Luke van Enkhuizen: welcome back to the smart metals podcast, the show where we talk about digital transformation for the metals industry.

My name is Luke van Enkhuizen and I'm together together here with.

Denis Gontcharov: Denis Gontcharov Luke van Enkhuizen: Today, we will be talking about the future of your factory.

Particularly, we'll be talking about something a little bit controversial, a bit of a thought experiment, where we dive into the value of domain knowledge and how important that is for the future of your factory and how this will maybe change if we will use a more unified architecture, such as unified namespace.

So let's kick it off there and let's talk about this today.

Denis Gontcharov: Yes.

I'm personally very excited about this topic.

We are entering an age of AI and I think it's best illustrated with this quote.

It's actually quite old.

The quote reads that the factory of the future will have only two employees, a man and a dog.

man will be there to feed the dog the dog will be there to keep the man from touching the equipment.

a quote from Warren G.

Bennis.

I think we can't deny that we are entering a time where more and more of the thinking and decision making is being outsourced to machines.

For example, scheduling decisions, process control has already been automated.

before to make these kind of complicated process decisions, you needed to have an engineer who had sufficient domain knowledge to operate machines and the process.

given that we are connecting systems into one unified namespace.

We can imagine having an artificial intelligence that will take some of this decision making on it.

What do you think, Luke?

Luke van Enkhuizen: Yeah, this is actually, I think already happening, right.

In other ways, in many other tangent industries.

I mean, like in the financial services markets, a lot of things already done by algorithms and rules.

And I think we see these rules already around us in many other industries as well.

So I think it's a matter of time until this really breaks through also in the metals industry on a larger scale.

The question is only how this happened and what do we need to get there?

I guess.

So it's not the question of if, but more like when and how, and what do we need to get there?

Denis Gontcharov: I really like your example about the financial industry, because indeed it's also a very high stakes game.

If you make a mistake, you lose a lot of money and financial markets are arguably more complex because you don't have physical laws by which nature operates.

So you're dealing with more stochastic processes.

And if they can't do it, well, why can't it happen in manufacturing?

Luke van Enkhuizen: yeah, exactly.

And also like it, it's not then the, that's the systems are using anything else than we are using.

We are both using data.

We are both using process values instead of it's the change of price of a stock or an index or anything like this.

\It's the change of a, the value of.

A temperature, for example, or in a more discrete sense, it's the time that a certain operation took and which parameters were part of that.

So eventually you can plot everything on a time based data series, and it should give you some insights for decision making.

Right.

Denis Gontcharov: For sure.

And previously, these decisions were always made by a human mind with lots of experience.

I'm thinking of a process engineer who studied thermodynamics or studied discrete manufacturing supply chain.

So, but let's pause for a second and try to define what's in this person's head.

Look, how would you define domain knowledge?

Luke van Enkhuizen: Yeah.

It's like the know how I think.

In my industry, in the metal fabrication industry, it's also called tribal knowledge sometimes even it's like the, the, the things that you know among each other about how things supposed to be, how the processes are, what means what, What does it mean that the temperature has been in this range for so long?

Does it indicate that the machine needs some changeover soon?

It needs to repair or not?

Does it normal that the machine stands still?

Does it tick on a funny way?

I don't know.

There's so many ways you can exemplify, but also what kind of customers need.

What kind of things customers need from you?

There's so many things that you know, your company as your domain, that's the domain you're in and the knowledge that that sets the rules of the game that you internally have to find for yourself.

And whether it's that it is in a document perhaps it's a process documentation whether it's, it's a rule book, I think it's a data set.

It could be anything in between, but in summary, the things that make your company.

What you are today.

And maybe you can call it even your secret sauce.

Denis Gontcharov: Yeah, absolutely.

And I think we have to really make a distinction between information and experience.

Because at the end of the day, all the information is technically there.

You have all the sensor data, you have your rule books, you have your manuals, but you still need the person that has the experience.

And I think the way to build domain knowledge is by blood, sweat and tears, by studying the information and applying it in your work.

