Industry and Technology Committee on April 13th, 2026

[The Chair, Liberal, Ben Carr]

Good afternoon, everyone.

I hope you all enjoyed your constituency time.

It's nice to see everybody.

We have a couple of new faces around the table today on the Liberal side. Welcome to the industry committee.

Colleagues, we are continuing our fascinating study on artificial intelligence. As I've remarked on a number of occasions, we've had an incredibly insightful set of testimony to date. I'm looking forward to continuing that with the witnesses we have here with us today.

Just for verification, colleagues, all witnesses have had their audio connectivity and formalities tested by our House of Commons officials here. As we don't have any witnesses in the room, I would remind you that when your earpiece is not in use, please ensure that it is placed on the sticker in front of you.

We have two hours' worth of testimony this afternoon. All three witnesses are joining us online at the moment. We do have a couple of witnesses who were slated to be here in person. They haven't yet arrived. If they do arrive, we'll probably have to forgo their introductory remarks, but we will provide them with an opportunity to receive questions from members. That's unless they arrive while we're still within the introductory phase of the meeting.

From Next Generation Manufacturing Canada, we have Jayson Myers, chief executive officer.

We had a nice conversation a couple of weeks ago. It's nice to see you again, sir. Thanks for being here.

From Vector Institute, we have Alan Veerman, chief operations and finance officer, alongside Jessica Blackman, director and chief data officer.

I will give Mr. Veerman the floor and then go to Mr. Myers. After the introductory remarks are made, members will have time to ask some questions.

Mr. Veerman, the floor is yours for upwards of five minutes, sir.

[Alan Veerman, Chief Operations and Finance Officer, Vector Institute]

Thank you, Chair.

Good afternoon, honourable members. Thank you for this opportunity to contribute to the committee's study on AI in Canada's strategic industries.

My name is Alan Veerman. I'm the chief operations and finance officer here at the Vector Institute. I'm responsible for the day-to-day operations of the institute. Appearing with me here today is Jessica Blackman, director and chief data officer.

The Vector Institute is one of Canada's three national AI institutes, originally established in 2017 with support from the Government of Canada, the Government of Ontario and leading private sector firms across a range of Canadian business. Vector is located in Toronto, with more than 960 affiliated researchers at universities across Canada. Supporting an AI talent pipeline, Vector-recognized AI master's programs produce over 1,000 graduates annually, over 90% of whom stay and work right here in the province of Ontario.

The easiest way for me to summarize Vector's mission is that it is taking cutting-edge AI research and then enabling organizations in Canada to adopt and deploy AI faster across a range of sectors.

Vector has more than 300 partners, from various start-ups to enterprises and broader public sector institutions. Vector's industry engagement is second globally only to MIT, as measured by the number of participating companies.

I think you've heard from others that Canada has successfully built a world-class AI research base. That's right. In turn, the three national AI institutes have each developed approaches that accelerate the safe, responsible and productive deployment and development of AI technologies across multiple private sector companies and public sector institutions.

That said, the nation's private and public AI adoption rates still lag behind those of international peers. In 2019, Canada was fourth in the global AI rankings, thanks to being the first country with an AI strategy. Since then, Canada's position has been slipping. As of 2024, Canada now ranks eighth. This decline is not uniform. Canada still ranks third globally in AI research. Where Canada is comparatively weaker is in both infrastructure, currently 16th, and operating environment, currently 18th. Vector's experiences reflect these rankings.

The message here is consistent. Canada is excellent at producing AI talent, but Canada lags in AI adoption and deployment, with trust in AI and speed the two biggest issues, in our view. To that end, I have three main reflections to share with the committee today.

First, I believe Canada's AI adoption problem is fundamentally a trust problem, and trust comes from understanding. Recent research shows that 69% of regular AI users trust the technology, compared to just 5% of non-users.

