Roadmap to practical AI impact
“AI will always keep the human at the center, it’s a guidance tool, not a replacement,” says Kim Custeau, Executive Vice President at AVEVA. What does that mean for the future of industrial operations? In this episode of AI at Scale, Kim shares how AI is moving beyond hype to become a practical enabler of efficiency, resilience, and sustainability. Rather than focusing on technology for its own sake, Kim explains why success starts with solving real-world problems and embedding AI into everyday operations.
From accelerating engineering design to empowering the next-generation workforce, this conversation explores how organizations can leverage AI to optimize processes and prepare for a future of personalized, human-centered experiences.
In this episode, you’ll learn:
- Why AI adoption should begin with clear use cases.
- How embedded AI assistants democratize access to trusted data and preserve critical knowledge for new employees.
- How AI-driven automation of complex tasks boosts efficiency while keeping humans in control.
- Practical steps leaders can take today to prepare their teams for dynamic, personalized AI-driven operations.
For executives navigating digital transformation, this episode offers a clear guidance for leveraging AI to deliver measurable impact
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Transcript
Gosia Gorska: Welcome everyone. This is the „AI at scale” podcast by Schneider Electric. My name is Gosia Górska and I’m the host of this program. Today, my guest is Kim Custeau, Executive Vice President at AVEVA. Welcome, Kim.
Kim Custeau: Thank you for having me here today.
Leading the product portfolio
Gosia: You are bringing three decades of experience in digital transformation, customer-centric innovation, and operational experience. You are leading not only the product portfolio strategy, but also all the cross-portfolio initiatives such as AI, sustainability, and cloud platform alignment. Can you tell us what it means in practice that you are leading the product portfolio? And maybe what are the challenges in aligning all the AI initiatives across a broad ecosystem of products?
Kim: Well, maybe first I’ll introduce AVEVA. As Schneider Electric Company, we are an industrial software company. We deliver solutions across a very specific asset life cycle: design, build, operate, and optimize. Our solutions target industrial and infrastructure customers. My role leading the portfolio is to be the link between the requirements of our customers in those markets and the deliverables of technology and software. My team works closely with customers to ensure we can specify these requirements and enable customers to achieve their business goals and sustainability goals. As you pointed out, that’s a wide spectrum, so there are certainly challenges across the board. When it comes to AI, there’s a lot of excitement around it, and everybody wants to implement capabilities within the offers that we have. To mitigate the vastness of this, we enable ourselves to deliver use cases associated with the asset life cycle, but also build common capabilities. By building common capabilities, we ensure that everyone within the portfolio can drive faster time to deliver to customers. We have a combination of both, and that’s how we align it.
Most impactful AI projects
Gosia: That sounds like a vast project and product portfolio. Which areas among those that you are currently leading are bringing the most impact and value on industrial operations?
Kim: That is a very good question because there is so much hype in the market around AI. It’s important to state first that we focus our AI initiatives on use cases. We leverage AI where it supports the use case best. It’s not about the technology or a platform in general; it’s really the use cases that we focus on. AI is a critical enabler across the life cycle. We assess how we deliver capabilities because we have to look deeply at the person and what they are trying to achieve in the organization. We ensure we are making those tasks more efficient, which is certainly a great use case for AI. We take a coordinated approach to this. We have fit-for-purpose applications that have delivered significant return on investment over the last 20 years; AI is not new to AVEVA. We have also taken advantage of new AI technology to enhance the human experience and decision-making. For example, we have implemented an industrial AI assistant. The unique capability is that we’ve embedded that in the everyday workflows of the people within the organizations we target. It enables people across the business to very quickly, easily, and safely interrogate the data set available to them. It supports the new workforce with all the tribal knowledge of the company to enable a new way of looking at the data. We’ve made this very intuitive and provided transparency to help drive the most effective decisions. We have taken it a step further using generative AI capabilities. In the engineering area of our portfolio, one of the critical tasks is to lay out the piping as you design a new facility. This is critical for the flow of gases or liquids, but it is very meticulous, requiring a lot of detailed fine-tuning and measurement. We leverage AI to look at the existing topology—where the steel is, where the assets are—and then suggest to the user a couple of different routes that are optimal for piping across that design. We try to take the tasks that are necessary and critical but automate them, keeping the human at the center to make the choice on which route was best and most efficient for the long term.