And that's why it's so hard to find and so hard to replace.

I think that's why also Boeing has a lot of issues now that they are losing the domain knowledge.

By experienced engineers retiring.

Luke van Enkhuizen: Yeah.

It's funny you mentioned blood, sweat and tears of course, we don't want to have blood, sweat and tears in a factory, I hope, but yeah, the essence is very clear that there is a history that you've built up about certain smart ways of doing things whether it's fabricating parts, whether it is your continuous process, there are all kinds Smart things you have done over the years to make it better, to give you that edge over the competition or avoid quality issues, but all of these things are happening over time and all these things are in the history of the company, it says that that may be not available for the rest of the company or even the group to do something with.

So AI will be miles away if we have no way to give that information further to an AI system.

So you might have lots of experience, but if there's just no data, if it's siloed away somewhere, you cannot access it.

And if you're also not recording it, there's also not much to analyze upon.

And so then it comes to be the question of what is the physical reality, which you cannot change, which is something that is just there, which you can measure.

And which are decisions that are being made during the course of a production run, for example, that influenced that physical reality.

And those are two different things.

Usually they are called actually the IT and OT stuff because IT plans something.

There is something made in the ERP system.

There is a project being done.

And then in the factory, something happens.

And how do you bring those two together?

That's I think where actually domain knowledge comes to being right.

It's not just what happens on the floor.

It's just not what the schedule you need both.

Denis Gontcharov: Yes.

I think by your expression of you need both is very relevant here.

Let's focus a discussion on a concrete use case of, for example, building a process dashboard.

In my experience, the main problem has been that the domain experts, they know what they need, but they are not able to build it because it requires very strong digital skills.

Whereas the IT people, they know how to build it, but they don't have the knowledge to understand what they have to build.

So you have these two different teams and with different skills, and they both have to be aligned to create something that generates value for the business.

How would you approach this problem?

Luke van Enkhuizen: Yeah, I think this is a, it's a perfect example because you can have that dashboard built very quickly, if you have already all the information in place, if you have all your ducks in a row, you know exactly what needs to be done.

It's relatively easy to do this, but if you don't, then you need to rely on a lot of knowledge that you have to be acquired, whether experience from a consultant or from a software that is a pre built solution, or you have to make something yourself.

And I think that's where the distinction is going to lie what you're going to do and how long it will take.

Denis Gontcharov: Would you say that domain experts always know what they need?

An additional controversial stance on top of our earlier mention of having only an AI in the factory is that you could argue that in the times of Henry Ford, when he developed the automobile, the people were asking for faster horses, not automobiles.

So aren't we constraining ourselves by only taking into account the opinion of the main experts?

Luke van Enkhuizen: Yeah.

Well, and this is a very interesting because if that person is a someone that is in charge of selling horses, obviously they're going to sell you more horses.

And if you're going to design an automobile.

You need to have like, you know, a bit of a different approach.

And I think this is very similar to what's happening in the industry, right?

You have large established players that have fleets of software consultants and solution architects that pay for outlets to promote their product as the one and only solution.

And it's faster, it's better, exactly all these things, but it's not different.

It's just.

Bigger and larger and more complex.

And if you then try, if you decide to completely turn it around and you say, okay, no, we're going to go for the approach of the unified namespace.

We're going to put all our data in a structured way, see it as the first class citizen approaches from a digital first perspective and build that foundation where all the knowledge is going to go into, yeah, you will find completely different answers.

And I think of course.

Domain knowledge is super important and needs to be captured in various ways.

But it comes from both sides.

It comes from what you already have created, what you already have built.

And even it people already in the company already built things and the probably that the operators don't know about and vice versa, there is already data in various places.

So it's really important to bring that together.

Denis Gontcharov: I fully agree.

At the end of the day, the way I see it is that both camps.

We'll have to learn some of the language of the other camp.

So what I mean by this is that the domain experts, they should be aware of the new possibilities that are possible with digital infrastructure, like the unified namespace, but they'd also with AI and conversely, the IT people, they cannot just stay in their realm of bits and bytes.

