Energy Industry Masterclass Insights

Nuno Gonçalves: I am doing and I was deep diving on what I think is one of the most interesting industries as well. Mike, so yeah. How's it going? Everything good? New York seems cloudy as well. But all good?

Mike Reed: Yep. All good. All good. It's, I think it's that time of the year when you've gotta have everything on hand, because you never know what tomorrow's gonna bring.

Nuno Gonçalves: It was right. layer after the other. It was, yeah,

Mike Reed: 80 degrees on Saturday and 30 degrees on Sunday. So I think it's that rollercoaster for a little while. But yeah, really looking forward to some sun and spring. It's a pretty magical time of year. How's it going in Boston?

It's all good.

Nuno Gonçalves: All good? All good. It's always a little bit more wet, but it's going good. It's, I'm happy Spring is here and that, we're starting to have a little bit more better weather as well. So why don't we get started? It's, oh three already, three past the hour.

Time flies, and welcome everybody. As always, I'm joined here by Mike. Mike is our Chief Product Officer and our co - founder, ed Reejig. Mike as always, has brings this wealth of information and data around e every single one of these industries. I'm here and my name is nal. I'm here as the head of workforce strategy for JI and hoping to give you some of the insights of how we're seeing this industry, which is for me, and one of the industries that I somehow follow closer, with as it impacts many different parts and many other, industries as well.

And we're trying to unbundle. One at a time, as, these industries and giving you the insights of what we're seeing, what are the opportunities that we see, and somehow, what can we do, to help this industry actually increase even more and become even better. So we're gonna, as you all know, this session is, one way or the other about data, about trends, what we see, but also it's also about action.

So, we'll walk you through some of the, and Mike will share the agenda as well. If you click on the next one and we'll walk you through a set of structured, information that will allow us to go through everything that we want. So on one side. What is this and what's driving change in energy?

and it's changes everywhere, but I think there are some specificity to this industry, Mike. I think that there's, a piece around ai, and how AI is also impacting this industry. We will then go closer and have a closer role, look at some of the roles and some of the work and somehow how some of the evolution of work is impacting some of the roles that we see, in the industry.

And then we'll go a little bit more in the action and say, okay, how do we ultimately reinvent work? And I've been, Mike and I don't know, about you, but I've been in many calls these past two weeks where people were just asking. How do we do this? How do we go about being able to transform AI with AI and transform our organizations to be able to be better, faster, more efficient, more precise, and ultimately drive more value as well?

So we'll talk a little bit about the how and then, which is typically at an organization level. What do you need to do? What, how do we need to re - engineer and redesign your organization? Then we talk about what does this mean at an individual level as well, which is the reskilling and so on, so forth.

So hopefully this is a great agenda. We're trying a different approach here where we are trying to give you and start with data that we believe will matter for everybody, and then say, okay, what does this mean and what are we going to do moving forward? Does that sound a good plan? Mike, what do you think?

Mike Reed: Yep. Sounds a great plan. The only caveat, I'm sorry, around that is, you and I we're running without a producer today, so there's a real chance if you throw your question in the chat during the call, it might get, asked unfiltered. So maybe there's some responsibility on the listers to make sure that, you're not asking us to say something that's gonna get us in trouble.

But, looking forward to it. Let's go.

Nuno Gonçalves: Yeah, do. Put us in trouble. Let us, let's go. We, both of us, a little bit of trouble, Mike, so as. So, really interesting thing. So let's have a look at the energy industry outlook. And if you think about the energy, you, we've been talking about sustainability for ma for many years.

We've been talking about how to power and how to power in a, in an efficient way. How to source a different source, sources of energy and, different parts of the world having different approaches to energy and sustainability as. But still, I., an energy, an industry that draws a lot of revenue, and a lot of investment and a lot of people behind it as well.

So, it is a fundamental pillar as it says on the slide because ultimately it helps, all of the industries one way or the other because it powers all the industries, but also supports our lifestyle, right? And those of us that have been, and I was born in Africa, I know what it is to actually be without energy and not being able to watch TV and not being able to, I.

power anything one way or the other. So, again, it's not only electricity, but the, we need to understand that it's also needs something, an energy that is at the cornerstone of many of the other things as well. So we're talking about $2.1 trillion and all these numbers, it's mind blowing for me, invested globally in low carbon energy, right?

We've been talking about the everything around, sustainability and making sure that we have, that we take care of our world in a way that we will continue to be able to explore the resources in a sustainable way. There's a lot of investment, and I think it has been exponential, in, since, in the past two decades, the investment that we see there.

on the other side, we also see, and maybe I would love to stop here and actually ask you one thing, Mike, which is around the energy that requires the power, the powering of these. Ai, facilities and these computer facilities and data centers and everything that is very demanding as well.

'cause we continue to see the US electricity demands growing, which is a big consumer of energy in the world. Is this also because of the data centers as we see there? How do you see that moving forward? Is there a big demand also from more the residential energies as well, or it's more industrial that is leading that growth of 2%?

Mike Reed: Yeah, I think the, I think from what we saw in the data that the growth is being led primarily by commercial and industrial. So I think the residential build is, and a new build generally, mixed use is driving a huge uptake in renewables. So there's a tight line between new build, residential new build, mixed use.

And builds featuring or leveraging renewables. But clearly if you're not reading about the drought in AI chips, you're reading about the power consumption that's required to drive them. I think it does feel, in the short term, there might be a bit of a blip there as, operational efficiencies are looking for ways to deliver the new levels of AI performance, but with less resources.

