Julie Bilodeau:
Welcome to BDO Digital Digest, your go-to for the latest in innovation technology from BDO Canada. I'm Julie Julie Bilodeau, and I'm here as usual with Hamed Faghfoury.
Hamed Faghfoury:
Hi, everyone.
Julie Bilodeau:
Yes, as curious leaders, we're here to explore how evolving technology propels businesses forward and reshapes our work. Today you'll also see Paul Dostaler is here. He's a partner in our BDO Ottawa office, a national industry lead for the manufacturing and distribution industry. He's also an IT professional leading development teams in the areas of connected services and the internet of things, application transformation, and solution architecture. He brings 20 years plus experience to the table with us today in the world of manufacturing. So our topic of today's episode is where we're going to explore how manufacturers are adapting to new economic pressures while harnessing technology and AI. We'll look at how rates and tariffs are shaping innovation, how AI has evolved from predictive maintenance to enterprise-wide value, and what recent survey insights reveal about readiness for adoption. So we're going to discuss how manufacturers are realizing the ROI beyond the shop floor using modern data platforms to drive insights across operations, finance, supply chain, and sales. And boy, do we ever have a lot of ground to cover. Welcome, Paul, and thanks, Hamed, for being here as usual.
Paul Dostaler:
All right. Thank you very much for having me here.
Julie Bilodeau:
Sure. Looking forward to this. So, Paul, let's start off. That was quite the intro. So let's start off with how have you seen recent economic factors like these rates and tariffs that we keep hearing about in the media? We also hear about technology and the lack of adoption, really, that we've actually covered that in a Productivity Paradox Episode Two, in terms of adopting new technology, especially in the world of AI. How is this in manufacturing? How's this all coming together? What does the current landscape look like compared to, let's say, the US?
Paul Dostaler:
Sure. Great question. And you're absolutely right. The tariffs are just dominating our lives and have been for a number of months now. But maybe we'll start by setting the context. Looking back even 10 years, think of a time before COVID when we already knew that we were a little bit lagging behind the US in terms of our adoption of technology. Most reports would say that we're about 10 years behind in our adoption of technology. Then COVID happens, right? And so the world gets flipped upside down. And for manufacturing, where you rely on hands-on skilled labor, you have things like lockdowns that are happening, you have a resurgence of remote work in those industries that can offer that leading to something called the Great Resignation where people were reevaluating what they wanted to do with their lives causing this kind of drain in that skilled labor that's so important for manufacturing.
So, a big hit there, but it doesn't stop there because of COVID. Then we experienced this period of extremely high interest rates, and manufacturing being a low margin business to start off with, it became really difficult to find the capital in order to make those kinds of investments into technology and innovation. So just as we're climbing out of this and interest rates are coming down, there's an election in the US, and now we have this tariff war causing manufacturers to make some pretty significant adjustments in how they operate, how they place their products, and move their products around. And from what I'm hearing from the market, many manufacturers have figured it out. They're at a place where they're stable, but all of this years of risk and uncertainty has caused most organizations to go into cost containment modes to wait and see when this all kind of...
Julie Bilodeau:
So they're being very cautious?
Paul Dostaler:
Exactly. And so-
Julie Bilodeau:
Conservative. Yeah.
Paul Dostaler:
If I were to answer your question, that has absolutely slowed the pace of innovation, especially in Canada, and we find ourselves set back a little bit in comparison to the US. The other little point that I would say is that we're going to talk a little bit about technology and AI. The US is also investing a ton of money into AI infrastructure and programs, whereas Canada is investing as well, but not at the same rate, not at the same pace. So what we're seeing now that those kinds of investments they're bridged a little bit by the fact that we can use cloud technologies that are available throughout the globe, and so multiple factors playing into this, but yes, it's been a journey.
Julie Bilodeau:
Sure.
Hamed Faghfoury:
Paul, I've worked with you for a long time, and we've talked about AI use cases over the years in manufacturing, and it tended to be focused on the shop floor. They've tended to be around the predictive maintenance that Julie mentioned and you've alluded to. Are you seeing a change in that? I know you've chatted about it in previous conversations. I've heard you talk about there's a broader departmental benefit that's now coming into manufacturing. Can you get into that and tell us sort of what you see there and what are the challenges, and what are the opportunities manufacturers are seeing in that space?