They also will have to do an effort to better understand the needs.

Of the, the main experts to better be able to sell or to tailor their it solution to their problems.

Luke van Enkhuizen: Yeah, definitely fully agree there.

I am also not saying myself a couple of years back, I would also still preach that ERP would be the way forward for companies to make their central source of truth, for example, and put all the knowledge of their company in there and me as a domain expert would amplify that message from ERP companies and now only recently something new has to be coming in that shows you there is another way for example, event driven architecture and this allows you to capture what's happening in the factory as the events as they reoccur, just like you would be looking in a factory and seeing things happening event by event and machine starts and stops and order is launched.

An order is created changed, all these things are events.

And so it is kind of like, I'm in the domain of that factory and I'm an industry expert.

That's what I mean with domain expertise.

But when I mean domain knowledge, I mean more.

The knowledge that's in the factory itself, like that you're building up the experience.

for me, so that's more like the site and the enterprise in my definition, perhaps we can improve that a bit, but so from that perspective there, I think that if you looking at the domain knowledge in your factory,, what's going on, you can observe it.

Right.

And that's also the observability is what makes everything different.

If you do it right.

If you record everything in history, because then at a certain point, all your knowledge will be.

Already there.

You just don't see it.

That's where AI can come in to process vast amount of data into information that you can take action upon.

So I think that's the major shift you can have a horse that runs faster, but you can have a car that's built completely different.

And there's a chemical process going on completely different than what's happening in a horse.

In the end of the day, they both move you forward, but the car will do it much faster, much further, has much more growth potential.

And so forth.

Denis Gontcharov: Yeah.

I like your distinction between domain expertise, something that the consultant should have to empathize and to understand the domain expert who himself or herself has the very deep domain knowledge.

I think by you having this expertise, that way you realize that, hold on.

I shouldn't sell horses.

I shouldn't be the ERP expert.

Instead, I should really think about what's good for my industry.

And that, I guess, made you realize that no, actually the unified namespace would be a better candidate to hold all the data centrally.

Luke van Enkhuizen: Yeah.

I've been converted to that religion as well.

Sometimes I joke about it.

And this is so relevant for that future, that story that you said in the beginning about the factory of the future where it runs automated.

I only think the only way how this is possible is that events are recorded as they happen.

They are showing you a pattern, even though you don't see it yourself.

It's like the stock market in the beginning.

I can, you know, we can both look at charts and use software to make some visualizations, but on a larger scale, you need more than that.

You need multiple layers of calculations and complexities to see what's the market trend is really going right.

And so the same goes for your factory.

So it, it will need all that data from years of history to predict some of the future because the history doesn't always repeat.

But it, it rhymes, right?

And, and that's basically where then if you have a long term at a time, start capturing all these events and use that to then control the process with as well.

If a state changes or a calculation changes, then make a process decision to change something in the future.

That is where I think the real magic happened.

But the major distinction is that it's real time.

And it's based on historical data.

The historian is in place.

And instead of something that is only responding to the data in a moment.

Like if you look at it, for example, a swimming pool, the water level in the swimming pool is always leveled the same way.

How is that possible?

If somebody jumps in and water leaks out, that's happens with a very simple process that just checks the water level, right?

That's a feedback loop as well in itself.

But it doesn't record how much that water level has changed over time.

And it doesn't allow you to see what's happening around it, not only with the pool, but the whole, the whole building itself and how many people are buying tickets for the pool or something like that.

So it's like it, it extends is way beyond the boundaries of the moment.

Right.

And that's what you need for AI.

I think to make decisions upon.

Denis Gontcharov: Yeah, for sure.

I want to refocus back on the role of AI.

We mentioned earlier in the podcast that there'll be a man and a dog.

I don't fully agree with this.

I think the promise of AI will be an automation of a lot of decisions that were usually made by domain knowledge, but I don't think an engineer will ever be able to be fully replaced For the simple reason that AI relies on data and the data is not always there.

