In this episode of our podcast, David sits down (again 😀) with Aron Semle, CTO at HighByte, for a follow-up conversation roughly a year after our first podcast together.

A lot has changed in a year. When we last spoke, Industrial DataOps was still a concept many people needed convincing about. AI was already making waves, but the link between data foundations and AI readiness wasn’t as obvious to most manufacturing organisations as it is today. So where are we now? And where is the hype outpacing reality?

You can listen to the episode, or get the main ideas in this article! Let’s get into it.

We Don’t Sell the Problem Anymore

The market conversation has shifted in the last year. “We don’t sell the problem anymore,” Aron says. “People understand the problem. We have customers and prospects coming to us saying: we know we need to clean up this data to get AI-ready.”

That’s a big shift. And it mirrors what we see at the IT/OT Insider as well. During the peak of the LLM hype, everyone wanted AI but nobody wanted to talk about foundations. Now? People come to us and say: we want AI, and therefore we need to build a foundation.

That loop is finally closing. And AI is the catalyst.

Sebastian from Frost & Sullivan, who presented at HighByte’s DataOps Days late last year, made the argument that AI and DataOps are coupled — hand in hand — and showed the market growing accordingly. He’s right.

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Starting from Where You Are

So what does that foundation look like in practice?

Aron breaks it down using a digital maturity lens:

If you’ve got equipment with nothing connected and everything is manual, you’re not ready for AI on the factory floor. You need connectivity first — HMIs, SCADA, historians. If you’re at that level, you’re probably ready for the next step: Industrial DataOps.

The way HighByte positions it is straightforward. Industrial DataOps is not going to overlap with your existing systems that run the plant. It’s a layer you put on top — one that connects to all those systems, contextualises the data, and provides access to it from other consumers. AI being one of them.

The real work, though, is in harmonisation. “How do you consistently represent equipment that could be the same or completely different across facilities?” Aron asks. That’s the scalability piece. At one factory, the production order comes from an ERP or MES. At another, it’s a CSV file on someone’s desktop. If you’re consuming that data from AI — or from anything else — you shouldn’t need to know the difference.

UNS: Valuable, But It Was Always Just the Start

We couldn’t have this conversation without touching on Unified Namespace. And Aron’s perspective has matured in the same way the market has.

Five years ago, UNS was the start of DataOps at the edge: connect to everything, contextualise it, publish it to an MQTT broker. And for someone coming from nothing — like that bakery with independent machines and local HMIs everywhere — being able to subscribe to a broker and see the real-time state of the factory was genuinely transformative.

But now? Aron is candid: “Publishing to MQTT is just the transport of real-time data out. That strategy sticks around.”

With AI entering the picture, you also need historical data access, governance across multiple sites and contextual dimensions.

“It’s not that anything we did is nulled,” Aron clarifies. “It was the first step of the journey towards something bigger.”

LLMs and Agents: Let’s Demystify This

David pushes for definitions, and Aron delivers them with refreshing clarity.

* LLMs are the large neural networks most of us experience through chatbots. You put in a request, a response comes out. The context of the chat goes in each time (or a compressed version), and a new response is generated.

* Tool calling is when the LLM detects it can’t answer on its own and reaches out to external tools — this is where MCP comes into play.

* Agents are really just the code around the LLM. An agent is an LLM in a loop: instead of a human driving the conversation, the agent code handles tool calls, feeds results back into the context window, and decides when the task is done. How autonomous it is depends entirely on the code you write around that loop.

“But make no mistake about it,” Aron says. “We’re just building text documents and feeding them into a large language model. And then stuff comes out that’s sometimes in JSON format that we can code against. It’s not beyond that.”

LLMs are probabilistic. They’re hallucinating all the time and they just happen to be right a percentage of the time. In manufacturing, where determinism is fundamental, that’s a real constraint.

Now imagine that kind of confident hallucination in a pharmaceutical clean room. Or on a production line with multi-million-euro equipment.

“If we’re talking about fully autonomous agents that are going to get control of our machines — very bad idea,” Aron says flatly.

MCP: Getting Beat Up, But Here to Stay

MCP (Model Context Protocol) has had a steep rise — and Aron acknowledges it’s getting some backlash. That’s normal for anything with a sharp hype curve.

His take: “MCP is remote procedure calling for LLMs.“ It provides discovery, remote calling, and a bit of instruction in between. Where it gets criticised is by technologists who look at it and say: this should just be a REST API.

The funny thing? “If you go look at the latest version of MCP, it essentially is a REST API,” Aron says. “They got rid of server-sent events and it’s essentially an API now.”

The only scenario where MCP loses relevance, in Aron’s view, is if LLMs get good enough to call conventional APIs directly — and those APIs evolve to be more interaction-oriented rather than CRUD-style developer tools. But for HighByte, it doesn’t matter much either way: you’re still building the Industrial DataOps layer, the contextualisation, the custom pipelines. Whether the AI calls that via MCP or REST is an implementation detail.

What matters is that the tools are deterministic. “You define the MCP tool, its inputs, its outputs. If you get a call that’s trying to do SQL injection, you detect that and stop it with deterministic logic,” Aron explains. That’s why HighByte’s implementation was never a static, one-size-fits-all tool set. It’s designed so you build your own tools — like APIs — and control exactly what they do.

A Quick Teaser: i3X

We briefly touched on i3X, a new standard that Aron is heavily involved in alongside Jonathan Weiss and Matthew Paris. We won’t go into detail here — we’ll be recording a dedicated podcast with John Dyck and Jonathan Wise in the coming weeks — but the short version is this:

Aron sees i3X as a standardised API layer for the factory.

Every vendor already has APIs, but everyone does it their own way. If the industry can standardise how contextual data is accessed across vendors, it removes a massive amount of inefficiency.

“If cloud vendors step up and build clients for i3X, that is going to be the ingestion highway in and out of the factory,” Aron says. Version one of the spec is expected before the end of Q2 2026.

AI for DataOps: The Slider from Manual to Autonomous

Beyond using DataOps to feed AI (which is the foundation story), there’s the flip side: using AI to do DataOps faster.

