Getting Your Tech Stack Ready for Industrial AI

Sure.

Thanks, Robert.

And great to be with you, Nick.

I yeah, you know, our report.

I think really takes a pretty high level overview at the manufacturing industry in general and how AI is going to impact it and sort of what is going to be required for those impacts to take place.

I think about it as being, you know, on the same order of magnitude as like electrification of factories or, you know, even before that sort of moving manufacturing to one centralized location, you know, whether that be driven by hydropower or steam power.

But basically these technologies that are so revolutionary that it changes everything about working, including like the architecture, how factories are even designed and laid out.

I think AI is sort of at that level and it's going to be that transformational.

One of the implications of that is that it takes a lot of time.

There's this sort of famous quote that was talking about the PC revolution that you can see the computers everywhere except in the productivity numbers.

And I feel like industry is having a sort of a similar thought about AI.

Everybody's using it, but we haven't really been accurately able to measure the impact of it.

But fundamentally, I do think it's going to be that level of change And so in our report, we really explore it from that panage point, really thinking, well, what would be required for factories to take full advantage of it? Just like what would be required of the factories of the past when they embrace these other big technological revolutions.

So one, I would say, metaphor that we use to describe that is the idea of a stack, right? Where if AI is kind of at the top of the stack, what are all the elements that need to be underneath of it in order for AI to have all the data and context that it needs to be able to make decisions or provide insights to factories? And so if you kind of look at what that requires to be true, starting at the bottom level, you need your factory floor to be connected, right? That equipment needs to connect.

There needs to be some kind of network in place.

And in between that, there needs to be a lot of what we think about of the traditional manufacturing technology stack, including things like an MES or ERP, things like that.

And so it was really interesting to us talking about Robert and I and some of our other researchers at AMT to see, you know, just interesting timing, the Cisco report, manufacturing in the age of AI, the network driving the factory floor and beyond, because this...

You know, we sort of maybe coming from a different directions came to a lot of the same conclusions at the importance of the infrastructure layer in the stack and the fact that if that is missing or deficient, then we're not going to get the AI revolution that we are all excited about.

Nick, can you maybe talk about that same idea? How is it that you guys decided to investigate this specific topic? What's the philosophy underlying it? And then tell us a little bit about the study you did and the impact or the outcomes of your research.

Sure.

Thanks, Robert.

And great to be with you, Nick.

I yeah, you know, our report.

I think really takes a pretty high level overview at the manufacturing industry in general and how AI is going to impact it and sort of what is going to be required for those impacts to take place.

I think about it as being, you know, on the same order of magnitude as like electrification of factories or, you know, even before that sort of moving manufacturing to one centralized location, you know, whether that be driven by hydropower or steam power.

But basically these technologies that are so revolutionary that it changes everything about working, including like the architecture, how factories are even designed and laid out.

I think AI is sort of at that level and it's going to be that transformational.

One of the implications of that is that it takes a lot of time.

There's this sort of famous quote that was talking about the PC revolution that you can see the computers everywhere except in the productivity numbers.

And I feel like industry is having a sort of a similar thought about AI.

Everybody's using it, but we haven't really been accurately able to measure the impact of it.

But fundamentally, I do think it's going to be that level of change And so in our report, we really explore it from that panage point, really thinking, well, what would be required for factories to take full advantage of it? Just like what would be required of the factories of the past when they embrace these other big technological revolutions.

So one, I would say, metaphor that we use to describe that is the idea of a stack, right? Where if AI is kind of at the top of the stack, what are all the elements that need to be underneath of it in order for AI to have all the data and context that it needs to be able to make decisions or provide insights to factories? And so if you kind of look at what that requires to be true, starting at the bottom level, you need your factory floor to be connected, right? That equipment needs to connect.

There needs to be some kind of network in place.

And in between that, there needs to be a lot of what we think about of the traditional manufacturing technology stack, including things like an MES or ERP, things like that.

And so it was really interesting to us talking about Robert and I and some of our other researchers at AMT to see, you know, just interesting timing, the Cisco report, manufacturing in the age of AI, the network driving the factory floor and beyond, because this...

You know, we sort of maybe coming from a different directions came to a lot of the same conclusions at the importance of the infrastructure layer in the stack and the fact that if that is missing or deficient, then we're not going to get the AI revolution that we are all excited about.

Nick, can you maybe talk about that same idea? How is it that you guys decided to investigate this specific topic? What's the philosophy underlying it? And then tell us a little bit about the study you did and the impact or the outcomes of your research.