The IT/OT Insider Podcast with David and Willem

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.


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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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This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com

📣 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.
🚀 See you in the next episode!
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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.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com

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


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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.
Youtube: https://www.youtube.com/@TheITOTInsider Apple Podcasts:
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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.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com