HighByte’s latest release includes what they call a Pipeline AI Agent. Pipelines are their ETL tool for moving and transforming industrial data. The agent lets you prompt an LLM to analyse and edit pipeline configurations. What’s clever is the middle ground it occupies. Aron describes it as an AI slider:

On one end, fully manual — you know what you’re doing and you want full control. On the other end, full autonomy — let the AI handle it. The Pipeline AI Agent sits in between: human in the loop. You prompt it, the UI shows you inline what it edited, you review, accept or reject, and iterate.

This is where AI genuinely accelerates experts. Not by replacing them, but by handling the tedious parts so they can focus on what requires judgement. Give it a P&ID diagram, an Excel sheet, and access to an OPC server, and let it help you get started with contextualisation. Human in the loop to create deterministic output — absolutely needed — but accelerated by AI.

Wrapping Up

A year ago, we talked about Industrial DataOps as a discipline. Now it’s a recognised market segment. The conversation has shifted from “why do I need a data foundation?” to “how do I build one so AI can actually deliver?”

The takeaway from this conversation? The foundation hasn’t changed. The urgency has. And the hype-to-reality gap on agents and autonomous AI is still very wide. The vendors and practitioners who will win are the ones who are honest about where that gap lies — and focus on the use cases that deliver real value today.

HighByte will be at Hannover Messe 2026 (April 20-24) — look for Aron and his colleagues at the AWS, Siemens, and Microsoft booths.

Thanks for listening — and thanks to HighByte for sponsoring this one!

… and if you haven’t listened to the previous conversation we had with Aron, why not do it now?

About HighByte

HighByte is an industrial software company founded in 2018 in Portland, Maine USA. The company builds solutions that address the data architecture and integration challenges faced by manufacturers and industrial companies as they digitally transform. HighByte Intelligence Hub, the company’s proven Industrial DataOps software, provides modeled, ready-to-use data to the Cloud using a codeless interface to speed integration time and accelerate analytics. The Intelligence Hub has been deployed in more than a dozen countries by the world’s most innovative companies spanning a wide range of vertical markets, including automotive, energy, food and beverage, life sciences, and mining and metals.

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(*) At the IT/OT Insider we do value our independence and transparency. So as we look for ways to pay the bills we were looking for ways to work with sponsors without giving up on those principles. This is where the idea of sponsors comes from. Together with a few selected sponsors we’ll explore some topics that we both find interesting in the same way we write our normal articles. In the coming weeks you’ll find a couple of pieces that have been sponsored. Feel free to contact us if you are interested in a partnership as well.

Three years ago, over a glass of wine during summer break, we decided to start writing. No business plan, no editorial calendar — just a shared frustration that the conversation around IT and OT in manufacturing was either too technical or too abstract. We wanted something in between. Something practical, honest, and maybe a little opinionated.

This week, we are publishing our hundredth piece of content.

Some are articles, some are podcasts (like this one). Every single one started with the same question: does this actually help someone working at the intersection of IT and OT?

To celebrate we sat down in the same room (David’s living room, to be precise) and hit record. No guest this time. Just the two of us, looking back at what we’ve built — and looking forward to what’s coming next.

Here’s what we talked about…

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Our Favourite Podcasts

We’ve recorded roughly 40 conversations over the past two years, starting with Shiv Trisal from Databricks back in April 2024. Picking favourites is hard, but here we go:

Dan Jeavons on AI and Physics: If you’re drowning in “AI will change the world” marketing material, this one is the antidote. Dan takes a fundamentally different look at how AI can reshape manufacturing — not by adding chatbots to help files, but by understanding the physical world in ways that don’t require manually modelling every asset in the plant. It connects directly to the two problems standing between you and industrial AI we wrote about recently: the lack of an integrated digital twin and the lack of understanding of the physical twin.

Nikki Gonzales from Automation Ladies: Completely the other end of the spectrum. Nikki lives and breathes the SCADA/OT world, and she speaks for the smaller manufacturers — the ones who are twenty people, not the ones who can hire twenty people. If you’re North American and into that HMI/SCADA layer, also check out her OT SCADA Con conference.

Our Best Trips

We don’t just write and podcast — we also speak at conferences, attend events, and occasionally manage to be in the same country at the same time.

Vegas and ETLS: Last year, we were invited to speak at the Enterprise Technology Leadership Summit in Las Vegas, organised by our friends at IT Revolution. Here’s the thing: ETLS is an IT audience. The focus was squarely on generative AI, vibe coding, and the pace of change in the non-manufacturing world. For us, that contrast was gold. Seeing how fast things move outside manufacturing gives you perspective on what’s coming — and what’s different when your systems run 24/7 and a wrong deployment doesn’t just crash an app but shuts down a production line. Also, the Hoover Dam was genuinely impressive. Built to impress, and still functional.

Hannover Messe: The size of Hannover Messe is hard to describe — it’s a village. What’s valuable is sensing where the industry is heading, shaking hands with the people building things, and seeing the common themes across big tech vendors and small start-ups alike. We’ll be back this April.

Our Favourite Articles

Writing an article sounds simple. It is not. The writing itself is the easy part — getting it down to around 800 words and one core idea that you actually remember after reading? That takes iteration. A lot of it.

But the process forces you to think through your own ideas, and they often change while writing. That’s the real value.

Ceci n’est pas IT/OT Convergence: A nod to Magritte, because we’re Belgian and we can. This one was written in under an hour, almost out of pure frustration. The term “IT/OT convergence” dates back to Industry 4.0, and it has been misused so thoroughly that it’s lost meaning. It covers everything from adding an ethernet port to a sensor to deploying advanced AI models. People either love it or hate it, but most don’t realise it has both an organisational and a technical dimension — and conflating the two is where the trouble starts. It remains one of our most-read articles.