Second, Canada's sovereign AI compute strategy was absolutely critical, but it's effectively two years behind schedule. The lack of financial commitment is not an issue here. The speed at which it is happening in real life is.

In contrast, the U.K., a middle power like Canada, announced a commitment to AI compute, but their procurement at the time reportedly took about three weeks. Though the specific initiative has admittedly changed multiple times since then, speed is the theme.

Without faster deployment of AI compute, AI researchers will leave for jurisdictions with better infrastructure that is committed and online. The corresponding start-ups that form will scale in those jurisdictions rather than here. The same goes for the corresponding economic benefit.

Finally, increasing productivity through AI usage and deployment is genuinely difficult. It requires fundamental rethinking of business processes, cleaning of data and data governance, upskilling the company's workforce and a sustained and systemic organizational commitment. Put more simply, if a business is not rewiring its business processes around AI, it is missing out on the transformative nature of the technology.

For Canadian culture, such risk aversion manifests itself in procurement, among other things. Small Canadian AI firms looking for Canadian clients often hear that they require a U.S. reference customer first before being considered. This is backwards. More Canadian start-ups should find their first clients here, rather than abroad.

In summary, Canada has world-leading AI talent. We have a thriving AI start-up ecosystem, and Canada's banks and financial sector in particular are recognized as global leaders in AI adoption. This is a great foundation, but we're concerned that without faster deployment of AI compute infrastructure, we risk watching this AI competitive edge diminish further, and Canada's global ranking will continue to slide.

Thank you for your time. We welcome your questions.

[The Chair, Liberal, Ben Carr]

Thank you very much, Mr. Veerman. That was a very concise and useful introduction. Thank you for that.

Mr. Myers, I'll turn the floor over to you. You have up to five minutes for your introductory remarks.

[Jayson Myers, Chief Executive Officer, Next Generation Manufacturing Canada]

Mr. Chair and members of the committee, thank you for giving me the opportunity to say a few words about industrial AI.

Next Generation Manufacturing Canada, or NGen, is the industry-led not-for-profit organization that spearheads Canada's global innovation cluster for advanced manufacturing. As such, we're deeply involved in projects that integrate AI and manufacturing processes and equipment. We're dedicated to building world-leading advanced manufacturing capabilities in Canada for the benefit of Canadians. We do that by bridging the gap between research and technology on one hand and the needs of manufacturers on the other—the adoption by the customer. We do that by providing non-dilutive funding, project management and IP commercialization support for collaborative ventures among researchers, technology providers and manufacturers.

The projects we fund integrate technologies to develop, scale and accelerate the adoption of new manufacturing processes in Canada while keeping the benefits of the IP in Canada. That is exactly what's needed for the adoption of industrial AI. Our funding comes from both public and private sources. Since our inception in 2017, it has come primarily from Innovation, Science and Economic Development Canada.

The focus we have on transformation, collaboration and commercialization has really paid off. To date we've invested in 281 projects, with close to $1.2 billion in overall investment. Of 652 industry partners, 90% are SMEs. Every dollar of our funding has been matched by more than $1.70 from industry. So far, our projects have leveraged $4.2 billion in follow-on investments. They've generated over $8.2 billion in revenue. They've created 57 new companies, 4,500 new jobs, over 1,600 new IP assets and an estimated $1.2 billion in tax revenue that flows back to the federal government. That's approximately $5.70 for every dollar we've invested in completed projects.

We've invested in 153 projects that implement AI in manufacturing processes. These aren't projects that develop new large language models, but they're examples of industrial AI, where partnerships are important in building the technology stacks required for AI adoption. Industrial AI differs from purely digital LLMs, because it involves the integration of AI models with physical systems like sensors, vision systems, robotics and equipment, or the use of equipment like smart robotics and vision and automation systems in which AI is already embedded. It requires secure and segregated networks and edge computing solutions for real-time communications and control, as well as integration with operating software and systems architectures. Its skills requirements are different, requiring a deep understanding of industrial processes as well as IT software and data analytics expertise. So too are the tolerances in which it must work to ensure reliable, safe and compliant operations.

