Industry and Technology Committee on April 13th, 2026

[Jayson Myers]

Absolutely.

Again, those use cases that I provided are all Canadian, from Canadian AI providers. They're all working with Canadian manufacturers. They're all really good examples of how effective AI applications can be in an industrial setting and the results of that.

I'm very optimistic that we have everything it takes, as long as our technology, research and manufacturing sectors work together. That's not only the challenge we've seen but also the real opportunity.

[The Chair, Liberal, Ben Carr]

Thank you very much, Ms. Sudds.

Mr. Ste‑Marie, you have the floor for six minutes.

[Gabriel Ste-Marie, Bloc, Joliette—Manawan, QC]

Thank you, Mr. Chair.

I thank the three witnesses for being here. We've learned a lot from our discussions and their remarks.

Before I ask my questions, I also want to welcome the Liberal colleagues who are here today as substitutes. It's great to listen to these discussions and to work with them.

Mr. Chair, you're an excellent chair, but a member who's here today was chair of the Standing Committee on Finance in the previous Parliament, and he was an excellent chair too. I had the pleasure of working with him for several years. I welcome my dear colleague and friend.

Mr. Myers, thank you for your remarks. As we just heard, you announced another 20 new projects a few weeks ago. However, your website says that no applications for new projects are being accepted. Is that to be expected because it's cyclical, or is there some other explanation?

[Jayson Myers]

Thank you for the question.

The explanation is that we've fully committed all of our funding to date, and while we would love to be able to continue to fund new projects, we simply don't have that capability right now. We're looking for new private sector opportunities, as well as public sector opportunities, to raise funding to continue to support really good projects.

In our last call for proposals for AI projects in manufacturing, we were able to fund about 30 projects in total. The total intake was over 200, and the funding ask was about five times more than what we had to distribute or to allocate. I think we've allocated it very well, to very good projects, but we can always use additional funding to leverage up better results.

[Gabriel Ste-Marie, Bloc, Joliette—Manawan, QC]

It sounds like there is a lot of need when it comes to funding new projects. Are you asking the government to inject additional public funds?

In Quebec, Scale AI said it was waiting for renewed funding to be announced. Are you in the same boat?

[Jayson Myers]

We are, from the global innovation clusters program, that's true, but I think there are also other opportunities to leverage private sector funding here in industrial investment funds—ITBs, for example—to support really good implementations of AI where that could, for instance, help to support our industrial strategy for defence. Yes, we are—hopefully and fingers crossed—looking for funding renewal of the global innovation clusters program. It's been an exceptionally good program and has led to great results, but we're also looking at other funding opportunities.

[Gabriel Ste-Marie, Bloc, Joliette—Manawan, QC]

Thank you very much.

Mr. Veerman, you pointed out that the strategy is lagging and needs to be deployed faster. What do you want the government to do to help deploy AI for businesses?

[Alan Veerman]

For the federal government, I think the advice we've given around the deployment of AI in industry is that our business partners are looking for clarity on rules. We've not really encountered companies or stakeholders that are vehemently anti-regulation. They just want clear rules.

On the legislation that was previously tabled by the federal government, the former Bill C-27, one of the main criticisms of that legislation was that it outlined penalties for non-compliance with the legislation but failed to articulate what the exact rules are. For our business partners, one of the things they have articulated as being of importance to them for long-term planning, especially in capital-intensive industries like the manufacturers Mr. Myers works with, is that you will not be able to plan a three- to five-year capital outlay or long-term investment if you don't know what the rules are.

I think the other major piece of advice we've given to the government as they consider things in the legislative and regulatory space is that, to the extent that the government does consider legislation and regulations, whatever options the government considers, it also needs to consider the interoperability of those standards across jurisdictions.

It's an unfortunate truth that Canada in and of itself as a jurisdiction is not big enough to set a global standard. It's not to say that we shouldn't have standards, but they need to be compatible with international standards such as those used by the OECD, the European Union or the U.S.

Whatever the right answer is, we're not necessarily sure. We know and we would suggest that the incorrect answer is for Canada to develop standards that are not compatible with those of any other jurisdiction.

[Gabriel Ste-Marie, Bloc, Joliette—Manawan, QC]

Thank you, Mr. Chair.

[The Chair, Liberal, Ben Carr]

Thank you very much, Mr. Ste‑Marie.

Madam Borrelli, the floor is yours for five minutes.