IT/OT Cooperation Models: Willem wrote this one early on — October or November of our first year — and it’s still our most shared article. The core idea: when IT and OT need to work together, the problems are rarely technical. It’s about how you cooperate. Inspired by DevOps and Team Topologies, we looked at cooperation from an IT/OT perspective and defined models that go beyond “let’s have more alignment meetings” (spoiler: those don’t work). Since then, our thinking has evolved. We’ve come to appreciate that simpler cooperation models aren’t necessarily low-maturity — they’re great when used for the right problem. You don’t need a full-fledged cooperative project team just to get a laptop sorted. But when you’re building something complex together, simple won’t cut it. Much of this updated thinking has already made it into the Academy, and we’ll be publishing more on the blog this year. Also, check out our mini IT/OT book library — it’s consistently one of our top-five reads.

The ITOT.Academy

Speaking of the Academy: we launched it last year, and it’s become one of the things we’re most proud of. The idea was born on a tram in Hannover and started from a simple question: what training would we actually want to follow ourselves?

Not a week-long course. Not someone reading slides at you. Not a massive one-directional webinar. Something live, interactive, vendor-neutral, and focused on the people and organisational side of IT/OT — not on protocols and programming.

We’ve now had close to 80 people go through the programme across our first groups. The interaction is bi-directional: we teach, but we learn just as much from the participants. People from different backgrounds, different industries, all working on fundamentally the same type of problem. And the feedback from one group genuinely shapes the next.

The next group starts May 22nd. Head over to itot.academy if you’re interested in joining.(or take a look to our Hall of Fame if you are not sure yet)

What’s Coming Next

Cybersecurity: This is a big one. We chose to focus here because cybersecurity in manufacturing is non-negotiable, and with NIS2 and the CRA, it’s coming to your company whether you want it or not.

What we’ve noticed: not everyone has the same understanding of what OT cybersecurity means. Is it an IT problem? Is manufacturing somehow exempt because it’s “different”? (Spoiler: no.) We’ll approach it the way we approach everything — people and organisation first, technology second. Translating the legal texts into what it actually means for your plant, your teams, and your processes. Expect articles, podcasts, and a few good stories.

Hannover Messe 2026: End of April. We’ll be there. We already have meetings planned, and we’ll record a podcast somewhere on the fairgrounds (hopefully not on an ironing board this time). If you want to meet up, reach out to us.

And we’ll be making a major announcement. We’re not saying what it is yet. You’ll have to stay tuned.

Thank You!

One hundred articles. Roughly forty podcasts. Three academies. Zero regrets.

None of this would exist without the people who read, listen, share, challenge, and reach out. You’ve made the IT/OT Insider what it is. We started with a glass of wine and a blank page. Three years later, we have a community — and we’re just getting started.

Until we meet again — take care.

David & Willem

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📣 A quick reminder before we start: Our next ITOT.Academy kicks off in May, and our early bird offer is available once more. Want to join our fourth group and learn how to bridge IT and OT? There is no better time than now! 👉 Check the curriculum & enrol via ITOT.Academy 👉

If you’ve been following our blog and podcast, you know we spend most of our time in what we call the IT/OT zone: data platforms, connectivity, governance, AI use cases, and everything in between. We’ve also covered the Purdue model, MES, UNS, and even Model Predictive Control. However, we rarely talk about what happens at Level 1 and Level 2 — the actual process control layer that keeps plants running. Not the data it produces. Not the dashboards built on top of it. The control itself.

So when we had the chance to sit down with Margret Bauer, Professor of Process Automation at the Hamburg University of Applied Sciences, we jumped at it. Margret is an electrical engineer by training, did her PhD in data analytics on process data back in the early 2000s (before “data analytics” was cool), worked for ABB Corporate Research, and even did early IT/OT integration work — connecting SAP with ABB’s 800xA system back in 2007. (Yes, 2007)

PID: The Most Important Algorithm Most Don’t Know About

Let’s talk about PID control.

Not P&ID (the diagram) — PID, short for proportional, integral, derivative. If you studied engineering, you probably had one half-lecture on it, sandwiched between Kalman filters and Lyapunov functions. Easy to overlook.

Except it runs the world.

Margret was blunt about this: 99.9% of all rockets that have flown into space run on PID control. All the robots you see online? PID underneath. Every valve opening and closing in a chemical plant, a refinery, a bakery? PID.

The concept is elegant: the proportional part looks at the present, the integral part looks at the past, and the derivative part looks at the future. Three aspects of time, one controller. As Margret put it: it has the worst name and the best track record of any control strategy out there.

But don’t let the simplicity fool you. In practice, PID is hard to implement well. Valves have physical limits — they can’t open beyond 100% (no matter how politely you ask). They take time to respond. And when you need to coordinate two valves for the same flow — say, one big valve for coarse control and a small valve for fine-tuning — the strategies on top of PID get complex fast. These layered strategies exist across every process plant, and they are the strategies that nobody outside the automation world ever talks about.

A Dying Breed

Margret posted on LinkedIn that process control engineers are a dying breed. When we asked why, her answer was painfully logical: the automation worked. Companies invested in control systems in the 1970s, 80s, and 90s. Plants got more stable. And then management looked at the 20-person controls department and said: “Why do we still need these people? The process runs fine.”

So they cut the teams. One by one, across the industry.

And that is a major problem.

In industry, many control departments are gone — and with them, the expertise to improve or even maintain automation performance. And in academia, process control is barely taught anymore. There are barely any new process control engineers coming through the pipeline. The academics who still focus on it? A handful worldwide, passionate but outnumbered (and Margret surely is passionate 🙂)

The AI Reality Check

Willem couldn’t resist: “Margret, of course, I’m going to come in with the solution for all your problems. You need to use AI. It’s going to solve everything.”

(We all laughed.)

Margret’s response was obviously more measured.

One of her master’s students developed a reinforcement learning algorithm for a batch penicillin process that improved throughput by 25%. Genuinely impressive. But it worked because the student had a well-understood simulation model. In the real world? The algorithm wasn’t scalable, wasn’t repeatable, and wouldn’t transfer to another process.