We are limited by the sensors that we can install, especially we don't have a complete view, but what I think is that AI will perhaps, it's hard to put a number on it, but it will definitely outsource a lot of the lower level decisions that an engineer makes now.

The main knowledge will always in that regard be important.

In the near future to design the dashboards that we need so that to find use case that create the value.

But even in the future, even when we have an AI steering most of the process, I think the engineer will always be there to watch over the AI, assume responsibility and perhaps manage the most difficult, ambiguous decisions.

Do you tend to agree with this statement?

Luke van Enkhuizen: I'm not entirely sure.

I don't have enough domain expertise for your specific metals background in the aluminum industry.

For example, I don't have the domain expertise.

So this is such a thing example.

I wouldn't know because I don't know exactly is the, the domain knowledge that I need to have the domain expertise for your specific aluminum industry example.

So I would not be able to.

Judge the book by its cover and saying.

Oh, I think, yeah, sure.

It's aluminum.

It must be a continuous process.

Oh, it sounds like temperatures.

Sure we can do this for the argument, but I don't know.

And I think anybody that will say that to a company like saying, Oh, you know, we are experts in digital transformation we work with various industries.

We know just the solution for you.

I mean, I don't know.

There must be like having some psychic abilities or something, but I don't think they can know this.

This is where I think your expertise shows for years in a factory for my background.

I can say, yes, I think.

A lot of processes around starting manufacturing, logistics, self driving vehicles, basically everything that is somewhat predictable and can be simulated.

I think that's a very important distinction as well.

Can you simulate it with the software?

So for example, a sheet metal part can be bent.

You can simulate that.

Logistics, you can simulate that.

So if you can simulate it, then.

I think in the end, you can fully automate it without humans, but everything that's not in that realm that you cannot really simulate because there's too many variables.

I don't think you can, like, that's where you need a lot more of computing power that we are not at yet, but everything depends on the time frame we're looking at here.

Are we looking at next year, five years, 10 years?

And then I cannot look so far in the future because two years ago, did we expect that AI tools like just large language models would be so powerful today already.

I didn't expect that.

I'm still blown away every day that I work with them.

So, and seeing how this speeds up like developments, I don't know.

It's hard to predict the exponential growth.

Denis Gontcharov: Yeah, it's a good point.

I think we really have to be think about where we apply the AI to which games we let it play.

Okay.

I think the things you mentioned, things like supply chain planning, those are relatively deterministic, like a game of chess and can be calculated.

Whereas if you look at, for example, in aluminium production, the electrolysis cell, the process that we have been running for the last 200 years, almost.

We still don't really know what happens chemically inside the cell.

So that it's essentially still a very big black box.

We know that which levers we pull, that aluminum comes out.

But we don't have a complete scientific explanation of what happens there.

And I think that's also part of the beauty of this process, that we will always need someone with a gut feeling to decide what will happen.

Because the cell may go out of control for a variety of reasons, and it's not really clear what's going on before the fact.

Luke van Enkhuizen: That's fascinating.

I had no idea of this.

I think, a perfect example of your domain expertise that you have.

Let's uncover this a little bit for just a few minutes.

What's, what's happening in an aluminum smelter just for an example here.

And why is it so hard to fully automate?

Denis Gontcharov: Sure, so if we imagine an electrolysis process to produce aluminium, what we essentially have is a big bottle, made of steel, that is filled with molten salt.

So it's essentially a very toxic mixture of ions and in which we slowly dissolve aluminium oxide, just aluminium bound to the oxygen.

And the name of the game is to rip the oxygen from the aluminium so that you get pure aluminium.

Now, the way this dissolution happens of the aluminium oxide in the, what we call the cryolite molten salt is unknown.

We are not sure which molecules or which ions are forming.

are, we have some idea.

The only thing we know is that we heat it to a 960 degree temperature and pump a large electric current through it, but we get aluminum at the bottom and CO2 that evaporates, but that's essentially it.