Paul Dostaler:
Yeah. Yeah, for sure. So you're right, the classic predictive maintenance is the classic example of how to use diagnostics or perhaps machine learning in order to predict when machines fail. In manufacturing, it's so important to be able to understand what your uptime is. Uptime is productivity, leads to forecasting margin, and so on and so forth. And just on that point, even that use case, which would've been potentially difficult to achieve in the past, where we weren't really orchestrated for it everywhere. We didn't have all the necessary sensors and connectivity. Industry 4.0 came in and essentially provided that kind of connectivity to enable these use cases. Cloud came in to lower the barrier to entry to doing things like machine learning. So these things are almost table stakes, I would say at these days. But what's really interesting is that AI is more than just machine learning now.
So with the advent of generative AI and more recently agentic AI, we're seeing that AI isn't just about the shop floor. Like, yes, there are an infinite amount of use cases there, but we're seeing AI used also in the back office, if you will, in the IT side of the house. We're seeing things like the customer experience being improved through automated chat bots example. We're seeing the generation of quotes, for example, from customer emails without a human in the loop until it's time to actually present these quotes out into the public. We're seeing sentiment analysis and product testing before it's even manufactured and built. We're predicting product sales, allowing us to do better investments with this richness of insights that we can get from these predictive technologies.
Julie Bilodeau:
Wow. I'm thinking Paul to the Momentum Survey. I call it the Momentum Survey. It's this manufacturing leadership report that BDO posted in our insights area of our website. So for those of you listening, feel free to check out that area of BDO Canada's website. There's a link also that will be available here for those of you online and watching our YouTube video of this podcast. But this manufacturing leadership report, Paul would've given so much insight. And just so people know a bit more about it's actually the results of our national manufacturing and distribution leadership survey, where we surveyed senior executives across Canada to understand their business priorities, digital transformation efforts, their challenges, their opportunities that are driving growth. And really, it's about revealing where these industry leaders are directing their efforts in 2025. And so curious, Paul, as I'm thinking about this, and it's so timely that we have this episode, what did this recent Momentum Survey reveal? If you could give us a bit more visibility into this, just high-level about manufacturers' readiness and plans for AI adoption, and what does it mean for the industries like even our future competitiveness?
Paul Dostaler:
Yeah, great. And knowing that I was on the digital judge yesterday, I collected a few of these highlights that are really...
Julie Bilodeau:
So good. Well done. Awesome. Thank you.
Paul Dostaler:
And the purpose of this report is a few things, right? Many of us think we work in this industry, and we make all these assumptions about what is, what isn't priorities and whatnot. But I think it's really important then to go back to market and validate whether those assumptions are correct or not. And so one of the first things that we asked our respondents was, do you actually agree that we have a productivity problem in Canada? Because so much of what we do is on the assumption that we have a productivity challenge. And I understand that you spoke about the productivity paradox in the past episodes. So the result is that 75% of respondents acknowledge that we have a productivity...
Julie Bilodeau:
Wow.
Paul Dostaler:
... in Canada. And these are executives of manufacturing organizations across multiple sub-sectors from coast to coast in Canada. So then we got curious about whether digital technologies might play a part in improving those productivity gaps. So we asked first of all, why embark on a digital transformation, for example? And surprisingly, the top priority it wasn't productivity, it was in fact cyber security, and so-
Julie Bilodeau:
Really?
Paul Dostaler:
Yeah, with the increase of cybersecurity attacks in our landscape, the more that we use technology, the more that we create this kind of threat landscape in our organization, and more opportunity bad actors can have to cause problems. And so 30% of respondents quoted cybersecurity as their top priority. So that's good. That's very interesting. But then we said, "Okay, but back to productivity, are you looking at technology and AI specifically in order to improve productivity?" And 48% of respondents actually already are using some type of AI tooling within their business. So that's pretty cool. And specifically with the expectation of boosting growth and increasing margins. So we're on the right track, but we know that you don't just adopt AI overnight, right? AI is one very important pillar of usually a longer kind of strategy around digital transformation. So we asked the respondents who's actually executing on a digital transformation, and 33% of respondents were implementing some kind of digital transformation in the organization, and 33%. It might be an impressive number, but for me, it was a flag.