Right. However, I think it's clear that the trajectory is going one way. There's a lot of focus on efficiency. There's a lot of focus on renewable. There's, but there is still a, an upward growth in the demands for energy in order to deliver the lifestyle, that we're living across, economies.

Nuno Gonçalves: Yeah, it's very interesting. So I'm, as, I'm Portuguese and in Portugal, we, there was a lot of investment in solar energy, and wind energy, sorry. And then, to the point that actually allowed us to, service our country, for many hours in a row, with very clean energy as well, which is really interesting.

And I would imagine that ultimately if you're, if on one side we understand that everything that is industrial, but also because a population is also growing, so there will be an increased need for energy if ultimately that those investments will lead to more of a sustainable place where we can reuse and use that green energy as that you were talking about as well.

So, and this is, but certainly something that is driving the change, right?

Mike Reed: Yeah. And it's materially changing how energy is being generated and executed and delivered as well. So we've gone from large single point generation facilities. Power plants or hydro, generally large and generally single point to this real distribution of energy, whether it's roof, solar, connected back to the network, whether it's wind farms, these are now a much more distributed and smaller scale of energy generation infrastructure, which brings along with it the transformation to internet of things, to iot, to instrumentation and monitoring, to opportunities for operational efficiency and balancing the demands across the grid and how the grid is actually performing.

So there's a trajectory to continue to grow that's materially changed the way energy is generated, and distributed. And what's possible in terms of squeezing efficiencies outta it.

Nuno Gonçalves: So I think it's fair to say that one, it's a very big industry. Secondly, it's a cornerstone of many other industries because it's powering.

Almost everything that we do in society as well. But it's also fair to say that it's been an industry that is, has been facing challenges and has been put in a place where they had to reinvent themselves. Right. We see that on the batteries and on the, automotive industry and how, ultimately energy is acquired and built there.

but here it's, I think it's fair to say, but more. Challenges ahead, right? More challenges ahead because there's a lot of work that is still to be done. So how can we do this? Is, it will be a thread throughout our conversation. So 2.2, increase, as you can see there, relatively stable but still in debt direction.

$2.13 invested in low, carbon, energy, which is a big investment, but as you can understand, that 90% there in the United States utility scale, solar and wind capacity additions accounted for nearly 90% of all new builds and expenses in the first nine months, in of 2024. That's huge, which I think is a big difference than what, ultimately US was, a decade, ago.

So. If we actually start seeing what some of the CEOs are saying and, 'cause, we always do this right, we try to understand and we map a lot of, what the industry and some of our senior leaders are saying in, leaders in this industry as well. So, you see the challenges in all of, in every single one of them.

So maybe two of these companies that are a little bit more known by the folks that are with us, ExxonMobil and Shell. So those are companies that have been always trying to reinvent themselves and ultimately becoming more sustainable and with, and larger as well.. What maybe, some of the people that are there that don't understand, don't know gai, and this is a Dutch, or owned, company that operates in more the northern part of Europe and a significant investment.

So the company is much smaller than ExxonMobil and Shell, for example, but they're still saying, listen, we will continue to invest, and here we're talking about 12 billion, euros through 2030 to support the energy transition, because that's a transition. Talk about, and we've been saying, in every single sales masterclass that all the industries are transitioning to something new.

This is also true here, and it's not something that was done or that will start right now. It's something that is being, done for many years as well. So,. The ExxonMobil, CEO talking about, the energy needs and the need for ExxonMobil ultimately because, of the role that they have at Shell to actually go to more and make this transition as well.

We talk about operational efficiency, we talk about AI integration. We talk about workforce reskilling because every single one of these, pivots that, these organization need to make, will require one way or the other, net new skills, which is in our own jargon, an opportunity for, to upskill and to reskill people that are in this industry as well.

How do you see, how do you see these big companies versus more, these more private companies versus more state companies? Do you see differences on how they approach, on one side profitability and revenue, but on the other side, somehow the investment on this transition to renewables?

In some cases, it seems that some of these,. Governments own organizations seems to be doing these transitions a little bit faster than others. What's your take, Mike?

Mike Reed: Yeah, it's been interesting to looking across the market and across regions. I haven't seen or we haven't seen a thread that differentiates the nature of organizations.

What I feel is clear in this industry is that no one's talking about energy transition as a competitive advantage. Right? And, the regulatory environments and compliance requirements differs from region to region. So, and up and down and depending on your politics, but it's not as though there's a sense that this is the thing that's gonna differentiate this is perceived as a necessity.

And the conversations about transition, about, moving to renewables, about efficiency, about sustainability are about this, is, this is we're running to catch up with where we need to be. So I feel. I feel the velocity probably differs across geographies, probably differs between politics and, large orgs.

but I think the threat is really clear that they're, and the conversations that we see here from the CEOs reflect more consistency than we've seen in other industries where some of them may be maybe concerned about supply and chain and, onshoring work or some of them may be con concerned about the workforce transition.

And ai, I think more consistently across energy, they're dealing with the need to transition. And that's a huge physical asset that we're talking about. The network is a huge physical asset generation is a huge physical undertaking. Dis distributor generation is a huge change from that model.

So all of these things, in the energy industry I think are really driving, a workforce that is. Already in the middle of a transition, how do we get to this new future? Because it's not something that we're chasing as an advantage. It's something we're changing to remain sustainable.