What is AVEVA Connect?
Gosia: When I speak with industrial experts, many of them mention AVEVA Connect. Can you tell us a bit more about what it is and how it integrates AI?
Kim: Connect came to be because our customers were demanding a completely digital and highly interoperable set of experiences. They needed to support business resiliency, the new workforce, and break down silos. We developed an industrial intelligence platform that provided deep domain capabilities and technical and commercial flexibility. While it is a cloud offer, we can provide it as a hybrid experience, working with on-premise and Edge systems. The technical flexibility is tied to commercial flexibility, meaning you have a set of credits you can use for whatever is necessary for the organization, all with one experience. That essentially is Connect. Because we are able to bring the data sets together—whether it’s data we author through our applications, data we capture and store for customers, or data in other systems—we bring that together and put it into context. Once we shape that data with all of our 60 plus years of domain experience, it becomes ready for AI.
With the plethora of data and the opportunities of new technology, we want to make that available for AVEVA to create AI applications, but also for an ecosystem of partners that will build agents and provide AI and analytic capabilities that we want to bring into the platform. This gives our customers choice and an opportunity to have very unique use cases solved, whether by AVEVA or our partners, all on the same strong data foundation.
AI role in IT/OT
Gosia: We have a lot of innovation on the IT side and also innovation happening on the OT side. Can you share an example where AI has helped to bridge the gap between IT and OT?
Kim: What I’ve described so far really considers solutions for the OT (Operations Technology) team. We enable personas like operations managers, quality managers, reliability engineers, and people with pipe drawing capabilities. The capabilities we are bringing forward are focused on that user set. However, a new persona is emerging: the data scientist. To some degree, within the Connect platform, we have capabilities for that data scientist, but this scientist is trying to solve OT problems. They are the process engineer with technical skill who understands the landscape of the operations, and that’s where we focus Connect. But there are also other use cases where that data set is critical from an enterprise data science perspective—from the IT landscape. We have created an intrinsically connected integration with companies like DataBricks, Snowflake, Fabric, etc., where we keep the data in place. There is no copying of data, which is quite innovative, particularly the way we’ve worked this out with DataBricks. We share that information with the DataBricks offer, giving the data scientists in the enterprise access to trusted time series and engineering content they can leverage to solve different problems. The uniqueness is that as those calculations and data science work happen at the IT layer, the results—the enriched data set—come back to the OT layer. This is critical and quite unique to AVEVA. Even though we are sharing this information for a different use case, we believe the enriched content coming back to the OT layer is quite valuable for the personas I mentioned. It’s a very good example of having use cases from both IT and OT and ensuring that the results of both can be shared.
AI is transforming how we interact with data and systems
Gosia: Bottom line, it is changing the way we interact with data and systems. How do you see, from your experience and customer feedback, how AI is shifting the way that executives, operators, and engineers interact with data and systems?
Kim: As I mentioned earlier, there is a lot of hype around AI. However, if implemented correctly, I feel our industrial user community won’t even know there’s AI underneath. It will be democratized, and to some degree, it’s already starting to be. You have certain expectations when you look at software that capabilities are driven by AI. I want to reiterate my point that it’s not the AI; it’s the use case. When we can consistently provide the domain capabilities for these use cases, and customers can achieve efficiency, deliver projects on time and on budget, optimize assets, and make people more efficient within the organization, leveraging AI for that will simply be a matter of course. People will just assume it’s underneath.
Measuring the impact of AI
Gosia: Exactly. So, it’s either a good assistant or a tool working in the background, but the results are present. Let’s discuss results and impact. How do you advise measuring the impact of AI, not only on productivity but also on workforce empowerment?