This ties straight into something we’ve been discussing a lot recently on this blog: the physical twin problem. AI models need to understand the underlying physics, the process behaviour, the control strategies. Without that, you’re optimising in a vacuum. David’s own experience with nonlinear MPC during his master’s thesis confirmed the same thing — beautiful results on simulated data, useless on real plant data.

The takeaway isn’t that AI can’t help. It’s that AI without process knowledge is just maths looking for a purpose.

The Operator Paradox

There’s another angle Margret brought up that resonated with us: the better your automation, the more bored your operators become. One of her students — a former operator — said she used to bring a book to her shift. Press the button, sit down, read for eight hours, hand over to the next shift.

That’s great from a stability standpoint. But it creates a dangerous gap. When something does go wrong — and it always does eventually — operators haven’t seen enough upsets to know how to respond.

The more you automate, the less exposed your operators are to disturbances, and the harder it becomes to train them for the exceptions.

And you can’t just “turn off the MPC layer to make things interesting again,” as David pointed out. So the industry adds another layer — operator training simulators, essentially flight simulators for plant operations. Layer upon layer upon layer.

Margret’s view? We’ll never fully automate everything. Every process is different, every plant is an individual. We’ll always need people. The question is how we keep them engaged, trained, and ready for the moments that matter.

Why This Matters for the IT/OT World

If you’re reading this blog, chances are you’re working on data platforms, digital twins, AI use cases, or integration architectures. All of that is important. But it all sits on top of a foundation that most of us take for granted.

Process automation isn’t a solved problem. It’s an under-invested, under-documented, under-appreciated layer that directly determines the quality of the data we work with, the stability of the processes we try to optimise, and the feasibility of the AI models we try to deploy.

If the foundation crumbles, nothing above it works.

So next time you’re debugging a data quality issue, or wondering why your AI model produces nonsense, or trying to understand why a sensor reading oscillates when it shouldn’t — maybe the answer isn’t in your data platform. Maybe it’s one layer below.

Find Margret on LinkedIn: https://www.linkedin.com/in/margret-bauer-a885618/

Book ‘Process Control in Practice’ mentioned during the podcast: https://www.amazon.de/Process-Control-Practice-Gruyter-Textbook/dp/3111103722

📣 Our next ITOT.Academy kicks off in May, and our early bird offer is available once more. Want to join our fourth group and learn how to bridge IT and OT? There is no better time than now! 👉 Check the curriculum & enrol via ITOT.Academy 👉

Stay Tuned for More!

Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence.

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

In this episode, Wybren van der Meer, a strategic data consultant, discusses the importance of data governance in industrial settings. He shares insights from his background in physics and experience in data management, emphasizing the need for a clear definition of data governance, the evolution of data practices in industry, and the role of trust and reliability in data management. The conversation also touches on practical applications of data governance, such as in coffee roasting, and the challenges of scaling governance practices across different plants. Wybren highlights the significance of starting small with governance initiatives while keeping the bigger picture in mind, and the necessity of engaging people in the process to ensure successful implementation.

Find Wybren on LinkedIn: https://www.linkedin.com/in/wvandermeer/ 

More on the Unified Namespace: https://www.youtube.com/watch?v=d1QeZWb6rt0

More on the Industrial Data Platform: https://www.youtube.com/watch?v=mdtY2Ks8F6M 

Learn everything about IT/OT Cooperation, Industrial DataOps and more: https://itot.academy 

More about The IT/OT Insider: https://itotinsider.com/ 

Chapters

00:00 Introduction to Data Governance and Wybren's Background

02:51 Understanding Data Governance in Industrial Contexts

05:59 The Evolution of Data Governance in Industry

09:12 Defining Data Governance and Its Importance

11:56 Implementing Data Governance: Challenges and Strategies

15:01 Data Governance in Coffee Roasting: A Practical Example

18:06 Scaling Data Governance Across Operations

20:52 The Role of Data Governance in New Projects

24:06 Overcoming Resistance to Data Governance

27:01 The Future of Data Governance in Industry

Welcome to the first IT/OT Insider Podcast of 2026! We’re kicking off the year with someone who’s done it all: refineries, equipment reliability, process safety, even the postal industry (and found data at the heart of every role).

Kris Doering recently joined SaskEnergy, a government-owned natural gas transportation company in Saskatchewan, where he works on system modelling and asset planning. But before that, he spent years at the Co-op Refinery Complex as superintendent of refinery performance improvement, working on benchmarking, goal-setting, and deploying process safety software. His career also includes stints in equipment reliability, Lean Six Sigma at Canada Post, and early days implementing PI System for upstream gas producers.

What ties it all together? Data. And not just collecting it.

From Postal Sorting to Refinery Benchmarking

Kris’s career path is anything but linear, and that’s precisely what makes his perspective valuable. As he put it:

“Data has really been a common thread through the whole career. No matter where I worked, what field I worked in, it’s really been the thing that’s tied all of my roles together.”

His time at Canada Post might surprise those who don’t think of postal services as manufacturing. But as Kris explained, the parallels are striking:

“You’re getting things off of semi-trailers, you’re sorting mail based on barcodes, you’re dealing with advertising mail, newspapers, parcels from Amazon. There’s a lot of infrastructure and a lot of processes.”

Those early Lean Six Sigma projects at Canada Post became foundational for everything that followed. “That work really kind of prepared me for all of the other stuff that I’ve done,” Kris noted.

Leading vs Lagging: Why Process Safety Metrics Matter

Our conversation centred on process safety. This is a topic that doesn’t always get enough attention outside refineries and chemical plants, but has lessons for anyone working with data and performance management.

Kris worked extensively with process safety at the refinery, deploying HSE software and investigating incidents. He explained the critical distinction between leading and lagging indicators: “A lagging indicator is when something bad happens. A leading indicator is something that you can measure that you think will correlate to the outcome.”

But here’s where it gets tricky. As Kris pointed out, truly leading indicators—ones that predict future incidents—are extraordinarily difficult to design:

“The problem with trying to create a leading indicator for process safety is that, you know, there’s an infinite number of things that could go wrong and an infinite number of conditions that could exist out there.”