Nuno Gonçalves: What's holding these companies back?

So, because is it profitability? Does it mean that less clean energy is sometimes more profitable than anything? Because you were talking, you were saying, listen, it is a competitive advantage and I believe it is, for any, for many different reasons and for attraction and retention, for people, for sustainability and so on and so forth.

But what's holding back, or do you feel that this doesn't impact? Is it the fact that it's less profitable? Is it the fact that actually the requires significant amounts of investment as well? A combination of both?

Mike Reed: Yeah. Look, if I had to, if I had to highlight what I think historically, the last five years the drivers have been, it would be, regulatory uncertainty.

So a sense that. I's gonna be significant. Im, impacts imposed. But then a Paris Accord or some other, general piece of overarching regulation is rescinded. And it's that uncertainty about the level of commitment. I think that's been, impeding the speed of change. The other two factors I think that are relevant are the sheer scale of capital involved to be looking at the, a meaningful transition from historic power generation to renewables.

And that it's not just a, it's not just a another big spend, it's spent in a very different way. We're talking about much more available distributed generation, much smaller scale, which introduces a whole different, set of, challenges.

Nuno Gonçalves: This is fascinating. So just, and for those that have joined us for the first time, we.

This is a skills masterclass, and you might see, you might hear us talking a lot about work. And what we believe is that ultimately skills need to exist in an organization because there's work that needs to be done. So we actually start and understanding what is the work and ultimately so that then we can understand what are the skills that we're gonna need, moving forward.

So we'll always spend a little bit of time to understanding industry, to understand some of the drivers, to understand some of the strategic leadership thoughts and mindsets that we have, so that then we can understand the evolution and understand what is the impact on work on our workforce and what is the impact on people as well.

So that's the first third of our conversations is hopefully to give you a perspective of where this industry is and some of the drivers of change as well. I think as, and we did some other industries as well where there has been a lot of. The, one of the big changes is, or the impacts of change is the renewal of supply chain and the renewal of robotics and industrial renewal, right?

so that we have faster, more automatable, machines as well. But on the other side, as any other industry, there is an impact of the AI is bringing to energy and to the energy industry as well. So,. It is also not new to anyone that, the gen ai agent ai, what it's bringing is a completely new workforce, that is ultimately aiming to be, or wanting to be faster, better, more efficient, more precise, and actually do the work that needs to be done better, faster, or eventually even redesign or re - engineer the work one way or the other.

But there's, we are all in a lot of companies trying, a lot of companies and a lot of us trying to understand what does this mean and what can we, if we're in this industry or in any other. What can we do to actually, lead our organizations to a future where AI will be part of this transformation, will lead part of this transformation and where we are building a workforce, that has AI also fully integrated as well.

So there's, it's, it feels a little bit the race. It's not the race to, the gold race or the race to industrial and industrial automation. Maybe the race for, to leadership in AI and, new ways of doing work. But there is a lot of dimensions that you can see there on the slide that are somehow impacting our ability to see clearly what needs to be done and how it needs to be done.

Not all leaders are equipped to ultimately understand how to. How AI is impacting their businesses and what to do. Moving forward. Not all organizations understand and are able, even if they understand how, AI is impacting work, there's no connection between that work, that in that work and the evolution of that work with.

What does that mean from a skills perspective? There are operating models that are very confusing and where decision making is very difficult. We've always, we've all been in organizations, in big organizations where you have very low visibility. Very low visibility of the work that exists.

throughout the organizations there are hidden shadow organizations and so on. So we're starting from a place of, very nebulous in some cases. And we then need to create that clarity so that then we are able to understand what needs to change so that we can then become better, faster, and so on, so forth.

what are you seeing in some of the conversations that we're having, and we're having multiple conversations with very big companies out there. Do, is this something that they recognize? Is this something that they ask help for? Meaning that the complexity of, and the lack of visibility, so that they can then understand, okay, where are we and where do we need to go, Mike?

Mike Reed: Yeah. I think, the industry, a number of, segments inside the industry have been very procedural for a long time. So I think the, I think in some instances, the, that the work has reasonably well understood,. In a detailed and procedural way. One of the things that has been a benefit, I think, for the energy industry was that with a large capital investment on a power plant, for example, that's reasonably capacity limited.

So it's not, once you've got that in the ground, getting more out of it is hard at scale. So what they have done, 20 in the last 20 years is to really leverage up access to acquisition of data around performance. So the SCADA systems and the introduction of iot and the ability to aggregate this data related to the performance and use that as a lever to squeeze efficiency and effectiveness out of a capped asset, is something that's already in the blood of that workforce.

So transitioning from. Just the plant, the strain itself to how could we be running this plant more effectively and more effectively, more efficient. So that's something that they've been dealing with data. So the opportunity now is with the same workforce to be transitioning to leverage ai, broader scale analytics, machine learning and autonomous operation across these at the same time as we're also seeing this new solar generation.

So I think we see that they've, they're not, I don't anticipate so not inside, but from the conversations we've had, I don't anticipate that they're struggling with the what to do with the data as some other industries might be, where this is the start of their journey. This has been something which has been used in anger for a fair amount of time to try and maximize the output.

The opportunity here is, how can I bring AI over the top of this and really integrate it with a. A real material change. So the invisible work, going from a large plant to a whole lot of distributed suppliers, generators, different manufacturers, across a range of wind and solar and, small hydro and, other, project mechanisms for energy generation.

