dev Amratia: Are you going to say the C word? You know what the C word is in the Egan reports?
[00:18:01] Riccardo Cosentino: No, I'm sure I don't. I'm sure I know.
[00:18:03] Dev Amratia: Collaborate.
[00:18:04] Riccardo Cosentino: Collaboration.
[00:18:06] Dev Amratia: Collaboration.
[00:18:08] Riccardo Cosentino: Can you imagine, right? I mean, we've gone full circle, right? I mean, that was a long time ago. And we, innovation and, partnering, And yeah, I mean, it's tough, right? and I think you, you hit it on the head. Like it's everybody and nobody has to address this problem.
And I think that's what the challenge is that everybody and nobody, and then to bring everybody and nobody together is really difficult.
[00:18:32] Dev Amratia: I, I want to pick on the C word, if that's okay, I'll do it through by telling you a little story of my time when I was at Shell. we were working on, a refinery upgrade called the Port Arthur Crude Expansion Project. And the contractor on site was a joint venture between Bechtel and Jacobs, two Goliaths that had formed a JV and were working on this 15 billion dollar refinery upgrade. It's a big project. And I was a mere project engineer, with my little scope within the project, which was enormously delayed and lots of quality issues, classic stuff going on. Very combative relationship at the top level that was cascading downwards. But I had a relationship with my peers, right? In the Bechtel-Jacobs JV. And I was just, I was like, hey guys, why don't we just sit down together and just talk this out? Like, you just tell me what you know, and I promise I will do whatever the hell I can to help you.
‘Cause if you win, I win for free, And so I thought we had, like, such strong, because, we were friends, actually. we would go out drinking in the evenings. Not too much, of course, because we had to get to work the next day. but we were personal friends. and that friendship was created while working on this project.
So I didn't feel like there was some contract between me and the contractor at this point. And what I realized was, it wasn't out of ill will that my peers inside Bechtel-Jacobs were withholding information from me, they weren't purposefully doing that at the lowest level. The reality was that we just didn't have the insight to collaborate.
They couldn't give me the information I was asking. I was asking them, what do you think might go wrong next so we can anticipate it so that we can both do something about it? And they couldn't give that to me. And I was like, it was frustrating as hell, right? Because you're, because then the thing will go wrong.
And it's like, dude, why didn't you tell me that you didn't think we would meet welding rates? I would have probably gone and got, permission to get, I don't know, do something about it in advance of time. But this information doesn't exist. And so how could we collaborate? We couldn't. In the end, the bad thing happens because you weren't, you were unable to have any foresight.
And then. I have no choice but to enter this very combative relationship in the end. And I can tell you it was not fun, right? Like I didn't enjoy working in that environment.
[00:20:55] Riccardo Cosentino: Yeah. Look, I'll take that a little bit further. Maybe not completely the same, but. Those, I mean, I see it on my project. They're all, we know that traditional contracting is adversarial because it's a zero sum game, but the people make the difference. Right? I mean, and the sharing of the data, it's important. And the shared information is important. Sometimes it doesn't exist. So that's obviously that's actually an easier problem to solve. The deeper problem is where the lack of trust and not the lack of trust but is more the contractual relation like the amount of time that you know i've heard people saying i've said that myself okay i have to send you this really nasty letter. And I need to enforce my rights, but I don't want to do it, but I actually want to collaborate on the side and that is so counterproductive, right, to human relation and to progress challenging situate, like this project has challenges like we don't need that additional layer of complexity of having to, to say, and also saying, I'd love to give you the data, but I don't know how you're going to use it against me. Or if you might use it against me at some point, so I'm going to withheld. And those, and that's why I say it's a very unique industry, the way the supply chain is structured, these contractual relations are structured, are influencing them at the macro level, but I think I like your analogy because your problem is actually addressable, like with the tools, having to try and we now can trying to get the data, but, ultimately you still need that level of collaboration because even creating tools like yours requires people, you say you have, 625,000 scheduled, that means that somebody has provided those, somebody has shared those to create the common knowledge, right, to create the base to then having data to make more informed decisions and to build on that data set to get even better. And that in itself, Dev is a humongous achievement in our industry. Right?
[00:23:01] Dev Amratia: And of course, nothing is philanthropic. No one just gave the data for the greater good, despite me asking. Each party will only do things if it is in their self interest for it. And I firmly believe that will continue to exist.