[Kathy Borrelli, Conservative, Windsor—Tecumseh—Lakeshore, ON]

Thank you, Chair, and thank you to all the witnesses for being here today.

My question is for Mr. Myers. As AI is deployed into operations like factory floors, supply chains and smart infrastructure, it's increasingly influencing or automating decisions that directly affect business outcomes. What safeguards are being used today to ensure those systems remain transparent, reliable and accountable?

[Jayson Myers]

I think that the most important safeguards are the applications themselves where manufacturers, in particular industrial companies in their deployment of AI, can't afford for this to go wrong. When you're looking at deployments of AI and physical equipment, physical systems, there is a problem there. The model is not giving the results required, there had been insufficient testing here, or there is a misinterpretation of the types of instructions that need to be carried forward to deploy this in actual physical operations.

It's not just an issue of bias. It's an issue that this could kill someone. There are some very important health and safety risks that companies need to be able to safeguard. Of course, in industrial operations, you have to work within the tolerances of the physical processes that you're working in.

Part of our project selection process is to look at how manufacturers, their AI providers and their technology partners interpret the risks, how they identify the risks and how they intend to mitigate the risks. Those risks in a physical operation tend to be very different from the simple risks in a digital application of AI. I think the controls around that are already pretty stringent in terms of trying to avoid the negative outcomes, whether that's simply downtime that companies can't afford or a health and safety problem, as I mentioned.

One other thing that I think the committee might be interested in here, too, is looking at the standards around AI application, the standards that are being used not only in Canada but internationally around data interoperability and cybersecurity. Often, these are Canadian standards. They're Canadian industrial standards.

CSA is actively involved in looking at industrial AI applications and developing sets of standards around that, but the leading standard for data operability in international industry is actually built on Canadian software and Canadian standards, as is the leading application for cybersecurity in the automotive industry, for instance, which is built on QNX.

I think industry itself often leads in the application of these standards and the development of the standards, for the very simple reason that it has to.

[Kathy Borrelli, Conservative, Windsor—Tecumseh—Lakeshore, ON]

I'm not seeing any standardization across industries. Are we relying on individual firms to define their own accountability frameworks?

[Jayson Myers]

No. I think that, when we look at the accountability frameworks that companies are using, there's a lot of work that has been done by the Americans at NIST and by the OECD around accountability frameworks for industrial applications, as well as other AI applications. I think that is all part of the AI governance processes and procedures that companies need to look at.

They're certainly part of the risk assessment that we undertake when we're looking at projects. We require project partners in AI applications to show that they have good management and governance processes in place here.

[The Chair, Liberal, Ben Carr]

Ms. Borrelli, unfortunately that's all the time that we have.

[Kathy Borrelli, Conservative, Windsor—Tecumseh—Lakeshore, ON]

That's too bad. Thank you.

[The Chair, Liberal, Ben Carr]

Thanks very much.

Mr. Danko, the floor will be yours for five minutes.

[John-Paul Danko, Liberal, Hamilton West—Ancaster—Dundas, ON]

Thank you, Chair, and I really appreciate the discussion this afternoon.

Mr. Veerman brought up an interesting point in his opening comments. He said that AI adoption in Canada isn't necessarily an adoption problem and that it's mainly a trust problem. I wanted to dig into that a bit more from the perspective of a professional structural engineer.

When working in complex design, we've been using computer-aided design for years now, but the professional engineer is always responsible at the end of the day for that design. When you introduce AI into that process, it becomes a bit more complex, because you're dealing with a black box and you don't necessarily know how it's giving you the answers. When I'm using a calculator as part of my design, I am 100% certain that I'm going to get the right math from the calculator, but I don't know that necessarily from AI software.

My question is for Mr. Veerman.

When we're talking about the risks and regulation of AI in terms of adoption and trust, is there a necessity to have regulated accuracy in the products that are being provided to industry?

[Alan Veerman]

We'll look to our industry partners and their practices. The Vector Institute deals a lot with health sector institutions, which are regulated. We deal with financial services, which are regulated. Our industry partners know and well understand that they have to answer to their regulators in terms of their decision-making. That can come from a human, and that can come from an algorithm, but at the end of the day, the corporation is responsible for its actions.