There's now a whole lot of other sources that they're trying to bring together, and they're less controlled compared to how energy generation used to be. And that same observation stands for the network as well.

Nuno Gonçalves: It's interesting because, maybe a decade ago we started talking about digital and I remember being in some companies and companies saying, we need to be much more digital, and so on, so forth.

But then at that time, the vast majority of the organizations then just hired people that would bring some digital skills, but they didn't necessarily transform themselves. And what ended up happening was, organizations being very bloated and hiring and then, but not being able to transform.

So the question now is, as you bring not only the digital side, but you actually bring, this ai agents to these, to your workforce. And, in the next slide actually shows what we're seeing from an evolution of workforce where. You do have your human workers, right?

Whether it's more fixed and more agile, more flex as well. But you're bringing something that was never was there before, right? So maybe the only big addition was on the industrial revolution where you had humans, but then you're bringing machines, that were, that would be doing, heavy work.

But right now the complexity is exponential. So you're bringing a digital worker that is able to actually, do some of your work. And to your point, not only assist you, and I love this, the way that you position this in many of our conversations, not only assisting the humans, but also augmenting the humans so that not only they're able to do what needs to be done, but also do it better and do it more one way or the other.

Now, in some cases, maybe in the future, this will mean replacing, many of the tasks and subtask that the humans do. But the reality is that this introduction of these, digital workers. In any industry. And in this one is no, is not, different either will require us to rethink the way that we split the work between the, these four dimensions that are on the slide.

And to do that then to your point is it is do we need the visibility of what this work is so they can say, listen, let's take this. And then, digital workers will be doing this, with, the human workers one way or the other. And how do we combine all of this moving forward?

But it's a new reality, Mike. And the question is, and that's why the hence my question around, do people know how to do this? 'cause this is undeniable. It's there, right? It's not an if, right? It's a reality. So, but do people actually know how to then split the work and understand, okay, how are we gonna do the work moving forward?

And in particularly, I don't know if you have any insights for this in this industry in particular, but,. What has been your experience do, is that something that is easily understood and actionable in the different organizations?

Mike Reed: Yeah. I really liked your analogy with the digital transformation, comparing that to the, AI transformation.

I think the distinction there that I think is meaningful is that bringing people who have, understanding of how to acquire, what to do with data, they're still doing that work. So they're bringing skills and can share that, but they're still doing the work. Whereas here, clearly we're looking for AI to deliver some, as you said, assistance or to be augmenting them and taking responsibility for some of their subtasks or for taking responsibility for a halter.

The where we find ourselves now, I think is not unique to energy. I think energy health, others, heavily regulated industries, the core work, introduction of technology around that is very. And very regulated. So I think we are looking there then at AI introductions in the work around the work.

So the work that supports the business of achieving its outcomes, making that more efficient, analyze and analytics. What we see there almost universally is that, it's either, adoption for a specific use case because there's awareness of a solution that can operate something, right? So very narrow and very niche or organizations that are trying to transform the way their workforce is thinking by making AI available to them and trying to encourage them to explore the challenge we see in that approach.

while over time it will increase AI literacy and awareness and capability in the short term, we risk me creating agents that reflect what I do at the same time as you are creating agents to reflect what you do. So basically we're just duplicating or replicating how a whole lot of people think about their work rather than a structured way.

And I think the distinction there is if you are much more confident and capable in this, you'll take bigger leaps. But the chances are you are already more competent and capable in transition to a new way of work and you are already probably achieving that. Whereas those parts of the workforce, which aren't, will continue to drift back and be using their AI to help them with their screenplay or come up with a coffee recipe or book a travel.

So I think there's value in what I, what we see broadly as a current approach of adoption through availability. But the, I think the risk out of that is under the hood, the compute and cycles that are required to drive agents aren't cheap. So if everybody in an organization is making their own agents without a structure, I think that, I expect that quite quickly.

That will be quite expensive and probably work against. Intent, which is AI, to make a more operationally efficient and effective organization. That's not unique to energy. I think this sits strongly in the work around the work rather than the core work that the business has delivered.

Nuno Gonçalves: I love where you're going and, as always, you're bringing insights that making me think on different ways and better ways as well.

So I think there's, I think what you're, what you're suggesting, what I'm hearing is that ultimately is we, there's organizations need to see this at an organizational level, not only at a team level, not only at an, and the reality though, and this is where sometimes I'm afraid, is that a lot of organizations are, a lot of organizations that we speak, that we know are very siloed, Mike.

So there is a temptation of, oh, let me do this on my side one way or the other. So, this brings a new layer, which is ultimately guys, if you're transforming, and it doesn't mean that you transform everything at the same time, right? But you need an organizational view to understand what, where should you start?

And once you do that, then you invest it to the benefit of the entire organization, not only the benefit of a specific country, in an, in isolation or in many company, many countries or many teams doing the exact same thing and duplicating their work as well. So, if they do, then there's a risk of a lot garbage in, a lot of, waste that will be, bring, brought to the organization.

That's really interesting. So a couple of things. One. Just to reframe again, so what we said and what always where we start with an industrial industry outlook. What we see, how we see the industry, what are some of the biggest drivers, how do we see the, some of the CEOs and the leaders, the directions that they're having, some of the challenges that they're having.

what we are also saying is that ultimately, as an organization in energy and many others, even if you have clarity of where you need to go, and in this case I think there's clarity on where the energy, industry needs to go towards more green sustainable energy as well. The question is then how will you,.