And there's nothing wrong with this either. So it's like, why is it in each party's benefit that they continue to share data? And that more and more parties continue to share this data. It is because of, a really simple, we all as corporations and as individuals hold a competitive bug inside us, where if I can gain something from my competitor in the form of knowledge, I will typically make an investment of a potentially outsized risk.
That means when we go and knock on the door of, I'm just going to use examples. these aren't real customers, but if I knock on the door of BP and say, hi, I already have all the data on how Shell, ExxonMobil, Total and Chevron have been performing, would you also like to share your data with me, and in return, you get to learn how Shell, Chevron, Total and ExxonMobil have been performing so that you can improve where you are, right? At that point, there's two risks that emerge there. One is like, if I don't do this, am I the only idiot that's now left behind? And two is maybe not calling it a risk, but call that the opportunity is like, what could I learn from others?
What are others doing that are, it's like the grass is always greener on the other side. Like Shell always thinks that BP are doing projects better than them. And BP always thinks Shell did better projects better than them. And, and Atkins will think someone else is doing projects better and someone else would think Atkins are doing projects better and that's, that's great.it's great that we continue to believe this and that we want to trade information that might allow us to become stronger and better at our work, right? I think this is native to our industry, which is, good. That is the essence of how and why nPlan has been able to build the data set it has.
And then also by saying, as more organizations continue to share data with us in a fair and nontransparent way, each organization will be able to raise its gain even further. The fact that the seventh oil and gas company now joins means that the first one that ever came into the story for free, for no additional cost for the use of the same service, will be getting better and better value out of the same product, right?
The product just keeps accelerating in its value case as more organizations continue to use it. And that self propelling, it is a literal self propellant, is it's really important, not just in commercial, in, how we sell and plan commercially
into the entities that are using and plan today, but also in how we help change the way this industry performs, right?
You have to, there's no way of really making a large change unless you're able to operate at scale, like this is not going to happen if I only ever work for one company or five companies, nothing will change, we'll just keep doing what we're currently doing.
[00:26:11] Riccardo Cosentino: Yeah, couldn't agree more. Maybe switching gear again.
And so we, we looked at the, we looked at the macro. but obviously these projects are now delivered. These tools are now used by individuals down at the project level. And, I obviously have been, I've been involved in the, in, I'm scoping out various product like yours and, I observed your interaction between people on the ground that are now looking at these new ways of doing things, new products that are coming into the market, and, I'm not, I'm starting to notice obvious, obviously skepticism pushbacks on new way of doing things. and, I think we talked a little bit, before, before we press record, I'm noticing how, there's for these AI tools and these new modern tools that there's obviously a lot of interest at the senior level for adoption, however, the adoption is then flown down to the people on the ground. and there's obviously, as you go down into the chain of command, more and more resistance of the adoption.I think the executives see the value. But then you have the conundrum that people at the bottom who are actually supposed to create the value that the executive have envisioned, resist the adoption because fear, misunderstanding, inertia, just doing things that we've always done.
How do we overcome this? Because I mean, ultimately, if we can create the best tools in the world, we can create the best, we can have all of the data that we want to make better decision, but if these are then not adopted, it's all wasted.
[00:27:52] Dev Amratia: There's a two, I can tell you how we think of it at nPlan, and this is in our internal strategy that we use to guide, to be our North Star on what decisions we take inside our product development.
So, the way we think of it is that the only way to escape the conundrum that you just described is to go top, so top down and bottom up simultaneously. So I'll start with the bottom up one. And the best example that we have that we can learn from is BIM, and how BIM was a pain in the ass for the most part and everyone was like, oh, I don't want to do that. Like, why I just do it in 2D, it's faster. I'm just talking about just going from 2D to 3D. Forget it, right?
Forget any of the other stuff, the bells and whistles that come with it now. And I remember like long conversations with engineering managers. This is in 2010 when, 3D was still somewhat novel.
And it was like, look, our engineering hours are going to triple because we have to do the thing in 3D instead of 2D. Three times the amount of engineering hours. Are you sure you're willing to pay for 3x engineering hours to get to that outcome? Now, because I'm in this world, I see what the problem was.
That we never thought about the user. We never thought about, are there some tricks that I can play that could just make the user's life a little bit better? And I'll tell you one of the tricks that nPlan has up its sleeve, so our users are, come from a project controls background. They're typically in project controls.