Instead, what most organisations end up with are proxies—measures of how well they’re managing known risks. And that’s not necessarily a bad thing, as long as you’re honest about what you’re measuring.

Front-Line Scoreboards: Making Data Visible Where It Matters

Another practical insight from our conversation was Kris’s experience with front-line scoreboards—physical boards where teams track their own performance metrics.

“If you’re tracking the right information and putting it on a scoreboard that is understandable to the people who are doing the work, then those people actually engage with it. They want to know how they’re doing.”

This isn’t about surveillance or micromanagement. It’s about giving people the context they need to understand their impact:

“They know that they’re there to do a job and they want to know if they’re doing a good job or a bad job... and how to be better at their job.”

The key is connecting individual behaviour to outcomes in a way that’s visible and actionable. It’s deceptively simple, but as Kris noted,

“Connecting individual behaviour to organisational performance is an inherently complex problem, and replicating it through an organisation is complicated, too.”

Complex vs Complicated Work

Towards the end of our conversation, we touched on an important distinction that anyone in industrial operations should understand: the difference between complicated and complex work.

Complicated work has known solutions—it might be difficult to execute, but the path is clear. Complex work, on the other hand, involves uncertainty, ambiguity, and problems that aren’t well-defined. As Kris put it:

“It’s so important not to complexify things. You must come to the simplest solution. And as you gain more knowledge, more skill, more experience, what ends up happening is you recognise how to make things simple and break things down.”

The secret? “A desire to not choose to take on too much for myself.” Sometimes the most skilled move is knowing what not to do 🙂

Further Reading

If you want to dive deeper into some of the topics Kris discussed, here are two excellent resources he recommended:

* HSG 254: “Developing process safety indicators - A step-by-step guide for chemical and major hazard industries” Available free at: https://www.hse.gov.uk/pubns/priced/hsg254.pdf

* API RP 754: “Process Safety Performance Indicators for the Refining and Petrochemical Industries” Available (subscription required) at: https://www.apiwebstore.org/standards/754 Annex I is particularly recommended for defining process safety data requirements.

* The “useless machine”: https://www.cbc.ca/news/canada/saskatchewan/useless-machine-maker-from-regina-gaining-worldwide-fame-1.1326579

* And you can find the book “Sooner Safer Happier” by Jon Smart in our Mini Book Library.

Stay Tuned for More!

🚀 Join the ITOT.Academy (May and September Early birds now available) →

Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence.

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

Some guests make you pause halfway through the recording and think, “Okay… this one’s going to need a second listen.”

That was the case with Dan Jeavons, president of Applied Computing, formerly VP of Computational Science and Digital Innovation at Shell — and one of the people who has quite literally been shaping how data, AI, and physics come together in industry.

From ERP Reports to Foundation Models

He began, like so many, somewhere between spreadsheets and SAP.

“The biggest value of having an integrated system is the fact that you have an integrated data layer,” he recalls. “I didn’t like the systems much — but the data was really interesting.”

That curiosity led him from analytics experiments in R and MATLAB to building Shell’s first Advanced Analytics Center of Excellence — which, as he jokes, “was neither advanced nor excellent… but we got better quickly.”

Thirteen years later, he was leading teams across AI, data science, and advanced physics modeling — and wrestling with a problem that every industrial data leader knows too well:

“You either rely on physics and trade off flexibility, or you rely on statistics and trade off explainability.”

What AI Looks Like From the Plant Floor

Dan has worked across the energy value chain — from offshore wells to refineries — and says something that surprises many:

“From a data perspective, it all looks very similar.”

Distributed control systems, process historians… “whether you’re on a platform in the North Sea or in a petrochemicals plant, the data architecture doesn’t really change,” he says.

And that’s what makes the AI opportunity so big.

If every facility generates data in roughly the same way, then algorithms can be adapted and scaled — not rebuilt from scratch each time.

Why IT/OT Convergence Still Hasn’t Happened

At one point, we asked the question: Has IT/OT convergence really happened?

Dan didn’t hesitate:

“No. We’re only scratching the surface.”

He describes today’s operations as “a DCS at the heart of the operation, surrounded by siloed engineering processes — reliability, maintenance, safety — each with their own tool, using a fraction of the data.”

Adding AI layers on top of that, he argues, is helpful but incomplete:

“We’ve added a layer of intelligence on top of existing systems. But it hasn’t changed the work process yet.”

True convergence, he says, will come when AI doesn’t just analyze the work — it redefines it.

The Real Meaning of “Digital Twin”

Few topics create more buzz (or confusion) than digital twins. Dan gives one of the clearest definitions we’ve heard:

“A true digital twin must do three things: represent the physical world, be interrogable in real time, and run simulations that explain why and what next.”

That’s a high bar…

“The technology exists,” he says. “We just haven’t stitched it together yet.”

Change Management: The Hardest Part

Dan’s third “impossible problem” isn’t technical — it’s human.

“These facilities are extremely risky. They’ve run safely for 40 years. So when you say, ‘Let’s change everything,’ it’s a hard sell.”

He lays out the classic resistance:

* It works, don’t touch it.

* We can’t risk downtime.

* We’re here to deliver return on capital, not to experiment.

And yet, as he points out:

“Even with the way we run things today, we still have reliability problems, we still have safety exposure, and we’re losing expertise fast.”

His conclusion is blunt:

“Someone is going to figure this out — and when they do, they’ll be 50 % more efficient. If you’re not on that train when it happens… good luck.”

Rethinking the Cloud Debate

When the topic of cloud reliability came up (AWS outages, anyone?), Dan didn’t dodge.

“The idea that you’re safe because you’re air-gapped is a fallacy,” he said flatly. “Most OT environments are already virtualized — effectively private clouds. The question isn’t if you’re exposed, it’s how well you manage it.”

The challenge, he says, isn’t cyberthreats — it’s change management in the cloud era.

“Continuous deployment doesn’t work in operations. We need cloud architectures that respect industrial change control — and OT vendors who step up to modern security standards.”