Re redirect a big ship. How do you redirect our organization to be where the organization needs to be? What we also said is that what this is bringing is this AI is bringing additional complexity opportunities, but it's actually bringing additional complexity to a point that you need to, have this organizational view that allows you then to decide what are you gonna do and how are you gonna reshape your workforce moving forward.

So, just wanted to give you a bit of, we've been talking for in, for almost half of the time here, but that's the journey that we are, and that what we wanted to do is that say yes, if this is the strategy, yes, if this is the complexity that we have in an organizational view, what does this mean for the workforce one way or the other?

And this is where Mike then brings a little bit, a bit of the magic on the overall workforce composition and how this workforce is today and how is it evolving as well. So, Mike, over to you.

Mike Reed: And we can see this is for now state. And while there's a lot of slices on the left hand side, I think we can break this up pretty effectively into those who are focusing on renewables.

So a material transformation in how generation has happened. We've talked about what, how those outcomes are achieved, distributed rather than centralized new technologies, are much more coordination. We see in the bottom left, almost same size, almost 30%, around energy efficiency.

That's, so that's the maximization of output of either the generation or the network, the distribution. So that's, and that's been an area of focus for the last 20 years or more where we have risk of capacity limits from existing generation. How do I get the most out of that? How do I balance that?

How do I store, how do I manage that across the network so that we're getting. Energy to where we need. And then, the top left across transmission, energy, power, and fuel sector represents how the work was done. So we can already see that in this industry there's a significant shift from just historic generation to generation and efficiency.

So making sure that we can use what we've got to manage increasing demand to then introducing renewables, a completely different way of delivering it. We can already see this is carved up into three really material shifts. And we can see within each of these, which will remain in place for the foresee.

we are now looking at for each of these, how can we leverage AI and automation to further improve the ability to generate value here? So that's, we see, and we can talk to, we talk to some of the roles that are gonna support that. Some of the transformations, we can see that support that.

But it's been interesting looking at this workforce more than any others that we've seen that there's these three clear landscapes between where it was. How it was dealing with that in terms of efficiency and then what the new future looks.

Nuno Gonçalves: Mike, we have, so I was seeing, I was, having a look to the list of participants as well. So we have some folks also from the automotive, industry as well on the call. And I think there's, this is the, it could be really interesting to understand and how make the connection, because we've done an automotive industries, masterclass, a few weeks back.

But how does that somehow, impact one industry versus the other as well? If you, as you go through the data,

Mike Reed: just a thought that's a really interesting, synergy that you call up there really astute. So I think if we look at. I think we're, they would be looking forward to this state.

This looks a, this scenario that we're talking about in the energy industry with the prevalence of renewables, the future state for EVs. They're on the start of this journey, a significant transformation from combustion to electric. But I think that's, it's a really, it'll be, yeah.

Sorry, I'm pausing 'cause I'm thinking. And.

Nuno Gonçalves: I was putting you on the spot here because I think there's the one, there is a, there's a lot of intersection here. And if I remember, on our conversation around automotive, we were all, talking about the shifts of, skills and the shifts of, not only skills, but also from a supply chain perspective.

What does that mean from a source of materials perspective? What does that mean? And it's a similar, transition, right? Because it completely shifts and changes the, your operating model. It pushes you to change your operating model that ultimately will impact also your, not only your machinery, your supply chain, your manufacturing, but also the one way or the other, the way that you market, and the skills that ultimately you will need.

So it's actually impacting every single one of these, right? And the more one in this case, industry evolves, the better impact will have also on the other, so. Let's continue. I think there's a lot of to pull from and I think, these analogies and these comparables between these two industries could be something maybe to explore in the future.

Instead of us talking about one industry, we actually talk about, what does this mean for a group of industries? I don't know. Maybe a thought moving forward.

Mike Reed: Yeah. Very interesting. Very interesting. If we're gonna focus on what we would call the bold move, so we're at a time of change, and you're already in transition, but we're looking at what can, what's possible with the availability and suitability of AI in this space.

We're now looking at what roles can an organization materially make a change now for a significant return? So this is the, not let the game get away from it. This is jump in, make some decisions on how I'm gonna transport my workforce to leverage AI in the near term as now to 18 months.

We see predictive maintenance as being transformational, and it has been for a long time. But this, the ability to be working with large data sets to be analyzing much more efficiently. The scenarios that happened in the past and applied into the future is transformational. We have seen, significant returns already on this investment.

I think there was a, what was I looking at before bp? I think they had one in, one of their US refineries in Indiana, which was reducing downtime by 30%. Approximately 50 million savings annually. Just in relation to the maintenance, we not counting anything else. So there's significant proof points already about the value associated with predictive maintenance and the increased capability to accurately get in and address issues before they land.

Is. Material straight to the bottom line. There's a bunch of metrics we can look at here, but what we see here is a straight 2% to revenues from a non - revenue task. So this is super material, what we see compared to the existing maintenance workforce, we see that marginally reducing. So again, there's probably some more data that unpicked on how this is being measured across traditional generations and distribution networks versus the more distributed ones.