They can be risk managers, planners, schedulers, those sorts of roles, right? I'm not talking about executive vice president of planning. I'm talking about planner. That's it. And we realized like one of the things that was happening was we were hearing, I'm too busy to do anything with this tool, right? Like this is the more work I have to do.
No one wants more work, no matter how noble you are to the entity that you work for, you don't want more work typically. So what we learned was like, well, hold on. These guys are spending three weeks a month just reporting. We used to see planners that almost their entire job was summarizing the plan so that an executive can look at it on a page and they would draw literal lines inside PowerPoint to like show the plan in a dummy way.
Right? And it would take them days and days to do this kind of stuff. And then they have to update it. And it was just like, you could be 13 years old and do this job. You definitely don't need to have 30 years of planning experience to do that part of the job. So we said, can we automate it for you?
Can we do your reporting for you? Can we, just give us, tell us what your reporting requirements are. We'll write the report for you so that you, Mrs. Planner, can do the most valuable thing that you can possibly do, which is plan. That sounds silly, but you'd be shocked at how little planning happens in planning and how little risk management happens by risk managers, right?
They're doing so much administrative load. So the strategy we had was like, how can you help the user just to do something for them that is for them? And it doesn't really help the, who cares about strategic value? Just do something that
helps them selflessly help them. And that's a strategy, a core strategy of how we're building our product.
Then comes the top down thing. Like how do you go to the senior vice president, the executive vice president and say, hello, we have a product that can fundamentally change your business performance, change your PNL, change your risk profiles, change your dynamics with your clients. These are narratives that should appeal to the executive.
And one of the things that we've done, and you could call it one of the things we do, sorry, is we avoid talking about it as a software product for the simple reason that we think there's so much disposition that the software products belong so many layers underneath. Let's take a page out of the financial services industry book, which is, there's not a single chief executive or C-suite member of any financial services organization in the Western Hemisphere that is not interested in the Bloomberg terminal.
And yes, it gets used by hundreds of little people inside their organization. But the reason they are interested in this is because of the insights it generates. The insights that it can generate have strategic value for the entity. And if they can reap that strategic value, they get big bucks on their bottom line.
And so when the sales guy from Bloomberg gives the executive a call, that executive will almost certainly listen, because they know like, if I don't listen to this, because this tool gives me insight, I will miss out on opportunity inside my business. As a, as the infrastructure industry, the construction industry, engineering, we're not yet used to seeing tools deliver us insights.
It's very new for us. We've never had it before. So this dynamic is changing and it's fascinating.
[00:32:53] Riccardo Cosentino: I'm going to be a little controversial. And do you think is some of this is also, I don't want to dismantle it, but like, the Power BI, the Power BI, I've experienced, right, where, a lot of insights have been provided and sometimes the insights are not, it's just really, just a nice visualization of data, but there's no real analytics behind it. You just take it as data, you make it look pretty and that's and then you call it a dashboard and then that's supposed to solve your problem when in reality, I think we need more than that. Right? I mean, data visualization only get you so far. And so I think maybe people are being a bit burnt at the executive level. And not realizing the power of analytical insight based on, on, on real number crunching rather than just visualization.
[00:33:45] Dev Amratia: Perspective is nearly everything, right? Take the role of a consultant. Why should you pay someone hundreds times what you're, you would typically be paying someone, your own employee for work that potentially your own employee can do? The reason the consultant is valuable is because they bring perspective. They bring external perspective, right? And this trade of why you pay for external perspective is something that you will just not get no matter how pretty your Power BI dashboard gets, just can't get there. By default, it is only pulling data from within the entity, right, which has some value to it. I'm not, it's not nothing, but isn't, just isn't as strategically valuable as perspective that can come from external sources.
[00:34:32] Riccardo Cosentino: So let me repeat that because I think you just, I just got a light bulb moment and then, so what, which I knew already, but so basically, yeah, what we're saying is that the data at the end and, the data, the understanding the data and applying algorithm machine learning to the data brings additional perspective that in the past was only available by bringing, you touched upon this at the beginning, you know, you need it, if you had, you now have potentially 625,000 schedulers that you can interrogate about past projects. While in the past there's no way that you could have brought so many schedule to the table with their experience.
