Industry and Technology Committee on May 7th, 2026

[The Chair, Liberal, Ben Carr]

Good morning, everybody.

We are continuing today with our study on AI. We have a few witnesses here in the first hour. We're going to have a few more in the second hour.

Joining us from the Centre for Designing Change, we have Kulbir Colin Singh Dhillon, who is the executive director. From INQ Law, we have Carole Piovesan, who is the managing partner. From Oracle, we have Hamza Jahangir, who is the vice-president, AI solution engineering.

Welcome to you all. Thanks for being here.

I can confirm that all audiovisual tests have been completed.

Mr. Singh Dhillon, this is just for you, sir, since you're in the room. If you're not using your earpiece but it's plugged in, please just make sure that it's placed on the sticker in front of you. That's to protect the health and well-being of our interpreters.

Witnesses, you'll have up to five minutes for introductory remarks, which will be followed by a line of questioning from the members representing the various political parties around the table.

With that, Mr. Jahangir, I am going to turn the floor over to you.

[Hamza Jahangir, Vice-President, AI Solution Engineering, Oracle]

Honourable Chair and members of the committee, thank you for the opportunity to appear today as part of your study on artificial intelligence.

I'm here on behalf of Oracle Canada. We provide cloud infrastructure, data platforms and AI capabilities that are used by governments and regulated industries. In Canada, we support public and private sector organizations, including SMEs, to adopt AI in ways that are secure, compliant and operationally practical. For over 45 years, Oracle has supported the Canadian public sector, including federal and all provincial and territorial governments. Throughout Canada, Oracle technologies support citizen services, finance, human resources, education, government operations, defence, intelligence and public safety.

AI is now a core driver of productivity and service delivery, from improving manufacturing and construction outcomes to strengthening cybersecurity and reducing administrative burden in the public sector. Realizing these benefits, however, depends on deploying AI with strong safeguards, transparency and accountability.

From our perspective, three priorities matter most for Canada: trusted data foundations, sovereignty and security by design, and enabling adoption at scale across Canadian organizations.

First is trusted data foundations. AI outcomes are only as strong as the data behind them. In many organizations, data remain fragmented, limiting reliability and increasing risk. Canada should prioritize policies and investments that enable secure data integration, strong governance and auditability, particularly for high-impact use cases. Organizations need to bring AI into governed data environments with clear, enforceable controls about who can access data, how it is used and how decisions can be tracked and audited. This level of traceability and oversight is essential for critical services and regulated sectors where accountability, risk management and public trust are paramount.

Second is sovereignty and security by design. Data protection and control are central concerns for Canadians and for this committee. Oracle already operates cloud regions in Canada—in Toronto and Montreal—supporting customers that require data residency and compliance with Canadian requirements. We also provide higher-assurance deployment models, including dedicated and sovereign cloud options that are designed to meet specific regulatory and national security needs through stronger operational control, segregation and compliance.

In practical terms, sovereignty must be grounded in verifiable safeguards, strong encryption, robust identity and access management, clear separation of duties and comprehensive auditability. Additional capabilities, such as confidential computing, can further protect sensitive data while it is in use. As Canada considers its policy approach, we would encourage a focus on measurable security outcomes and enforceable standards, ensuring that sovereignty is demonstrated in practice, not just defined in principle.

Third is enabling adoption at scale, especially for SMEs. While Canada has strong AI research capacity, adoption remains uneven. Many organizations, particularly small and medium-sized businesses, face barriers related to cost, skills and procurement complexity. Oracle works with Canadian organizations to modernize data platforms and deploy AI securely, including through partner-led models that help businesses adopt cloud and AI, with the controls required in regulated environments. Expanding skills development and certification pathways will also be critical to building the workforce needed to scale adoption across regions and throughout the supply chain.

In closing, Canada is well positioned to lead in responsible AI. Doing so will require a balanced approach of strengthening trust, advancing sovereignty and enabling practical adoption at scale.

Oracle stands ready to support this committee's work by sharing technical expertise and real-world implementation experience.

Thank you. I look forward to your questions.

[The Chair, Liberal, Ben Carr]

Thank you very much, sir.

Ms. Piovesan, we'll turn the floor over to you. You'll have up to five minutes.

[Carole Piovesan, Managing Partner, INQ Law]

Good morning, and thank you, Mr. Chair and honourable members of this committee.

As you heard, my name is Carole Piovesan. I am the co-founder and managing partner at INQ Law, where we advise clients on privacy, data governance and AI risk management, among other practices of law. I have previously appeared before this committee and the ETHI committee. I'm an adjunct professor at the University of Toronto faculty of law, where I teach AI regulation. I want to be clear that the opinions I share today are my own.