We have some understanding of the physics and chemistry, but not more.

Then there's also a lot of other effects such as magnetic waves because of the very large electric current.

You have the heat losses, and then you have all the bath chemistry that changes.

Essentially, you're steering the process with just a few levers.

You're changing the voltage, you can add some pepper and salt, you can change the distance of the anodes and the cathodes, you observe the bath chemistry by measuring only once a day, and this is where the problem occurs.

Because it's such a harsh environment, it's so dusty, there's strong magnetic fields, there's a high temperature, very toxic chemistry, is that you can only measure, and also don't forget you have not one pot, but you have up to hundreds of pots in one factory.

So there's like no way you can observe all of them in complete detail if you have to make , manual measurements.

So what you do is you essentially do the best you can.

With a very few information that you have.

Luke van Enkhuizen: What kind of data points do they normally collect in a process like this?

Denis Gontcharov: So the information you have on a continuous scale is the voltage and the current, the temperature of the cell.

You have like once per day, if a team goes and measure it, you also tend to measure the bot chemistry.

Also every, let's say 32 hours up to 34 hours, you can also measure the composition of the metal to see how pure it is.

You can also measure certain voltage drops to see how much.

of the electrical energy you lose in your connections.

But that's really about it.

You don't really have that much to work it.

So you have, let's say, a dozen of input variables, which you have to steer a physical process that you barely understand.

It's a bit like cooking with experience.

You're cooking the steak, you're adding something, but you're not really sure of the exact chemistry.

But you don't have to as a cook.

If you're an experienced cook like Gordon Ramsay, you can cook the perfect steak without knowing anything about the chemistry.

Luke van Enkhuizen: Right.

Because the compound of the steak, the meat itself could be a little bit different, the butter could be a little bit different, the environmental conditions, you observe the changes, what you're seeing visually, and you are perhaps smelling it whatever, all these things.

But so why are you, are you then limited to collecting data points only once a day by checking into these spots what limits, for example The, operators from capturing these data points every second or every millisecond, even.

Denis Gontcharov: The number of pots you have in a hundred of electrolysis spots, bottoms in a single factory.

You only have a team of, let's say five people that are responsible for the measurements and they can only do so many measurements per day.

Physically.

You can't use robots or fully automate this process because of the strong magnetic fields that damages the equipment.

You also have the very high temperature and the dirty environments, but of course, also the cost.

Imagine even if you could develop a fully autonomous robot, you would which some plants are actually developing right now.

The benefit still has to outweigh the costs.

Luke van Enkhuizen: And so let me get this right.

So you cannot place normal sensors, even industrial grade sensors in this process on various data point that are capturing continuously because of the magnetic fields, or do I get this wrong?

So a normal temperature sensor.

Denis Gontcharov: Partly the magnetic fields, partly the heat, but also the costs.

There are always been projects where they want to retrofit existing cells by, for example, adding a mesh around each anode block through which the current flows.

This would give you exact information about how much electricity goes into the bath.

From every anode and that will give you very useful information.

Problem is that retrofitting all of your hundreds of bots with this sensor technology is extremely expensive.

And the process itself, just by having 200 years of experience is already about 95 percent optimal.

So the question is with a few additional percentage points of more efficiency that you gain with this technology, would you cover the cost of investment?

And that question is still being debated to this day Luke van Enkhuizen: That's a very interesting topic to emphasize.

It's sort of decision on when, when it makes sense to invest into further collection of data points depends highly on how accurate your current process is.

And again, the domain knowledge from 200 years of experience, if this makes sense or not.

And so there will be people on the shop floor saying, we got this, we're doing this for a whole career.

We know how it's like, we just want it a little bit better.

A little bit faster, maybe, and then there might be people like us coming from the outside or anybody else saying, where's the data?

Give me more data at all costs because data is the most important thing because that will show us things we don't even know that are there in the first place.

How I observe it a little bit here, like not saying one of the two is right.

I'm just like, it's a difference between approaches, I guess.