One of the areas where the Vector Institute spends a large amount of its time and energy is on the ever-evolving speed at which AI innovation crosses over into different areas. For a regulator, part of the effort is in helping them to understand how fast AI is evolving and the fact that the concerns they might have been tracking six months ago are now replaced by an entirely new set of concerns they might not have thought of.

We can look to our regulated sectors, like financial services, telecom and health, and the way in which they answer to their regulators on the need for what we call explainable AI. It's the idea that they have to be able to satisfy to a regulatory standard the nature of their decision-making and be able to justify that they understand how those decisions were made. That can apply to good old-fashioned data science, good old-fashioned statistical methods that have been in place for years, whether it was a judgment from a human or whether it was done by an algorithm.

I think one of the things that are an evolving area of research for the Vector Institute is that there are some terms from...call it a public policy initiative, where there's no agreed-upon definition of the goal of that standard. One of the things we're often familiar with is a refrain that algorithmic decision-making should be fair. There's no universally accepted computer science definition of what fair means. It's actually an evolving area of AI research. To the extent that a regulated sector has to demonstrate that to a regulator, they spend a large amount of their time and energy, for example, working with us on understanding the implications of that standard and how it will carry through into their existing risk management frameworks.

Mr. Myers also alluded to this. These are processes that industries have used for some time, and they are evolving in complexity. Both the industry partners that we have and the regulators that they report to have worked with us on those evolving needs.

[John-Paul Danko, Liberal, Hamilton West—Ancaster—Dundas, ON]

Thank you.

Mr. Myers, I want to ask you about intellectual property. This is something that you've both spoken about, but I think it's particularly interesting in the context of advanced manufacturing processes, where you're using an AI tool to develop proprietary processes that are a competitive advantage in industry. There's a bit of a disconnect, perhaps, in the AI world when you're feeding those into the AI system. They then become part of the training model and then perhaps are risking rather than improving somebody else's processes or developing somebody else's products or whatever.

How can we make sure that Canadian intellectual property is protected when we're using these AI products as part of advanced manufacturing?

[Jayson Myers]

The type of IP that is being developed here is certainly not patentable or copyrighted when we're dealing with applications, at least in industrial AI. Our experience is that you really need to have a very good IP management framework that involves all parties in terms of industrial AI—the manufacturer, the AI provider and the other technology companies that are involved.

If we're looking at the development of a new application involving AI, it is going to require training on models that have been developed by the AI provider. Our experience is that those models themselves remain the property of the AI provider. Here in the application, the outcomes and the IP developed as a result of the data used in those models usually remain the property of the manufacturer—the owner of the data. That really needs to be worked out.

There's also a different—

[The Chair, Liberal, Ben Carr]

Mr. Myers, we're significantly over time, so I'm going to have to cut you off, unfortunately. There might be an opportunity for somebody else to pick up on that. Thanks for the response.

Thank you, Mr. Danko.

Mr. Ste‑Marie, you have the floor for two and a half minutes.

[Gabriel Ste-Marie, Bloc, Joliette—Manawan, QC]

Thank you, Mr. Chair.

Mr. Veerman and Mr. Myers, if I could simplify a bit, you explained that adopting AI technology to boost productivity can be a fairly complex process for businesses. I would like you to illustrate that with an example you know of that was a success, without revealing any industrial secrets, of course. It can be a bit more general, but illustrate for us what makes it complex. What was the process and what was the improvement?

I don't know which of you would like to start. I see Mr. Myers nodding, so he can start.

[Jayson Myers]

There is a large automotive parts company in Canada that is using industrial AI to improve the efficiency of its equipment—for example, to reduce the downtime of its equipment. They're doing that not only through predictive maintenance but also through prescriptive maintenance, where they can look ahead. It's not to predict the failure of equipment but rather to make sure it doesn't happen.

This requires sensors embedded in the equipment, and real-time data so that, in some AI applications, where you're controlling the equipment and reading the equipment in real time and avoiding any latency problems. You're dealing with the need for some form of segregated network in order to protect and ensure the integrity of the data and the fast transmission of that data. If the equipment—in this case including the use of robotics—is run through AI and embedded in those robotics, that all has to be integrated as well.

That's the type of technology stack they're working with. The AI model helps them reduce the downtime of the equipment pretty significantly.

[Gabriel Ste-Marie, Bloc, Joliette—Manawan, QC]

Thank you very much. That was very enlightening.

I would have like Mr. Veerman to give us an example, but my time is up, unfortunately.

Again, thank you very much to all three of you.