From Use Cases to Foundation Models

Dan’s view of AI’s future is clear: we’re moving from narrow, use-case-specific algorithms to general-purpose foundation models that can reason across disciplines.

“Before 2023, companies built algorithms for individual problems: corrosion, valves, compressors. Now, the next generation of models will handle all of them because they understand physics, language, and time series together.”

He tells the story of Sam Tukra, his former colleague (now Applied Computing’s co-founder and Chief AI Officer alongside Callum Adamson) who figured out how to make those three domains “talk” to each other.

“He built an agentic system that cross-validated physics, language, and time series. I was equal parts proud, frustrated, and amazed. Suddenly, you realize — this is it.”

The result is Orbital, their platform that blends these layers — a system that can predict, explain, and reason across disciplines, from reliability to safety to economics.

Looking Ahead

Dan calls this convergence of physics and AI an “inflection point for industry.” He’s convinced that in the next decade, the companies who embrace it will operate differently — not because AI tells them what to do, but because it changes how they work.

So that means that we need to plan for another podcast in a year or so from now ;)

Thanks for listening!

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

Today’s guest has lived what most companies are still figuring out: how to turn fragmented systems, manual Excel work, and well-intended “shadow IT” into a coherent Industrial DataOps strategy that actually delivers value.

In this episode of the IT/OT Insider Podcast, we sat down with Rija Rakotoarisoa, Group IT Operations & Industry 4.0 Lead at Poclain Hydraulics, a French (international) independent group specializing in the design, manufacture and sale of hydrostatic / electrohydraulic transmissions: motors, pumps, valves, system for off-road or mobile machines and one of the global leaders in hydrostatic transmissions.

If you’ve ever found yourself trying to bridge IT and OT while juggling standardization, culture change, and budget cuts… you’ll feel very at home in Rija’s story.

From Developer to Industry 4.0 Leader

Rija started his career firmly on the IT side: a master’s in computer science, developer turned IT manager, working in a plant where his job was to keep systems running and people connected. Then came the shift.

“After five or six years, I felt like I had seen everything. I wanted to do something more than pure IT, something that had a direct impact on the business.”

So he went back to school, this time for a master’s in finance. Not because he loved accounting, but because it was his way to “remove the geek tag.”

“If you wanted to have more impact, you had to speak the business language.”

That change paid off. Rija became both IT and finance manager at one of the company’s plants and learned firsthand what happens when you put technology in service of the business. He used automation to help teams understand their own costs, improve efficiency, and cut the manual data entry that was eating up hours every day.

Lessons from Good and Bad Projects

In his later roles, including a global Industry 4.0 function, Rija saw dozens of digital projects across multiple plants. Some brilliant, others not so much.

“A bad example is when a company rolls out something top-down. They say, ‘This is the strategy, you must implement it,’ without asking the real problems at the plant. It takes time, money, and in the end, nobody uses it.”

Sound familiar?

The good examples, he says, start from the other direction. From real operational pain points.

“When you address the real problem in manufacturing - something that changes the day-to-day of the operational team - then they support you, they use it, and they apply it every day.”

It sounds simple, but as he adds, “it’s not.” It takes change management, communication, and people inside each plant who carry the message and help build local momentum.

Starting from a Digital Greenfield

When Rija joined Poclain Hydraulics, about 6 years ago, it was, as he puts it, “a digital greenfield.” The company had strong IT foundations (infrastructure, networks, ERP), but no consistent support for manufacturing systems yet.

“There were many IT/OT projects managed only by operational people. They cared about the end result, but not the implications in term of IT constraints. In the end, you have a big nightmare.”

In other words: well-intentioned local initiatives, zero standardization. The kind of environment where every plant has its own version of the truth.

So where do you start when the elephant is that big?

“We started with the most painful issue: the end-of-line quality control system. Each plant had its own version. We moved from local executable applications to a web-based, centralized one.”

Then came work instructions, and so on and so on. It was a classic “bite-by-bite” transformation.

How COVID Changed the Game

Like many others, Poclain had big plans for a global MES rollout. And then COVID hit. Budgets froze, priorities shifted, and suddenly the grand plan was off the table.

“We had to rethink everything. How can we do more with less? How can we use what we already have?”

What followed was a shift from “big system thinking” to a more agile, best-of-breed approach.

“I always say it’s not a happy event for everyone, but I thank COVID-19,” he laughs. “It forced us to be creative.”

That creativity led to the Data Hub project: a pragmatic approach to connecting existing systems, automating data collection, and building live dashboards that operators could actually use.

Building a DataOps Mindset

The guiding principle was simple: make data useful, make it live, and make it easy for non-IT users.

“I didn’t want my team to be the bottleneck. The system should be usable by non-IT people.”

That requirement drove their vendor evaluation which eventually led to selecting Litmus.io as their main Data Hub platform.

“Since 2021, we’ve been implementing Litmus as our main data hub. Step by step, we break the silos and build on it.”

But technology was only one part of the story. The harder part was governance and culture.

“It took a lot of time to explain to top management that the Data Hub is just an enabler. It’s not magic. You need something meaningful for the people at the plants on top of it.”

Standardization Without Killing Flexibility

Today, Poclain’s model combines global consistency with local agility.

“We master the data model centrally and duplicate it for each site. Plants can adapt the templates locally by defining their equipments and their mappings, but the core remains the same.”

The result?

Faster rollouts, cleaner data, and dashboards that update automatically without anyone touching Excel.

Rija’s model proves that digital transformation doesn’t have to mean disruption, just the right balance between structure and freedom, one data point at a time.

Interested in knowing more about Litmus? A few months ago we published our 5 Step Playbook for a Painless DataOps Rollout:

And have you already listened to our Industrial DataOps podcast with John Younes?

Stay Tuned for More!

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

📣 A quick reminder before we start: Our next ITOT.Academy kicks off on January 23, and our early bird offer is still available. Do you want to join our third group and learn how to bridge IT and OT? There is no better time than now! 👉 Check the curriculum on ITOT.Academy or in this previous article.

