CNME Editor Mark Forker sat down with Rob McGreevy, Chief Product Officer at AVEVA, in an effort to better understanding how the global leader in industrial software is leveraging Generative AI to solve some of the challenges facing the energy sector, including their approach to accelerating decarbonisation – and why AVEVA is advocating for an approach of ‘radical collaboration’ to really meet global sustainability targets.
Rob McGreevy has spent almost 30 years in the industrial software business, and during that time he has seen many changes and evolutions within the energy sector.
However, the advent of disruptive technologies like Generative AI is drastically changing the landscape across all industry verticals, and the energy sector has certainly not been immune to that.
Over the last decade, the energy sector has increasingly come under pressure from governments and policymakers to decarbonise manufacturing, or the production of energy.
AVEVA has firmly established itself as a global leader in industrial software, and for decades their portfolio of products and solutions have empowered businesses operating in the energy sector to increase their efficiency and productivity.
As a trusted partner, they are now helping businesses to accelerate decarbonisation.
CNME sat down with Rob McGreevy, the Chief Product Officer at AVEVA.
McGreevy is widely regarded by his peers as one of the most effective software business leaders in the energy sector.
He has enjoyed an illustrious career thus far, and prior to joining AVEVA in 2018, he worked for energy behemoths such as GE, Rockwell and Schneider Electric.
AVEVA is at ADIPEC 2024, which kicked off this week in Abu Dhabi.
AI will be a central theme at the conference in the UAE capital, and McGreevy kickstarted our conversation by highlighting how they are leveraging Gen AI to solve complex problem in the Oil & Gas sector.
“We’ve been using a series of different techniques when it comes to AI over the last few decades. We’ve been implementing AI into things like predicative and prescription analytics across the Oil & Gas markets to look at large assets such as equipment, vibration and thermal models, maintenance, and the sustainability for all those assets. What’s new on the AI front, or what I am calling the AI frontier is the generative element, and in particular the use and deployment of LLMs, and that’s been new for us. In general, we’re infusing AI technology across the entirety of our product portfolio, and that’s across the design, build, operate, and optimise lifecycle for Oil & Gas companies. We’re creating more intelligent designs for equipment for offshore oil rigs, FPSOs, and refining processes. You’ll see AI appear in lots of different ways, and we see it as finding the right AI tool for the right job. We look at different techniques for AI and then apply that to our portfolio in order to solve problems that exist in the Oil & Gas industry,” said McGreevy.
Decarbonisation is a huge topic, and it extends beyond the Oil and Gas industry, and with ADIPEC coming up, that subject of decarbonisation and sustainability will be front and centre of that conference.
McGreevy highlighted how the demand for energy is on an upward trajectory, and believes that AVEVA has the breadth and depth of products that can really accelerate decarbonisation for energy providers.
“What I would say is that the world at large, and our industry has a couple of challenges, and one of which is that the demand for energy is not going down, in fact, it is dramatically increasing. We still have parts of the population that don’t have access to power at all, but simultaneously we’ve got this need to drive decarbonisation across the energy sector. Technology can help, and the products we have at AVEVA can solve some of these challenges, especially on a capacity side to meet those energy demands for our partners, but also at the same time can look at effective optimisation to decarbonise. Across the energy value chain all these energy sources such as water, steam, air, gas and electric are used by manufacturing, and our software has different tools and techniques and capabilities to help reduce that, and sometimes reducing doesn’t sound too fashionable, but it makes a huge, huge impact,” said McGreevy.
AI is everywhere, and the general consensus across the board as that Generative AI has democratised the technology.
However, at the end of the day behind all the noise that comes with AI, you have to see results following its implementation, and McGreevy reiterated how important it is to show positive outcomes and ROI.
“When it comes to AI, it is so important to show quantifiable measurable outcomes and results for customers, because I do think were fast past the fashion era, everybody acknowledges the fact that Gen AI is new and interesting and is going to change the world, but the question for a lot of people is how? We’ve been lasered focus on that, and we’ve invested significantly in determining how these new AI techniques, particularly Gen AI impact industrial manufacturing and critical infrastructure to solve the challenges that we all know we face in terms of energy sustainability, productivity, and labour changes in the workforce,” said McGreevy.
McGreevy cited three different illustrations of how they dispense AI technology to drive and deliver better outcomes for their customers and partners.
The first example he provided was related to the design and engineering practices internally at AVEVA.
“Firstly, if we take the design and engineering side of our business, designers sit down and use our unified engineering tools to go and create offshore oil rigs and FPSOs. In the design tools we have used Gen AI to help engineers create the designs that they want, and it enables them to avoid doing manually doing pipe routings, so for example if you have a bunch of tanks, pipes and pumps then you need to stitch those together in order to create a facility. However, by using Gen AI we can connect the end points of those two things and have Gen AI optimise the route based on cost, piping and models for how that plant, or asset will perform and that represents a 20X improvement on productivity. Bottom line instead of having your designers and engineers manually draw this route, we can create scenarios for them in a matter of seconds as opposed to hours of manually routing them and seeing what the output is,” said McGreevy.