Denis Gontcharov: for sure.

But data is important.

For example, an area where I see a lot of potential for improvement is not necessarily a better steering of the process.

That's arguably already good enough.

It's more when people do operations on the pot, when they replace an anode or when they start up a new cell, the way people like manual, whenever a manual decision is made by an operator, that's where you introduce variability.

So a big trend in the industry right now is trying to record how well a certain operation on the pot has been performed to then potentially identify problems before they occur.

Luke van Enkhuizen: So there's this predictive maintenance in this, this context here, but in a certain way, right.

Denis Gontcharov: Yeah, it can be compared to it.

It's more about checking like verification of the operations made by a human, Luke van Enkhuizen: Right.

But for this, you also need to capture a lot of data then to, to make these kinds of predictions, I assume.

So in most smelters, do they already capture historical data in a somewhat structured manner about starting and stop and those kinds of things?

Denis Gontcharov: mostly on paper.

One example I've seen which really impressed me was that a lot of operations are done by a crane.

That moves across the pots.

So above them in the air, what they have did is that they mounted a camera on the operator seat that records the operation.

And, but that's where you also have in your MES system, the start and the stop of a certain operation, because after the press of a button, I am now changing anode one, two, three, the camera starts recording.

And then when he clicks on, I finished my operation.

You have all the information you need.

You have the start and the end of his operation and you have all the video material.

That is being analyzed by an AI to gauge how well the operation has been done.

But that's something relatively new in the industry.

Luke van Enkhuizen: Yeah.

Vision systems.

I was actually already thinking about this in the beginning when you told me about observing if a process is going well.

I was like, what can you do with vision?

What can you do with sensors?

What can you do even with sounds or even measuring magnetic fields?

There are probably ways that you could in a certain way, measure all these things.

And they should give you some hints and insights how well things are going and where improvement lies and the effective OE and which again, you will need if you didn't start bringing in outside factors.

I think in a previous episode, you mentioned energy consumption and optimization.

And I think this is huge that you are strategically can time your maintenance on times where the energy grid requires you to do so or predict even when you should do this based on the energy grid usage, just thinking out of the box here a bit, but I think those are kind of the things that you could do if you set it up, right.

So I see it from an outsider, but you are the domain expert here.

I'm just asking you now how you feel about this.

Denis Gontcharov: Well, I'm not a domain expert, so to speak.

I have some domain knowledge of the process, but I think that's actually a great point to tie it back to the discussion about the importance of domain knowledge.

How can you tell if an anode has been changed correctly or not?

With my knowledge, I can just realize that this is important.

It's important to ask, but then an actual domain expert, which would be like the engineer or the operator working there for over a decade.

We need the information.

We need the domain knowledge of those people to be able to Label the subsequent AI data, the visual data that we generate.

Luke van Enkhuizen: Yes, indeed.

So that would be a perfect example in it.

So how does a good change look like and how is that reflected in the data in other places down in the process?

So for example, if you make that change, did anything else change in measures right after that?

Something that you might have not noticed.

You know, like there could be so many factors that are related that we just don't see with the naked eye.

I think it's good example because when we go back to the beginning, we'll be just a factory with a man in a dog.

There is potential for it, I feel, but I think there's a long way to go with robotics and just the physical limitations.

But it surprises me a little bit that a human can do it and not a robot, but it has to do with some environments.

But then are these really like the first principles that you can really not change?

I somehow feel that if we find a way to overcome these things, then it's just a cost question.

And if it's a cost question, then I think we should include a lot more than just what's happening inside the four walls, but also externally.

As for example, I mentioned energy or regulations or CO2 all kinds of things you can bring in or supply chain optimization to make the decision a bit more a bit more founded in data.

Denis Gontcharov: Let's focus on your question of, do you need a human for this decision?

And I think it's a very important question.

And the answer is yes or no.

If you train the vision model to recognize if NLs are placed correctly or not.

I'm a strong believer that this can be completely done by a machine.

If you label enough images, there's only so many different variables.