I would've thought it would've been much higher just because of the kind of clients that we serve. We know that it's everywhere. So when we opened up the cover and really looked at the statistics here at the data, there's actually only 11% of respondents that had no intention of going through digital transformation. And so 33 we're in a digital transformation, but the rest the gap between that and the 11% are organizations that are either in the process of planning their strategy or have the intention of creating a strategy and then executing on it. And so it's all for good reason too, because of those that are implementing or have implemented a digital strategy, 66% of them claimed that the ROI from that transformation, they rated it at seven or higher. So there's definitely evidence to say that that ROI exists and that digital transformation is definitely a lever that you can pull to improve productivity.
Hamed Faghfoury:
So, Paul, it's a good segue to the last portion of this podcast. We have a co-host who you do not see or hear yet, so I will speak on her behalf. She's around us, but she is not here with us. Her name is Jenny, and Jenny has a question for every one of our guests, as you likely know, and she has one for you, and it's related to ROI actually. And her question is this: in what ways are manufacturers realizing ROI from AI, and how do modern data platforms enable insights across their organizations? So [inaudible 00:13:52]-
Paul Dostaler:
Sure. You know what I'll say, two things. One is that we had a great client experience a number of years ago, that the program was actually tagged by the client. It was named, and it was named Make Performance Visible. And I thought, what a great name, because the journey towards AI is fruitful in itself. So just being able to expose these insights to collect your data together and get insights across your entire organization, there's a lot of ROI that you can get from that before you even tap into AI. So making performance visible across the entire organization is great. Now, I mentioned earlier a number of use cases around generating quotes automatically and all kinds of use cases, but when we really talk about going across the org, one thing that is actually gaining some momentum right now is we're seeing more and more organizations start to build out digital twin environments.
So digital twin is not a new concept. In fact, in the media, it seemed to really peak even a few years ago, but organizations were a little bit slower to really build out that technology. But again, now with the barrier of entry for machine learning and AI in general kind of lowering, we're seeing digital twin technology really take over in a way that you can create a simulated environment to test out scenarios. So what does it mean to test out? What if I build another plant in this geography? How might that impact my margins? How might that impact the amount of tariffs I have to pay for moving product around?
Hamed Faghfoury:
And this is real-time type activity?
Paul Dostaler:
It can be real time, and it can be just kind of like a one what if scenario generator. What if I choose to reconfigure my production line? What would be the impact of that? So with that digital twin, with the simulation, you're able to better understand the risk associated to change and also the potential...
Julie Bilodeau:
And that's because Paul, you're actually seeing it play out, right? It's literally a reality twin, correct?
Paul Dostaler:
Exactly. It's based on your past data, and it's predicting what would happen with all kinds of potential side effects, right? It's not just looking at one stream of data and input and output. It's literally looking at what might be the trickle effect in your entire environment, in your entire organization. And so one of the areas that's pretty hot in this space, too, is what happens when I make changes to my supply chain, right? Supply chains were so disrupted through COVID, through tariffs, you name it. And so it can be pretty fickle in terms of one small change can have a huge impact if you don't have the materials that you need to build the products that you want, or if you can't achieve the level of quality that you need to sell into market at whatever rate you need to make those margins and so being able to test before making those large commitments is really crucial and digital twin is one of the ways that you can better understand the impact.
Julie Bilodeau:
That's very cool. I'm excited. I actually need to end us there because we're at time, but what a great place to end. It sounds so inspiring to have this alternate reality in a sense that we can play with, and like you're saying, test scenarios out and see how it all plays out before we actually go and pull the trigger.
Paul Dostaler:
It really is.
Julie Bilodeau:
Thank you, Paul, so much for being here. Thank you, Hamed, of course my co-host to be here as usual. Thank you for all of those listening and for tuning in to BDO Digital Digest. Join us next time as we continue to explore the future of technology and how it impacts us day to day. Until then, stay curious, stay innovative, and bend the arc of possibility in your world. Thanks again so much, everyone.