But it does appear that overall there's a slight drop in workforce requirement for maintenance as we move to more of this predictive model, because it's more scheduled to be more coordinated, there's less responsive, and reasonably short term in terms of the setting up a modeling for yours. So there's a reasonably reasonable scale data activity at the outset to understand what your network is, what's under what outperforms, and to be validating the performance of the models, but a reasonably quick, turnaround to deliver value with predictive.

and it goes from being a technician to also including those skills around data analytics, and how the model's performing and what it's telling you. Another one that we called out there was grid operations analysts. So again, now we're talking about the efficiency of distribution, how to manage moving power around the network.

and again, a meaningful, and again, energy efficiency, whether a generational distribution has been something that, the introduction of iot over the last decade, over the last 20 years. And SCADA ahead of that has really moved forward in this particular industry. But again, we're talking about, improvement in execution of the works by around 30%.

And again, about the same level of, cost benefit straight through to, the bottom line in terms of revenues. Again, we see a potential reduction in these roles, but again, you're getting significant improvements and efficiencies. And this is what we talk about bold. So what we're looking for is to increase efficiencies.

What could we be doing now to deliver on value? And a marginal reduction of around five to 8%. But what we see in this particular one is it's a more significant undertaking because we're now talking about the infrastructure that supports the analysis, not just the analysis itself. So introduction of more it instrumentation monitoring so that we've got a better source of data across the network and we can manage that.

And the last one we're grabbed a hold of when we were looking at bold moves to deliver significant step changes in efficiency was around energy trading. So again, this is the energy sector. Energy industry is a huge industry, touches on a whole lot of aspects from oil and gas to the networks and generation to renewables, and also to energy trading.

and here we see this again in terms of other examples that we've seen across the knowledge in, knowledge workforce. The ability to use those models to deliver some real value, in terms of the outputs that energy trading. And so it's taking existing skills that they have in want and stats and applying that to this new world of, ML and ai.

So how can we apply that to managing, trading energy in the, in those markets? And again, re because this is similar to the first one, predictive analytics. It's generally leveraging information that is available. It's usually a much quicker uptake in terms of bringing both the skills and capability online, but also the data and infrastructure.

If we were gonna, if we're gonna pick a winner, we love competitions. But if we were gonna, if we were gonna look at somewhere to start. We use a number of different metrics. We have an ai, potential index, which is a measure both of the potential upside, but also the maturity of technology.

So not only are you, do we look at how much benefit you have, but how likely are you to achieve that benefit? Operational efficiency is a straight line to the impact on your business. Is this impacting those processes and those tasks that are most material to the outputs and then the time to benefit.

So, and how long will you wait? These combinations of levers allow somebody to make a decision on that's right for them. Do I need to validate that I can implement something, maybe I wanna look for a short time to benefit on a smaller scope, or do I need to look at transforming my business? And I can't avoid any more looking at the most critical areas of my business and focus on that.

Potentially looking for the largest, operational efficiency management of the grid, on that basis, seems to deliver us from based on broad industry understanding.. The fastest path to value across the energy. But again, this is quite a energy is a very broad industry, and this is limited in scope to management of that distribution side.

but it was a really interesting exercise in trying to unpick across a range of, segments. How would we compare, how do they compare? What's value today and what's risk today? Because that's core to making decisions here,

I think.

Nuno Gonçalves: yeah, and I think you're,

Mike Reed: yeah. The point that I'd probably summarize wrap up there, that there's a lot of opportunity to focus on efficiency. But I think I, something that we continue to repeat is that bold without responsible is.

We're being clear about how we are understanding our workforce and preparing them for the future as well.

Nuno Gonçalves: Yeah, and I think, so if we've been talking about the, somehow the landscape and of the industry we've talked about, what's happening in the industry and what can you do, right.

It's exactly what you just, explained right Now the question is then how do we do it? Right? And for us here at. We're we do have also a little bit of the responsibility of guiding the how and how educating one way or the other of the industry on how to, do these transformations.

How do you ultimately leverage the efficiencies that you were talking about and transform some of the roles that you were talking, that you just mentioned. Right. So, a few things, and if you've seen, we've, we were talking about sometimes the invisible work, but you, the reality is that you need to start shed a light to the work that exists.

One, in your organization. That's what bridging also does. And, passing the commercial. But that's what we want to do. We wanna make sure that we make work visible, that we bring the visibility to a place where we start understanding the different duplications. What are, what is the work being done in different parts of the organization?

we need to, we need organizations that start align aligning ai. And their strategy around AI with their business priorities. So ultimately they're trying to translate business priorities to what does the, how can AI somehow supercharge accelerate your business and your priorities as well.

But you also then, once you start having visibility on the work, you also need to understand how can you be faster, better? And in some cases it might be that you are. Augmenting what you do, you are redesigning how you do it. In some cases it might be a complete full re - engineer of how you do the work and how you deliver the value that or your organization needs to deliver.

And then of course, what does this mean? You need to make sure that you bring a new upskill and reskill your employees. And for us, which is very important, is that no one is left behind. So because we believe on this zero ISTed potential, so that ultimately we leverage a potential that existed in that, in every organization.

me, Mike, and typically I analogies. And for me, my head is, you have a big ship. And the big ship is you see it as a Lego ship, right? That is the big organization, the big legacy organizations that have been very successful and very large as well. So I, why do I say it's all Lego?

Because ultimately we need to deconstruct the Lego. We need to understand every single one of those pieces that ultimately make that organization from a work perspective. So that's what we are trying to do, is to deconstruct. Then ultimately we need to redesign or re - engineer something different.