If you look at the way.

You see the slack.

If there's lots of light, it means you have holes.

That's the thing a machine can answer.

But if you go to the more higher level questions, for example, you can actually compare an electrolysis to a human.

if it gets too warm, temperature above 1000 degrees we actually say in the, in the industry that both has a fever, it's sick.

The question is, why is it sick?

Why is it, why is the temperature high?

Why does it have a fever?

And that question is extremely difficult to answer, even for an experienced engineer with over a decade of experience.

And that's something that I do not see being automated anytime soon, by an algorithm.

Luke van Enkhuizen: right.

Then we are at a point where we just are managing by exception.

And I think that's already a massive progress And if you get sick there's probably a very complicated process.

Like if humans get sick, we don't always really know what the accurate diagnosis is.

We have a lot of ideas.

We can look for viruses, but we have to probably do something to really check in the body.

Look at the blood, look at the saliva to know what's really going on.

You can not just like talk to a computer and a computer tell you with accuracy.

What you have, right?

So I think that that is a great example where you are looking at the exceptions.

But I feel even with that part getting sick, with the diagnosis of that part getting sick, you could probably also use data too.

Have a extra pair of eyes with you, because what would this, the specialist do to research this, which steps is he taking?

What does he see?

What does he hear?

What does he note?

What does he see in the historical data?

if you look all the, bring all those things together, that that's actually the domain knowledge that he has, and that can be recorded some way.

And it would be used as a blueprint for another time, something gets sick.

And if you record this often enough, you start to see a pattern and then you could probably predict it even before it happens.

Denis Gontcharov: Yeah, I think no one can really tell.

It's a very exciting period in history where we'll see just how smart the systems can become.

My gut feeling I wonder what your opinion on this is that I think we may see our dear Pareto appear as well in these discussions where, for instance, 80 percent of the decisions currently made by an experienced engineer could be done just as well by an AI, and we will have the 20 percent of the most difficult decisions will remain in the hands of the engineer.

Luke van Enkhuizen: Yeah.

I think this is something we will see across all industries that a lot of workload will be offloaded to AI systems where the humans are monitoring.

It's kind of like an air traffic controller.

The planes can perfectly take off and land by themselves.

You know you can perfectly schedule which plane lands where automated, but there is still traffic control necessary and quite a few of them because what if this computer is wrong and what if something unplanned happens or what if and last minute change is required.

And so I don't think we will get to the point that there will be a man and a dog, that the dog will start barking if the man wants to touch the button.

I think we're far from there indeed in this current time and place.

But I do think we need to move forward very quickly to a world where a lot of this decision making can be assisted or at least suggested by AI systems very quickly where it will predict like, hey, you know, check this out, probably something wrong here.

Before you actually already noticed it, like like when it was already too late, I think this is a great example for that.

Yeah, I think this is a great thing to wrap this episode almost up on this topic already.

Cause we talked about the importance of domain knowledge.

Of course, there's much more to be said about how this will affect for a company to want to do a project, but just in covering that really, that, that's really foundational, you know view on domain expertise.

I think it's quite important.

Denis Gontcharov: Yes, I fully agree.

And in a sense, if you want to transport your domain knowledge into an AI, the precondition you need to fulfill is that you actually need the clean data, which again, highlights the importance of investing into a proper digital infrastructure to capture all the data from your systems.

Luke van Enkhuizen: Yeah.

And then you'll quite quickly notice that to then do something with that data becomes more accessible to anybody in a factory, if you do this right.

And so I think we will uncover more about the unified namespace and the approach behind this.

For example, you want to make a dashboard in the next episodes because that will be more specific topic of like how to build a quick proof of concept or a solution.

And what do you need by that?

I think that's better for what next episode to talk about.

What do you think?

Denis Gontcharov: Yeah, I fully agree.

And I really look forward to that discussion.

Luke van Enkhuizen: Well, that was this week episode.

Thanks for listening.

Bye bye.

Denis Gontcharov: Bye bye.