McGreevy also highlighted their success in infusing AI into their predictive and prescriptive analytics solutions, and also pointed to the importance of advising businesses around the changes happening within the labour movement.
“We have yielded huge results using predictive and prescriptive analytics on equipment like turbines and compressors, and we are looking at vibration analysis in order to predict that if this turbine continues in this manner, then it’s going to introduce a wobble, or whatever the issue may be, and we can essentially notify someone to say you better take a look at this asset because it is going to have a catastrophic failure, which could result in a loss of production and downtime. The third example that I’d like to highlight is around consultancy and advising people around the changes occurring in the workforce. Using Gen AI to actually advise people in situ on the job about what to do is incredibly important. We’ve got 50 years of process, production and design information just sitting in there, so we know how equipment and processes behave. If we can provide an interface using Gen AI in a conversational sense, and connect that Gen AI interface directly to our systems then that’s powerful. We know that we have trusted and reliable data, and the Gen AI can kick-in to begin to advise them on what to do with the data. Those are three ways that we are using Gen AI to provide quantifiable results to our customers,” said McGreevy.
It’s fair to say that the energy sector has traditionally been quite rigid when it comes to change, but as McGreevy pointed out the COVID-19 pandemic actually forced the industry to embrace technology in order to keep the show on the road.
However, he did concede that when it comes to change management in relation to technology it remains a major problem across the board.
“Change management in terms of technology is a huge issue overall, but certainly in industrial markets. We tend to be a more conservative marketplace and that’s born out of not conservatism in sort of thought processes, but more in terms of safety and security, because at the end of the day the things that we do are quite meaningful, and they have safety and environmental implications. I think that’s one of the primary factors as to why as an industry we have been cautious in adopting new disruptive technologies. However, it’s fair to say that things have got better, and dare I say it as a result of COVID-19. The pandemic forced our industry to take a look at technologies and techniques that we would never have done at such an accelerated pace, and that’s opened the door to look at things like AI in a much faster fashion,” said McGreevy.
Echoing his sentiments regarding the implementation of AI, McGreevy said that the quickest way to enact change and remove resistance when it comes to the deployment of new technology is to demonstrate positive results and outcomes.
“Change management and education is definitely an issue, but the fastest way for us to overcome this is to engage with customers on use-cases to show them the results, and thoughtfully answer the concerns and questions they have. We don’t rely on LLMs to provide the process and production answers, we use the LLMs to facilitate the conversation, but when we encounter a question about process, production and design data, at that point we hand off that conversation to the AVEVA software, and it asks the trusted systems that our customers have been using forever to form the response. That means the trust level in the AI that we are implementing is quite high because we are literally using their data verbatim. It is so important that they can verify the voracity of the AI outcomes that we have,” said McGreevy.
AVEVA have talked about ‘radical collaboration’ to really meet net zero targets and objectives, and the term certainly sounds disruptive, but how do you foster radical collaboration across the energy sector, and what does it actually mean.
McGreevy said that at its core, it’s all about breaking down existing silos in order to make these functional operating models to be more integrated with each other.
“I really like the term radical collaboration because it sort of evokes you, and just sounds something really disruptive. For us what radical collaboration means is essentially bringing together all the parties in the value chain from industrial engineering, manufacturing and Oil & Gas, which is all the industry verticals that we serve. The reason it is radical is the fact that it hasn’t been done before. All of our collective businesses in this sector are all built around functional operating models, which are quite rigid and siloed. They don’t talk to one another, so breaking down those silos, those organisational, operational boundaries and the business models that we have all grown up is a challenge. To break this down it requires this radical collaboration, and you’re making these functional groups really integrate and operate more cohesively and differently,” said McGreevy.
McGreevy concluded a brilliant conversation by reinforcing that radical collaboration has to happen in order to rethink and reimagine the exiting business models in an effort to build a more safe and sustainable future for the energy sector.
“For us at AVEVA, as a software provider that means that the digital backbone has to be highly interoperable because everything that happens across that value chain has a knock-on effect both upstream and downstream. Radical collaboration leads to hyper interconnectivity across this value chain, and we recognise it’s going to take everyone in the industry, so the partner ecosystem that we have developed over the last 30 years is just going to have to grow because we need more and more people to do managed services. We need tech partners to create very specialised algorithms for looking at those AI use-cases that we described. The way we have been operating these businesses, and the way we’ve running manufacturing and critical infrastructure simply has to change. In summary, as an industry the silos that exist across that value chain have to change because of the nature of the current landscape, and that is what we mean when we talk about radical collaboration. It’s essentially looking at how we have built and designed the value chain and rethinking and reimagining it, and the technology can enable us to make that happen which should result in a more safe, sustainable and use of the world’s resources.