So all we need a better, faster boat ship, or we may need an aircraft. It's still a means of transportation, but it's something that you need to be fully redesigned one way the other. So that's the analogy that I believe it's the hub, is that you need to make it visible, deconstruct the Lego pieces, a able to re - engineer into something that will be the future, and then understand, what do you need to ultimately operate that boat or, an aircraft as well.

That's the responsible piece of the reinvention. If we double click, what does this mean for us? Is that ultimately for. On, we, as, and we are known to bring these ontologies, and we've built in the past two and a half years, these 23 different ontologies. And what I think it's phenomenal is that I've seen parts of this information in the past in very, in data sets that are not, combined, that are not dynamic, right?

We see roles in job architectures. We see sometimes tasks in, od jobs or even in some of the job descriptions. We see skills in some of the town marketplaces and so on. So, but what these ontologies bring, that I think was a phenomenal achievement is that we bring a correlation between everything.

We bring a correlation within every single one of these industries of what are the roles that exist in the industries, but what are the tasks that exist there? What is the work that is done there? And then what are the skills that are correlated to all of that? Why is this important?

Is that once you're actually, once you've deconstructed Mike, then you're starting to construct work. But then you need to understand what are the skills that you won't need anymore because you won't be doing that work anymore. What are the skills that, the net new Lego pieces, the net new tasks, the net new skills that you're gonna have moving forward, and how does that impact your folks?

So for me, that's the beauty of what we are bringing as well, and, which with these ontologies, it seems dynamic and understanding as we are building the new boats or the new aircraft. What are, what is the work that we still maintain? And ultimately, what are the skills that we're gonna need moving forward?

That triangulation is absolutely needed once you have visibility of war so that you can re - engineer, the way that you will be, operating in the future as well. Yeah, absolutely. So if we click on the, go ahead. Go ahead.

Mike Reed: I was just gonna say that it gets to the core of what are we actually looking to deliver?

And that's the focus, the whole thing, the how and the who and the, with what. They're the pieces of your Lego boat that we've gotta play with, but it's the, what is this industry looking to deliver?

Nuno Gonçalves: Right. And for me, what has been, and listen, I've been a chief learning officer, chief talent officer for many years, and it's so incredibly.

Rational, right? Is that ultimately what happens is that, you need skills because there's a piece of work that needs to be done, otherwise you don't need the skills, right? So this correlation and the fact that ultimately, especially now with ai, what's being deeply transformed is the work.

Then you need to understand and you need to correlate skills and works, because ultimately AI is automating the task. It's not automating the skills, it's automating the work. It's not automating skills. So once you do, then change the Lego pieces, what's the impact on that individual level? That's the things that are most critical for us.

So ultimately that's a, that's for those that are out there listening to what we're saying, it's a mindset shift. We've been very focused on skills and isolation. What we advocate though, is that skills is a means to an end, right? So, and that end is ultimately work that needs to be delivered, and that's a connection that we haven't had before, that we now have and that we can bring to different organizations moving forward.

So if we click on the next slide there, as well, then Mike, what you'll see is just a bit of, and just to give you, because it can be somehow conceptual, give you a little bit of what does this mean and how does that, is ref, how is that reflected on all the jobs? So what we then say is that, yes, you need to create visibility of what every single one of the jobs of your organizations do, at least those that you want to transform or those that you believe that you need to transform.

And in this case, you will see that, you will have the work, the tasks, you'll have the skills that are associated to those tasks, but then all the insights and all the intelligence that Mike was telling you about before. Which is how much exposure every single one of these jobs are have to ai, meaning will these jobs be highly impacted and will parts of those tasks or subtask be done in the future by an AI agent or by some, somehow another technology one way or the other?

And if so, what does that mean for that project manager moving forward? Does it mean that you will not, you'll have less project managers? Will it mean that you'll merge a project manager with a change manager? Will it mean? So, and that's the read the beauty of, and that's the arts, right?

So with the ontologies, we bring you the science, we bring you the numbers. Then you need a little bit of your art to understand what are you gonna do? Are you redesigning or accelerating a project manager? Are you doing less, more with less? Or are you actually, creating net new jobs and, that will help your organization become better moving forward as well?

So that those dimensions, how much more efficient you become, much more how much, more speed and fa how faster do you become, what is your opportunity to reduce duplication? Those are all data points that we will, that ultimately you need to have so that you then decide where are you going to transform, right?

And again, that's not,. Rocket science, it's giving you a rougher magnitude that if you have a role that is 80% exposed to automation versus a role that is 20% exposed to automation, maybe you need to start with the 80%, exposure first. One way or the other. We've never had this before, Mike, and that's the thing that right now, I believe that, and we say it might be HR or not, but we now have start to have the data and the tools to be able to do the job that the business requires us to do, to translate the business strategy into what does this mean from an organizational perspective into individual perspective, giving the data and factual data, not opinions, not perspectives, factual data that will help the decision making of an organization moving forward.

Mike Reed: And as just saying, this is your challenge, organization, not my problem. I'd love to help, but the solution is there. And it could be that the. The 80% opportunity, those roles are where the resource constraints, those people just need more time. So let's get them more time. I can get them 20% more time.

Or it may be that the 20% role is your most important role. And in order to make sure that I'm focusing as much resources as I can on that, I want to improve the performance of my 80% because I can bring those people with their skills across to help this pool and make a new type of role, or at least free them up.