For the current study undertaken by this committee on data sovereignty, AI adoption and strategic industrial sectors in Canada, I offer three recommendations.

The first is to modernize Canada's federal privacy law, which is something this committee has heard multiple times from multiple witnesses. It is long overdue and an important step in Canada's strategy for data sovereignty. Though technology neutral, PIPEDA, in its current form, was not designed for such sophisticated data-processing activities or processing technologies as AI. AI systems are collecting, inferring, profiling and making decisions about Canadians under a statute that predates the smart phone.

The gap between what the law requires and what the AI practice demands is widening every year. Refreshed privacy law—modernized privacy law—would also support AI development. Updated rules on data collection, use and disclosure in the context of AI would enable the creation of trusted Canadian datasets for training AI systems and foster the private investment in AI development.

Privacy is a critical value in Canadian society that we must protect and defend. While modernized privacy law alone will not address all AI risks, it is one important and overdue step; and it is notable that provincial and territorial privacy commissioners are starting to weigh in on the use of personal information in the context of AI risking a fragmented approach and narrative on AI.

Second is to build a Canadian AI assurance market. You've heard many times in this committee, including from the speaker just before me, that Canada is known for responsible AI and has made significant investments in AI ethics, good governance policy and research over the past decade. We have an advantage in creating an AI assurance market like no other. Not only is that good business for Canada, it's also essential for supporting safe and beneficial AI development and deployment in Canada.

To that end, Canada should build a functioning AI assurance market with accredited auditors, testing labs and certification bodies that assess AI systems before and after deployment in high-impact contexts. Those building blocks already exist. Some are homegrown through some of our regulators, including Health Canada or OSFI, which have been piloting or embedding different AI assurance mechanisms into their sector guidance. Also, there's international guidance through the ISO 42000 series for AI management. Canada played an important role in the development of those standards.

Moreover, federal procurement and funding mechanisms should require assurance evidence for high-impact AI use cases, insurers should recognize certified AI governance in their underwriting, and standard-setting bodies should develop an accredited AI assurance profession. For Canada, this is also a commercial opportunity, positioning Canadian firms as the credible, trusted alternative in global AI procurement. It is also essential for the advancement of responsible AI, because governance without verification is aspiration. We can write rules, but unless there is an independent mechanism to test whether AI meets those rules in practice, in real-world deployments and not just lab benchmarks, the rules are largely symbolic.

Third, make AI safety Canada's signature contribution. Canada has a distinctive opportunity in AI safety. We have world-class AI talent and a long track record of responsible, multilateral engagement. AI safety should be Canada's brand. The Canadian AI Safety Institute is a start, but it must operate at full force, and it must be backed by the kind of sustained policy investment that signals to the world that Canada takes this seriously.

The core regulatory ask here is simple. Companies must demonstrate transparency in their risk management processes and show that the systems they are building will minimize harm.

Canada also has an opportunity to work multilaterally to secure international treaties and support among some like-minded countries.

In closing, here are three steps: modernize our privacy laws as the foundation for data sovereignty, build an AI assurance market that turns accountability into a competitive advantage, and establish AI safety as a sustained Canadian brand and priority at home and through coalitions around the world.

Thank you.

I welcome the committee's questions.

[The Chair, Liberal, Ben Carr]

Thank you very much.

Mr. Singh Dhillon, the floor is yours, sir.

[Kulbir Colin Singh Dhillon, Executive Director, Centre for Designing Change]

Good morning, Mr. Chair and honourable committee.

Thank you for the opportunity to be here today.

Canada has built a strong reputation in artificial intelligence. We are known globally for our research and our talent, and the foundations we've helped create in this field. The question in front of us now is not whether Canada understands AI, it's whether we are turning that understanding into real outcomes. The reality is that Canada doesn't have an AI research problem, rather, we have an AI translation problem.

We are very good at developing models and advancing ideas at a research level. We are much less effective at deploying these systems into the environments where they actually create value, for example, factories, supply chains, infrastructure and the core industries that drive our economy. The gap becomes even more important as AI evolves—and it is evolving on a daily basis.

We've seen a version of this before. In other sectors, early signals were dismissed as premature until they became dominant industrial realities. Physical AI is following a similar trajectory, and the window to build capability may be shorter than it appears.

We are now entering a phase where AI is no longer just digital. It's becoming physical, embedded into machines, robots and industrial systems that operate in the real world. This includes what we describe as “human-centred robotics” and, in some cases, “humanoid systems”, technologies designed to work alongside people, not just behind a screen.