So you now basically have packaged up the experience in a way that can be extracted without the human. Because in the past, you could only try to experience from the human. Now you can extract it from, yeah, from the data that you're collected and that applies not just to your tool, apply to all the tools that use, I mean, that's what the machine learning part does, right?
You just, you learn from the past and trying to understand how the future is going to behave, which is humans have done, always done, but we're limited by the number of human you could bring to the table.
[00:35:41] Dev Amratia: Yeah, that's a good way of saying it.
[00:35:43] Riccardo Cosentino: Yeah, because I'm trying to.
[00:35:44] Dev Amratia: 760,000 schedule.
[00:35:47] Riccardo Cosentino: Sorry, my apology.
[00:35:49] Dev Amratia: That's okay. It's
[00:35:51] Riccardo Cosentino: It's growing because I remember listening to some podcast a few years ago and the number was smaller, so congratulations. If I dig out some of your podcast, that number was smaller.
[00:36:01] Dev Amratia: Thankfully it's growing, yes.
[00:36:03] Riccardo Cosentino: Look, we're almost at time and I just, gonna open up to you. I mean, you know, it's, anything that you think you want to pass to the listener one question that I have is that I, which I asked to several people who are engaged in our industry is like, what are your hopes for the industry? You know, what do you think we should hope for this industry going forward?
[00:36:22] Dev Amratia: My hope is that it is an industry free of bullshit. If I'm allowed to expand that slightly.
[00:36:29] Riccardo Cosentino: Please. Define bullshit.
[00:36:32] Dev Amratia: Yeah, I mean, it's when the contractor makes claims that are unachievable, the client believes that the contractor is, pulling their leg and then start stamping on the table and says, it doesn't take two months to make this change.
It should, you should be able to do it in one month in the contract. That's bullshit. Client doesn't have data that tells them it must be one month. They just have some intuition inside their head that makes them say this. I, I used to do this. Equally, the contractor is also guilty here.
And I think this is what I call bullshit, right? It's bullshit that you say, I can do this. And you actually have no clue whether you can or cannot do this. And if we remove, if we free ourselves from this and we actually talk about the amount of uncertainty we hold in genuine terms, I think we will be much more productive than we currently are.
I'll let you in on what internally at nPlan, we hold ourselves to a vision. We believe something about the, what the world will one day look like. And that's why we come to work every day. And our vision at nPlan is to build a world no longer limited by its appetite and risk. So we believe that the world is held back because of this appetite.
A classic example, at least in the UK, there are thousands of examples all over the world, but one of the examples in the UK is a famously marred project called the A303 Stonehenge Diversion Project.
[00:38:01] Riccardo Cosentino: I know that one well, very well.
[00:38:03] Dev Amratia: Yeahand it is a complex project in some ways, but largely that project has failed to proceed because no one believes anyone.
There's just so much bullshit in that system that the investor, which is the government, just is like, I don't trust anything I'm seeing, and because I don't trust it, I'm going to remain indecisive about it. And do I blame them? No. I think it's human to be indecisive about something that you feel is not well founded, and we don't have a record of doing these things very well, right?
So anyway, that's why we stand, right? And I have examples in my own history where fantastic projects fail to come to fruition because we could not convince our executive who would finance the project that this is a project that can be delivered. Because they couldn't get that confidence of delivery, they were just like, I'm sorry man, can't finance this thing. It's too much uncertainty for us. And I was like, man, it sucks. And I think society as a whole is therefore held back because of this.
[00:39:06] Riccardo Cosentino: Well, that's very noble and I completely share with you the, let's hope there is less bullshit. I would say the bullshit doesn't get concrete for faster of or, things built faster. Right? that's what I want to say. Okay, on that note, Dev, thank you very much for making the time today. I really enjoyed this conversation. just for the listener. I, I think I convinced your cofounder to also join me on the podcast.
So stay tuned. We're going to have a different conversation with Alan. We're probably going to touch upon more, more technical items, but I'm so glad that you guys agreed to support this podcast.
[00:39:39] Dev Amratia: Thanks for having me, man.
[00:39:41] Riccardo Cosentino: Bye now.
That's it for this episode of Navigating Major Programmes. I hope you found today's conversation as informative or provoking as I did. If you enjoyed this conversation, please consider subscribing and leaving a review. I would also like to personally invite you to continue the conversation by joining me on my personal LinkedIn, @RiccardoCosentino.