I have provided the committee with a table that contrasts industrial and digital AI. This is in annex A of the document that was provided to the committee.

The adoption of industrial AI is complex, although it's not really all that expensive when compared with capital expenditures in manufacturing. In fact, when consulting companies report that the majority of industrial AI implementations fail, it's not because of the technology. It's because adopters don't have the data quality, digital and technology infrastructures, skills or often the business plan and management systems required for the successful adoption and productive use of AI.

Success depends on partnerships among AI providers and manufacturers. It's not simply a transactional vendor relationship. It depends on integration with production technologies and operating systems. It depends on AI readiness. Do companies have a plan about how this will lead to improvements in critical operating processes? Do they have the management and operational skills, data systems, technology infrastructure, cybersecurity and AI risk mitigation practices required to deploy AI in their operations? Money alone isn't going to guarantee success.

The criteria we use in assessing the readiness of manufacturers to adopt industrial AI applications are outlined in annex B of the document provided to the committee.

We know that Canadian manufacturers need to boost their productivity performance. We also know how important it is for manufacturers to do so in order to continue to drive the Canadian economy and provide the production capacity to supply our needs for homebuilding, infrastructure, health care, energy, environmental sustainability and defence. Industrial AI applications offer them the best opportunity to rapidly improve productivity and build that capacity.

Real use cases from Canadian industrial AI solution providers show that 30% improvements in throughput, quality control, equipment, operating efficiency, energy efficiency and delivery times can be rapidly achieved—if not significantly more. I've provided the committee some of the results of the 122 use cases that have been curated by Canada's AI4M, a manufacturing consortium that we support.

Industrial AI implementations like these do not replace jobs. In a sector beleaguered by labour and skill shortages and facing existential challenges, they enhance and protect jobs.

As the government refreshes Canada's AI strategy, it's going to be crucial to focus on how to accelerate the adoption of industrial AI. There's an important role for government to play in underwriting the risks, especially for SMEs, in a sector that's so vital to Canada's economy.

As we've shown, industry partnerships that integrate technologies, help manufacturers prepare for successful implementation and develop the workforce skills required to use AI-enabled tools and technologies effectively will be instrumental in achieving the step-change improvements in industrial and economic productivity upon which all Canadians will depend.

Thank you.

[The Chair, Liberal, Ben Carr]

Thank you very much, Mr. Myers.

Colleagues, we're going to enter into our first round of questions.

Madam Dancho, the floor is yours for six minutes.

[Raquel Dancho, Conservative, Kildonan—St. Paul, MB]

Thank you very much.

Thank you for the opening statements from the witnesses today. I appreciate your expertise in this important study.

Mr. Myers, it's great to see you again. I appreciate your briefing. I have a number of questions for you.

You mentioned this at the end of your remarks, but I wanted to talk to you about the potential for productivity improvement in Canada with industrial AI adoption. This committee recently did a study on productivity. We understand—even from the mouths of the Governor of the Bank of Canada and the executives there—that productivity is in crisis in this country, that the stagnation of productivity really has led to increasing affordability issues and the like.

I know you're aware of this. In brief, just to elaborate a bit on your opening statement, how do you feel that AI industrial adoption could support Canada's productivity?

[Jayson Myers]

In manufacturing, I think the biggest gains are going to come from improvements in industrial production processes. This is where AI really needs to be integrated with other physical systems: with the equipment and with all of the IT infrastructure, the technology stack that is required to support AI implementation.

Some of the use cases that have been provided by the AI4M AI manufacturing cluster are really pretty revelatory here: areas such as throughputs, quality control and quality assurance, and areas for reducing the downtime of equipment, for instance, and improving energy management, reducing emissions and reducing energy use. These are some of the applications where AI has really been shown to lead to very significant improvements, with, in some use cases, well over 30% improvement. I think that's the type of productivity gain we really need.