So it really is dependent on each organizational's business strategy. And while you may have customer service, you may have a thousand customer service people. I can deliver the same level of output with 20% of them. But is that gonna be sustainable? Is the new expectation gonna be that customer service is now a new thing delivered at a new level in order for me to remain?

So that's the business differentiator. That's what is possible when you can unlock this capacity.

Nuno Gonçalves: And that's a place where if we just go, just one thing, Mike is around the skills and we've been talking about for decades around skills professional and skills adjacencies and so on, so forth.

we have never had as clear information and data to what are the skills that need to be upskilled? What are the skills that need to be upskilled in your workforce or some of the re - skilling paths that you need to have in your organization as well. So to your point, it's not only about the efficiency at a, at an organizational level, but it's also making sure that you understand what are the adjacent skills and how can you lead and how can you guide and manage people to roles that will be more sustainable in the future.

How can you build their skills, so that they have a better job, a more sustainable job, and a stronger job in the future as well? That's how, that's what this information will give you as well. So just wanted to, give this, piece of information. Go ahead. While we're excited.

Mike Reed: And if we look, this is the responsible bit, right?

So these are roles that we know will be impacted and with no other information could end up with a much smaller required for deployment for workforce. So what do we do to make sure that we're being responsible? And there's a couple of the headline scenarios here, just pulled the top three ranking out.

were from data entry to data analysts, not particularly unique. We're seeing that across a number of industries where the management and handling of data is being automated. The bottom one from admin assistant to project coordinator. Again, we're seeing that in a number of industries as well. It's taking that, in innate ability to plan and putting around a framework to support, project and agile delivery.

The one that feels a bit more unique and a bit more worth diving into here was around maintenance technician to main, predictive maintenance analysts, which we touched on at the start when we're looking at where's the biggest bang for buck. So not only is this a big bang for buck, but there's a path for your current workforce here as well.

What does that look? That looks you've got a workforce at the moment who are already familiar with the mechanical, the electrical equipment that they're responsible for. They already understand what to look out for, what the nature of those are, and they understand how to diagnose incidents as they happen.

What we're looking to give them is exposure to those tools, which will allow them to run scenarios about what could happen and what are the implications of the thing that could happen, and how likely are those things to happen. And also, those IOT systems, the scale of the instrumentation, the monitoring that is feeding that information to them.

So the skills that they will need talk to the future of predictive, the skills that they have, talk to the output is generated from maintenance in the energy space. I think there's, you're really gonna be driven towards the vendor that you are using. So who are you using for your assets, your energy assets?

Who are you using? What platforms are you running on? Is it Siemens, is it ge? How are you delivering this? And that's where you can drive a lot of that, skills acquisition in the new ways of work. For the more general fundamentals around iot. There's lots of courses available. So the acquisition of these skills at a introductory level, the beginner level, these things are very readily available and very affordable.

So while I might say the cost of re - skilling completely to, predictive analytics are predictive maintenance in the GE ANOVA platform, it's 10, 000. It's probably $3, 000 worth of training and then just time. But what we see is that the cost of losing those people and having to acquire other people is significant.

It's more than the cost of the individual. So you are reducing the risk of losing that huge cost of turnover. The cost of re - skilling is negligible by comparison, but the revenue, as we've already seen, we've been able to see that there's a 2% impact on revenue. As a result of the availability, the increased availability.

So it's a straight line to value for the organization in this investment and for the individual. We see a significant uptick in the salary between a maintenance technician, generally across energy and those who are operating in the predictive maintenance analyst and re, predictive, proactive response framework.

So this feels an ex, a really good example of win for everybody. The workforce that's most at risk has a really clear path to become a more valuable part of the organization, both valuable for themselves in terms of salary, but also valuable for the organizations in terms of direct pickup for revenue.

Nuno Gonçalves: Yes.

Mike Reed: Conscious time. We're at time.

Nuno Gonçalves: Yeah, we're at time. So, real quick, one, two messages that I want you guys to stay with one. It's a journey. And we know it's a journey. You will not be able to do this from a Sunday to a Monday, right? You have a blueprint here. The blueprint is that you do need to start with the ontology.

You need to start with the data. You need to start with the visibility. Then you need to redesign and reimagine how you're going to do the work. And once you do that, then you need to start mapping your people to the jobs that will be more sustainable in the future. Mobilize what we call mobilized work to worker, making sure that you're guiding them to the jobs that will exist and not to the jobs that will sunset in the future.

What does that mean? It means that one way or the other, there will be a need to upskill or reskill your, workforce. So how do you do that? And of course then you get to a place where you will have a full integration between AI and humans. And hopefully in a place where you are so proactive that you always constantly, leveraging the potential that, your people has as well.

So that's your blueprint and a little bit of a step - by - step approach there if you want. Now, if you want more information, I'm more than happy. We can, we're more than happy to have to schedule and book a personalized skills masterclass for you, for your organization that is Ed, not only for you as an industry, but also for you as an organization as well.

So we have ton of data and we're more than happy to tailor some of those conversations to your organization within a specific and given industry. So you have the, QR code, just scan it and then ask for the masterclass and we'll be there. A hundred percent. So Mike. Thank you so much for your time.

We're two minutes, late, here, but I appreciate you as always. We know, we will see each other, much before of course, but on the 17th of April, at the same time where we will talk about one industry that is very hard, very dear to my heart, which is the consumer goods industry as well. So thank you again, Mike.

Always a pleasure. Hope everybody enjoyed and let us know. Reach out if you need us.