Kim: I think this is really difficult, and this is why sometimes AI projects without purpose don’t see the results customers are looking for. The success of AI is truly tied to the success of the use case. This requires a combination of human intelligence that aligns and provides the guardrails, along with the technology. I have examples where customers today, especially in oil, gas, and power industries, have deployed predictive analytics, and the cost savings in these use cases are in the millions of dollars. We can look forward and predict patterns that allow people time to safely take down a line, examine equipment, and see something they might not otherwise notice. The cost avoidance savings and safety savings are in the millions with our customers. We also have solutions deployed in food and beverage. Here, we still provide anomaly detection capabilities for asset health, but we can also extend this to predict quality. Imagine being able to predict the quality of the pasta or the beverage being made; the customer satisfaction impact of that is quite large. Success criteria and measurement come from the correct use cases. In terms of empowerment, generally speaking, if you make a decision based on one piece of data, it might be good. But if you have two or three additional pieces of relevant content in context, your decision-making will be better. When you can access trusted data in context and uncover patterns much faster, this certainly provides confidence in the decision-making. It enables the workforce. For people who have been with an organization for some time, they understand the assets well. But for new people coming into the organization, making this kind of “tribal knowledge” available truly empowers them to understand, to have transparency of where the data comes from, and to make those decisions with confidence.
AI and sustainability
Gosia: That is a great impact. Since you have both AI and sustainability initiatives in your scope, do you have any particular examples where they both come together, or how do you manage this in your portfolio?
Kim: We think of sustainability in a couple of ways. First, we measure our products. We are part of the Green Design Foundation, ensuring we design products that keep sustainability in mind. We have benchmarked all our products for the power they consume. That is the technical side. On the use case side, it can be endless. By making the data I discussed available, people can instantly understand a trend in terms of emissions or carbon usage. Making the data available makes people realize the effect of their decisions. When we have dashboards focused on customer sustainability metrics, it enables people to make the right call. There are also use cases associated with the capabilities Connect provides. We can build simulation models to help with the reverse osmosis of salt water into fresh water, simulating what that process would look like for building desalination plants. Think about water safety, the efficiency of water use, or water leakage. In the water industry alone, leveraging information and technology gives you access to looking at things in a new way. The great combination is that it solves problems you weren’t able to solve before.
Future of AI
Gosia: It’s really exciting to hear about all these innovations that are already happening. Looking into the future, do you see any specific area or type of AI that will affect the future of employees, companies, business, and industry more than others?
Kim: This is a good question. Things are moving so quickly, and innovation is happening rapidly. I believe that in the industrial market, AI will certainly drive more collaboration between the silos of an organization. I feel it will also be much more personalized. Experiences will be developed and created dynamically for individuals, based on their location, role, and situation, because the AI will have access to all that context. AI will promote what it believes, based on all that data, that the individual needs to see and know at that moment in time, and you’ll be able to interact much more freely with that content. I believe that the human will always be at the center of this; AI will be a guidance, an advisory tool. That will always be the case because, in the industrial market, we are talking about very serious assets, processes, and challenges when things don’t go well. The human will always be in the loop. The AI innovation will focus more on individuals, understanding the situation, and suggesting the next move.
Gosia: How do we prepare for this? What would be your advice to industrial leaders to prepare for the future and their teams?
Kim: You have to embrace it. The tools created in the industrial market must have transparency regarding where the data is coming from so the human can be confident in the content to make the decision. I advise training and trying use cases. Don’t do a “big bang” approach to AI; instead, think about what specific problem you are going to solve, and then build confidence that the technology can support delivering on that use case. We have to proceed like that because of the nature of the industrial market.
Wrap up
Gosia: I think this is great advice. Thank you so much, Kim, for this conversation. I learned a lot, and I’m sure our audience did as well. It was a pleasure to host you today.
Kim: Thank you so much for having me.
Gosia: Thank you. And thank you to everyone who is watching and listening to us. Follow us on podcast platforms and on the YouTube channel so you can discover future episodes. Thank you.
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The first Schneider Electric podcast dedicated only to artificial intelligence is available on all streaming platforms. The AI at Scale podcast invites AI practitioners and AI experts to share their experiences, insights, and AI success stories. Through casual conversations, the show provides answers to questions such as: How do I implement AI successfully and sustainably? How do I make a real impact with AI? The AI at Scale podcast features real AI solutions and innovations and offers a sneak peek into the future.
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