It's not just to improve productivity. Productivity is really about.... There are three ways to achieve greater productivity. You can do a lot more with more. That's the positive aspect of productivity. That's what AI can lead to in manufacturing.

The other two areas are not so good. In the first part of 2010, Canadian manufacturing productivity looked terrible against that of the United States. The reason was that manufacturing production dropped in both countries and the U.S. removed many more jobs than Canadian manufacturers did, which led to a significant improvement in U.S. productivity in manufacturing, but it wasn't a good news story. The economic output dropped, and jobs dropped a lot more.

I think that's what we need to avoid. We need to look at the positive impacts of how AI and other technologies can be implemented to improve and expand the capacity of manufacturing today, at a time when it's very difficult to find people working in the sector.

[Raquel Dancho, Conservative, Kildonan—St. Paul, MB]

Thank you.

With that productivity stagnation or the issues we're seeing, we've also seen our manufacturing sector decline quite sharply in the last three or four years. We're seeing declines in the employment, the output and the shipments. Your perspective is that further AI adoption would perhaps turn that around and we could have greater outputs.

I think the concern of a lot of people is that AI and robots are going to replace the jobs of those in manufacturing, but what I did find interesting was that—and you may be aware of this—recent StatsCan survey data showed that companies adopting robotics managed to, somewhat paradoxically, increase their workforce by roughly 20%.

Do you have any experience with that? Can you comment? Should we be concerned that AI may take all of our manufacturing jobs?

[Jayson Myers]

No, I would say just the opposite.

If manufacturers don't adopt AI and some of the more advanced manufacturing technologies that are out there, including robotics—and many of these new robotics systems are smart robotics enabled by AI—they just won't be competitive.

Today, when we're looking at the challenges facing manufacturing, particularly with respect to trade barriers and the opportunities, though—

[The Chair, Liberal, Ben Carr]

Mr. Myers, I'm going to ask you to pause for a moment.

I'm going to stop the clock, Madam Dancho. Apparently, we're having a little translation issue.

Mr. Fonseca and Mr. Eyolfson, what's the issue?

[Peter Fonseca, Liberal, Mississauga East—Cooksville, ON]

On our English channel, we're hearing French.

[The Chair, Liberal, Ben Carr]

I'm going to give the translators a moment to see if they can correct that on their end, and we'll come right back.

[A voice]

It should be working now.

[The Chair, Liberal, Ben Carr]

We're good. That's excellent.

The floor belongs to Madam Dancho, so she'll do what she wants with her time.

[Raquel Dancho, Conservative, Kildonan—St. Paul, MB]

Thank you.

I am encouraged by that, and I was encouraged by the StatsCan data.

One thing I am concerned about is what we're seeing with the dark factories in China. I'm sure you're familiar with them. They're almost 100% automated, and there are no people working and making a lot of the cars and other things. That seems to fly in the face a bit of the adoption that we've seen locally, with the StatsCan data showing that there's an increase in employment.

I have concerns, though, that what we're seeing in the factories in China—these dark factories with nobody working except for robots in the dark—could happen here if we really leaned into this.

Is that not a concern, based on some experience in another country? Should we not be worried about this? Are we overreacting?

[Jayson Myers]

Even here, there are some examples of factories that are working with lights-out production. Overall, for the business that's employing the people and for all of the people who are being employed to maintain technology, to program technology and to develop the new software for this technology, even in China, the overall rate of employment has increased.

Today, it's not just a question of competitiveness and productivity. We also have to look at how we expand production at a time when we really don't have many skilled people coming into the manufacturing sector, to take advantage of the new opportunities in procurement, infrastructure and defence, for example. We really do need to look at how AI can be implemented, not only to expand production and production capacity but to do so in a way that is going to lead to the development of new products, new product lines and new processes that are going to make sure Canadian manufacturers are in an internationally competitive position.

