PART 5 : Embracing Digital Twins and AI Integration for Business Success

In this insightful episode, we explore how digital twins and artificial intelligence (AI) are transforming the way organisations operate—and what the next 10 to 15 years might hold.

From the smart city innovations in Barcelona to the rise of large language models like ChatGPT, we break down the evolution of digital twin technology and its increasing synergy with AI. Discover how businesses can harness their data more effectively, make smarter predictions, and build adaptable, future-proof systems—all without needing a degree in data science.

We also address one of the biggest challenges facing IT and business teams today: finding time to innovate. Hear how implementing a digital twin doesn’t have to be complex or disruptive, and why acting sooner rather than later can give you a powerful competitive edge.

Key topics:

• Real-world examples of digital twin deployment
• The role of AI and machine learning in data processing
• How to integrate APIs and start simple
• The importance of usability in implementation
• Why your organisation’s data is its most underutilised asset

If you’re thinking about how to futureproof your business or just want to better understand where technology is headed, this episode is for you.

Ready to explore further? Drop us a message to connect with our advisory team or request a demo of our platform.

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Connect with our team Alan Crowley, Kevin Christopher, Brenda Shanahan, Eduardo Espinal, Stephen Breen, Liam Nolan, Callum Hennessy Cian Kavanagh, Niamh Quinn

Transcript

I want to talk about technology and where do you see it kind of going in the next 10, 15 years? That's a great question. Uh, as far as I'm aware, it re- it kinda h- made headlines or showed up in the, uh, tech space about 10 year- hang on, what year are we in at all? Yeah, so 2015, there was a lot of digital twin initiatives and some of the examples I didn't actually mention around, um, smart cities.

So Barcelona, a very complex smart city scenario, so they have a digital twin of the city where they can map out routes, congestion, um, it informs their, uh, congestion-free zones as well and, um, how they pedestrianize some of the city. Um, but they, again, and even just something as simple as changing, uh, traffic lights to help with slow traffic can all manage to put in there, uh, smart city solution which, again, has a digital twin.

A large digital twin, uh, component of it. Um, for, for me in the next couple of years, uh, like, we have, and I think we're going to talk about it again, um, around AI and where AI is coming up day-to-day, so like, not to call it an- an arms race in the AI space but it, there's a lot of popularity around that and, and what AI means and what it used to mean. So for, for digital twin it was a big data problem.

Yeah. And it was, it was a data problem and so we've had machine learning for, for a long time before AI kind of popped up and ChatGPT, before they showed up it was machine learning, um, now we have large language models, um, which is kind of where ChatGPT is built on and I think for what that's adding, we now have processes that can be independent and do discrete levels of work. So now if you're building a digital twin and you're looking at processing your data from vehicle telematics, you might create an LLM that specialize in processing, um, the telematics from vehicles and then that ends up, that output ends up feeding into your digital twin solution and you now have your data sanitization piece, um, added on top of what's there.

Um, the I would tag well, we're going to get a lot around around the, the... Be, be very, very predictable, uh, you could say that AI does pattern matching and it's the same with machine learning, it's like, it's a lot of pattern matching. So, part of the pattern matching is this what happened before, what is going to happen, uh, coming and because of the amount of research and, and hype around the LLMs and AI, that is where I would see it coming into the digital twin piece, is just more sophisticated predictions, um, around again, essentially your data patterns.

So, would you say it's worth your while then because it's going to continue evolving, to get one in as early as you can and start deploying a digital twin in some way, shape or form so that you can just evolve constantly with it? Yeah. I- i- it is a, not a labor of love, that's the wrong term, but it is down to that measure twice piece. It's the sooner you have it, the sooner you can add to it, the sooner you can build upon what you have and the sooner you can start making informed decisions.

Um, I think, do you know, uh, uh, one of the company's greatest assets is its data so if y- it's almost like if you had, um, office space and you're only in one corner of it, you're not utilizing the asset that you have. So, if you're not utilizing your data as a business, um, you're, you're going to not have that competitive advantage that other businesses have. Part of that is a digital twin, um, there's, there's lots of ways to utilize your data, digital twin absolutely, there's data analytics, um, which also can feed into your digital twin.

So it is more along the lines of the, the sooner you are, uh, well, the longer you're under-utilizing your data, the, the more you're losing out in the future. That makes sense. Matt, would you say it's, your wrong person to say this to now 'cause you're an IT person, but what would you say to somebody who speaks to their IT department and they go, "Look, we haven't time to be doing this. Like, that's not on our radar at the moment"?

Is this something that if you could just get APIs of all of the data you can hand over to people like us to just do it for them? And is it easy then to use the system afterwards? I mean, will I need a degree in IT to be able to operate this? I mean, that's how, it depends how good our implementation, our, like, what I would say is, it is the, it is that question back to the stable. Like, it is a common IT versus business scenario of we don't have time for this.

I was like, "Well, this is about taking time now to save money in the future." And that's quite a tough proposition for some businesses to make where they feel like they have to be doing something immediately on something and for a lot of this piece in terms of how, do you need a data science degree to understand this, like, no, that would be a failure of your implementation. The idea of a digital twin is that the most important information is in front of you, presented in a way that's actionable.

Um, if we start presenting stuff that's not actionable, if we start presenting stuff that doesn't make sense, um, to the user and they need to, again, ask ChatGPT what it says, then we've kind of lost in that scenario and I would say it doesn't bode well for their, uh, for that person's implementation of a digital twin. From our p- perspective, usability is incredibly important in what we deliver and then the high quality-ness and all that, that goes without saying.

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