When Anton Melander joined Northvolt in 2018, the company had around 100 employees and zero factories. The goal? Build Europe’s first large-scale lithium-ion battery production from scratch — and do it fast.

“When I joined, there was nothing. I joined the digitalization department, which at the time was me and three others,” Anton told us. “We knew making batteries was fast — really fast — and with tight tolerances. Even microns of misalignment could lead to short circuits. So we wanted to be as data-driven as possible when scaling production.”

From that tiny team, Northvolt’s digitalization function grew to over 140 people, while the company itself ballooned to 5,000 employees. It’s a rare story (and one that ended too soon, after Northvolt’s bankruptcy earlier this year). But in those years, Anton learned what it truly means to build an OT organization from nothing, and why many of those lessons are now shaping his next chapter as a startup founder.

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Greenfield Decisions

Northvolt’s early advantage was also its biggest challenge: a completely blank slate.

“We had to figure out what to build and what to buy,” Anton said. “Off-the-shelf software meant long requirement lists, consultants, and change orders. Building in-house gave us flexibility, but it also meant taking full ownership.”

At the time, OPC UA was only beginning to gain traction. Northvolt pushed its equipment suppliers to provide compliant servers and built its own IoT platform to handle the data. “We provisioned over a thousand gateways,” Anton recalled. “Just setting that up was a project in itself.”

That internal platform became the backbone of production — the foundation of what they later called North Cloud, an internal MES connecting operations, quality, and material flow. “We built a lot ourselves,” Anton said, “but we still leveraged AWS for the cloud, and bought things like ERP and PLM. It was a mix, but a deliberate one.”

From Projects to Products

As production ramped up, the company’s digital organization had to evolve.

“In the beginning, we were completely project-based,” Anton explained. “But as production started, we realized that technology is one thing — it’s only useful if it actually helps people do their job. So we moved from a project-oriented way of working into a product-oriented one.”

That shift (which many IT/OT teams wrestle with) required a mindset change. “Everyone wants their feature,” he said. “The backlog keeps growing forever. But not everything that sounds important actually moves the needle. You need to tie initiatives to measurable results: quality, throughput, yield.”

He laughs looking back. “Sometimes senior stakeholders would say, ‘This is the most important thing,’ and you had to tell them: ‘It’s not going to increase throughput or quality.’ That’s the hard part.”

His other big lesson? Master data.

“You can’t calculate OEE if you don’t know the ideal cycle time. You can’t calculate downtime if you don’t know the planned uptime. It’s easy to draw perfect architecture diagrams, it’s harder to make them work in practice.”

What Comes After Northvolt

Anton left Northvolt in 2023, before its final collapse, but the experience left him with two realizations: first, that building your own tech stack is both empowering and costly and second, that most manufacturers will never have that luxury.

“More than 90 percent of manufacturing companies have fewer than 200 employees,” he said. “They can’t hire 140 people to build their own MES or IoT platform. And yet, they still need data.”

That insight became the starting point for his new company, Ronja, which focuses on helping small and mid-sized manufacturers make sense of the data they already have.

“We’ve spoken to more than 300 manufacturers since we started,” Anton told us. “Almost all of them say the same thing: they have lots of data, but they’re not using it. The problem isn’t collecting data, it’s getting value out of it.”

Ronja’s approach isn’t to replace systems, but to sit on top of what exists, making data accessible to non-technical users. “In most factories,” he said, “data lives in Excel, in emails, in historians. We help people connect it, visualize it, and analyze it faster — without waiting for a two-year MES rollout that eats the entire budget.”

Closing Thoughts

Northvolt’s story is one of ambition and hard-earned lessons: a company that built everything from scratch, scaled fast, and still couldn’t outrun industrial reality. But its alumni, like Anton, carry those lessons forward.

His takeaway applies to anyone working on digital transformation, from startups to global enterprises:

“The closer you are to the shopfloor, the more unique every factory becomes. You can’t standardize everything — but you can make it easier to understand, to learn, and to improve.”

And that, ultimately, is what industrial digitalization has always been about.

Stay Tuned for More!

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

In our earlier articles, we laid the groundwork for Industrial AI — breaking down the difference between classic AI, generative AI, and agentic AI. But frameworks alone don’t tell the full story. How do these ideas play out when you’re inside a real industrial company, tasked with building teams, getting budget, and making data actually deliver value?

For that perspective, we sat down with Nathalie Rigouts, who until recently headed data and analytics at Borealis and is now Head of Business Applications Data and AI at Umicore. Nathalie brings a refreshing, pragmatic voice — someone who moved from finance into IT, and who knows first-hand the reality of building data capabilities in industry.

From Finance to Data & AI

Nathalie didn’t start in IT. Her background is in finance, where every month she wrestled with massive spreadsheets just to get accurate actuals. That pain, she recalls, was the start of her data journey:

“Every month again, I was struggling with getting the correct actuals. And then of course, you have to make your forecast.”

From implementing a financial planning tool, to establishing BI at Borealis, to eventually leading data and analytics, her path shows how close the link is between business need and IT capability. And she’s clear about the lesson: it’s not about technology for its own sake.

“It’s not about implementing Microsoft Copilot. You’re not going to gain any sustainable advantage there. But if you can have a deep understanding of the processes in your company, and where data-driven solutions can help, that’s when you start to create value.”

Start Small, Sell the Success

One of the recurring themes in Nathalie’s story is pragmatism. At Borealis, the team started in 2016 with literally one data scientist and a laptop. “Python notebooks on a laptop, and we started.”

The key, she says, is to find enthusiastic allies and solve problems that matter. And once you do, don’t stay modest: market the success internally.

“We often forget to sell our success. I would go everywhere and talk about small things we did. And that’s how you gain support for the next steps.”

From that first laptop, the team grew, but only because each step came with visible, tangible wins that created pull from the business.

Use Cases That Matter

So what are typical use cases in manufacturing? Nathalie sees three common ones:

* Predictive maintenance: “If equipment fails often, anomaly detection and predictive maintenance are obvious first steps. But it’s not an easy nut to crack. Often, you don’t have enough failures to feed a model.”