[Raquel Dancho, Conservative, Kildonan—St. Paul, MB]

Thank you very much.

[The Chair, Liberal, Ben Carr]

Thank you, Madam Dancho.

Ms. Sudds, welcome to the industry committee. The floor is yours for six minutes.

[Jenna Sudds, Liberal, Kanata, ON]

Thank you very much, Chair, and thank you to all of the witnesses for joining us today.

I'm going to start with Mr. Myers.

It is a pleasure to see you again.

I was at the N3 summit just a week or so ago, which was a terrific day. At the summit, if memory serves me correctly, you were sharing some great news for a bundle of, I believe, 20 projects that were receiving funding through your organization, obviously with support from the federal government. I know that, for my riding of Kanata, that included Inpho, an incredible photonics company working here in Kanata.

Can you speak to us about the role you play in supporting and growing companies in advanced manufacturing, linking to AI here and across the country?

[Jayson Myers]

Thanks for your question, and thank you for coming to N3. We had over 1,000 participants, and 114 of our projects were exhibiting what they've developed as a result of our support.

We raise funds from both the public and private sectors, primarily through ISED, to invest in collaborative projects that are really transformative in terms of the advanced manufacturing processes that come out of these projects.

The key here is to integrate technologies. Often, technology companies have a fantastic technology, but it's not everything that is required in order to implement it successfully in manufacturing. We take a look at how we can integrate technologies. How do we build the IP relationships around collaborative projects? Then, of course, how do we find the right manufacturing customers and the right type of solution for those manufacturers?

To date, we've invested in about 281 projects. I think that collaborative model is exceptionally important. There aren't very many other organizations that focus on the collaborative aspects of open innovation and technology, particularly in the manufacturing sector. We not only provide the non-dilutive funding—they're on a reimbursement model of about 35% or 40% reimbursement—but we also help companies develop IP strategies. We help them diversify their markets and find new customers in Canada and internationally.

[Jenna Sudds, Liberal, Kanata, ON]

I love that you touched on the collaboration. It's one of the things I have been struck by over this last year. The number of Canadian companies working collaboratively towards building out sovereign solutions is incredibly inspiring. I certainly see the role that NGen is playing in helping to facilitate that.

[Jayson Myers]

I'm off to Germany this evening, where there will be 100 Canadian companies and our project partners at Hannover Messe. It is the largest industrial technology show in the world.

[Jenna Sudds, Liberal, Kanata, ON]

That's incredible. We wish you luck. I look forward to hearing the results from that mission.

Here in Ottawa, as recently as last week, I was able to visit the Canadian Photonics Fabrication Centre, which I think highlights the importance of advanced manufacturing and enabling next-generation technologies.

Can you speak to the role that photonics and advanced fabrication play in supporting the development of our AI systems? I'm thinking particularly of sensors, data transfer and these types of areas.

[Jayson Myers]

Photonics, lasers and the development of the electronics infrastructure behind AI applications are all part of what I was referring to as the technology stack required for successful adoption of AI solutions in industry, not just in manufacturing. It's important.

First, we can look at the need for sensors, the need for real-time command and control information that really requires edge solutions in a manufacturing application, to ensure that the latency is not expanded in a way that couldn't be used effectively in manufacturing processes. Integrating not only the data coming from the equipment machinery but also the software being used for materials handling for enterprise resource processing in manufacturing really shows how important it is to focus on the integration of technology. Today, we have robotics and vision systems, and many of these systems are already AI-enabled.

The important message I have is this. When looking at AI, don't think of AI simply as algorithms. We need to focus on how that is integrated into the technology stack that can deliver improved productivity results at the end of the day.

[Jenna Sudds, Liberal, Kanata, ON]

I see your point on the technology stack.

Do you see opportunities for Canadian companies to further our ability to build out that Canadian stack or digital infrastructure for AI specifically?