* Quality control with computer vision: mainstream, but effective. With enough annotated pictures, good vs bad quality can be classified quickly. The catch? Data Quality.

* Logistics optimization: untangling shipping routes and optimizing delivery to customers with AI-based optimization models.

These are concrete, valuable problems — and they also highlight the role of data governance. As she recalls with a smile:

“We had beautifully annotated data — but all in Finnish. That’s when you realize governance is not optional.”

GenAI: Efficiency or Attractiveness?

When it comes to Generative AI, Nathalie is cautious. The business case is not always straightforward:

“I tried to make the case for Microsoft Copilot. At €30 per user, that’s not small. Does it reduce workforce? No. At best, people spend more time on value-added activities. But what does that bring to the bottom line? Hard to say.”

Yet she also sees why companies can’t ignore it.

“Companies have to invest in it because it will determine their attractiveness as an employer. New graduates take these tools for granted. If you don’t offer them, you won’t attract talent.”

She distinguishes between two levels: workplace efficiency (nice, but hard to quantify) and domain-specific models trained on your own IP. The latter, she believes, is where the real value lies. For example in pharma, where LLMs trained on internal knowledge can speed up R&D. “That’s when AI becomes a true digital co-worker.”

Governance, Change, and Legislation

On governance, Nathalie doesn’t mince words:

“It’s always the people, the processes, and the tools. The main component around which all of them center is the value case.”

Her advice: don’t let your solutions depend on a single enthusiast, and don’t leave an escape hatch back to the old way of working. Change management is part of the job.

And on legislation, she takes a positive view:

“It’s an opportunity. It forces us to think about awareness, ethics, governance, documentation, monitoring. All things that make sense. Yes, it’s work, but it helps you get budget and build maturity.”

Closing Thoughts

What we loved about Nathalie’s perspective is how grounded it is. No buzzwords, no silver bullets: just the reality of building teams, solving problems, and learning along the way. Whether it’s predictive maintenance, quality monitoring, or navigating the GenAI hype.

Her closing reminder:

“Keep it simple, be pragmatic. We built beautiful solutions with just scripting business rules. The business was happy, and nobody needed a fancy machine learning model.”

Stay Tuned for More!

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

When we talk about industrial connectivity, two names always come up: OPC UA and MQTT. They’re often mentioned in the same breath, as if they’re competitors. But as Kudzai Manditereza reminded us in our conversation, that’s a bit of a misconception. These protocols solve different problems, and understanding their history helps explain why they’re both so important today.

OPC UA: From Printer Drivers to Industrial Standards

The story of OPC goes back to the 1990s. At the time, every automation vendor shipped their own drivers, making integration a nightmare. The OPC Foundation stepped in to create a standardized interface — inspired, of all things, by Microsoft’s printer driver model. Just as Windows could talk to any printer through a standard interface, OPC offered a way for SCADA systems and historians to talk to PLCs without custom drivers.

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The first generation, known as OPC Classic (DA/HDA), was Windows-only and limited in scope. It solved the immediate problem but couldn’t handle the growing complexity of industrial data. Enter OPC UA (Unified Architecture): cross-platform, internet-capable, and built with powerful information modeling.

This is where OPC really shines. As Kudzai put it:

“The shop floor is full of objects — pumps, compressors, machines. OPC UA lets you model those objects, not just pass around raw tags.”

That means a machine builder can ship a unit with a pre-built OPC UA information model, ready for plug-and-play integration.

The OPC Foundation even created companion specifications for different industries, so a compressor in Germany “speaks” the same OPC language as a compressor in the US. No more reinventing interfaces for every project.

MQTT: Born in the Oil Fields, Adopted by the Internet

If OPC UA came from printer drivers, MQTT came from oil pipelines (well… actually from the even older pub-sub newsgroups back when the internet was still something really special).

In 1999, IBM engineers developed MQTT to monitor pipelines over unreliable, low-bandwidth satellite links. The key innovation was the publish/subscribe model: instead of clients constantly polling servers for updates, devices publish data to a central broker, and anyone interested can subscribe.

This lightweight, bandwidth-efficient design made MQTT perfect for remote monitoring. But it didn’t stay confined to industry. In fact, one of its biggest early adopters was Facebook, who used MQTT in their Messenger platform. By the 2010s, MQTT had made its way back to industry, now riding the wave of IIoT and event-driven architectures.

As Kudzai explained:

“MQTT doesn’t tell you how to model your data. It’s a transport protocol. But its hierarchical topic structure maps naturally to concepts like the Unified Namespace (UNS).”

Think of it like a Google Drive folder structure: data is organized into topics, and anyone can subscribe to the parts they care about.

OPC UA vs MQTT: Different Tools, Different Jobs

So should you pick OPC UA or MQTT? The answer is: both, but for different layers.

* OPC UA excels close to the machines (Levels 0–2 in the Purdue Model). It provides a rich, standardized way to model and expose machine data. Perfect for SCADA, DCS, and local control.

* MQTT shines at higher levels (L3/DMZ and above). It’s ideal for integrating thousands of devices into enterprise systems, feeding data lakes, or enabling event-driven architectures. And of course also for IIoT devices spread around the world!

As Kudzai put it:

“You’ll never control a pump with MQTT. But if you want to share events across your enterprise (machine status, recipes, quality data,…) MQTT is a great fit.”

And that’s an important distinction. OPC UA is about structured access to objects. MQTT is about lightweight distribution of events. They don’t replace each other — they complement each other.

Closing Thoughts

Industrial connectivity isn’t about choosing one protocol over the other. It’s about using the right tool for the job. OPC UA and MQTT are part of the same toolbox — and when used together, they unlock scalable, reusable, event-driven architectures that finally let IT and OT speak the same language.

As Kudzai summed it up:

“The ability to reuse data is a huge factor. Once you stop thinking point-to-point and start thinking platform, that’s when scale happens.”

… And we couldn't agree more!

Also, take a look what HiveMQ has to offer:

Stay Tuned for More!

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.