This shift has direct implications for Canada's strategic industries, manufacturing, mining, transportation and construction, because this is where productivity, resilience and long-term competitiveness will be shaped.

Canada is not yet structured to lead in this next phase. We have the building blocks. What we are missing is the capabilities and the abilities to bring them together in a coordinated fashion, to test these systems, validate them, integrate them and deploy them at scale. This is the gap we are focused on at the Centre for Designing Change.

One example of this is a national initiative around human-centred robotics and humanoids. This is not about chasing a headline or building a single product. It is about using that platform to map Canadian supply chains; validate domestic capabilities; test systems in real environments, including extreme conditions; and ensure Canadian companies are part of what comes next.

This work builds on CDC's broader national efforts in physical AI, including recent work with NGen Canada examining Canadian industrial readiness and capability gaps in this space. If we don't build capability at that level, we risk relying on systems developed and controlled elsewhere in the very sectors we depend on.

This connects to a broader issue that has come up in this study, which is data sovereignty. Data matters, because it's the context of AI. Sovereignty goes beyond where data sits. It extends to who controls the systems, the infrastructure and, ultimately, the deployment layer. Without that, we may participate in the AI economy, but we won't shape how it operates.

As AI moves into physical environments, the challenge changes. It's no longer just about intelligence, it becomes about interaction. These systems are working alongside people. They need to interpret human behaviour, respond appropriately and know when to escalate or to step back safely. If they can't do that reliably, adoption will slow, not because the technology doesn't work but because people won’t trust it. This is what we mean when we talk about emotionally intelligent AI not as an abstract idea but as a practical requirement for safe and effective human-machine collaboration.

As we think about policy and regulation, the focus needs to expand. It's not just about developing AI, or even regulating it. It's about enabling deployment responsibly at scale and in the sectors that matter most.

If I can leave you with one thought, it is that Canada does not lack ambition in AI; we lack infrastructure to execute it. If we can close that gap, particularly in physical AI and strategic industries, we will have a real opportunity not only to participate in this shift but also to actually lead in it.

Thank you very much for your time.

[The Chair, Liberal, Ben Carr]

Thank you very much, witnesses, for your opening testimony.

We're now going to enter into the first round of questions.

Mr. Guglielmin, the floor is yours for six minutes, sir.

[Michael Guglielmin, Conservative, Vaughan—Woodbridge, ON]

Thank you, Chair.

Thank you to the witnesses today for all of your expert testimony.

Mr. Dhillon, we've heard a lot at this committee about artificial intelligence and its use case to increase productivity, help us with the productivity issues and improve efficiencies. Really we've heard two different sides of this argument with respect to jobs. On the one hand, AI as a tool could be used to create jobs, and we just need to re-skill people. On the other side of the equation is more of a quasi-doomsday scenario, where AI could lead to mass unemployment.

From your perspective, where do you think the truth is, and what do you think the impact of artificial intelligence on jobs will be?

[Kulbir Colin Singh Dhillon]

I describe in my book Soulful AI that AI should not be deemed to be a technology. Rather, it's a digital species. It's like no other development that's been created by humanity. You can't compare it to steam, electricity and computer power. The objective of AI is intelligence and the growth of its intelligence, so companies are currently working towards AGI, which is general intelligence, which means it's as good as if not better than humans, with superintelligence being maybe a decade or so behind that.

What will this do to the industrial revolution model that we all live in today? The reality is that we live in a period that is governed by the structure of industrial revolutions. We're currently in 4.0.

The largest line item on anyone's balance sheet, for any corporation or any company, is human labour. The trends over the last 40 years of sending products and services overseas were simply to offset the balance sheet for human labour and the cost of the hourly wage. Let's not kid ourselves that corporations and companies will not look to reduce the head count of their companies if they can, both digital and physical, because they will. These are the trends. Just this past Monday, I think, Meta announced 16,000 layoffs in its organization.

We are in a period when the growing use of AI, currently, I would think, attacking white-collar jobs, is occurring, but inevitably this will affect both blue-collar and white-collar jobs. Inevitably there will be a head count reduction.

[Michael Guglielmin, Conservative, Vaughan—Woodbridge, ON]

Thank you for that.

You also described in your opening statement humanoid robotics as a strategic opportunity for Canada. I was wondering if you could briefly define what you mean by that and then say who you would say is winning that race today and where Canada currently ranks.

[Kulbir Colin Singh Dhillon]

I'll start from the back end of the question.

Who's winning the race today outright is China. China has registered 50 to 150 humanoid robotics companies. The advancements in humanoids in the past five years are all down to artificial intelligence. They are all down to edge computing with neural networks doing end-to-end communication. China's leading because, if we are not aware, China's going to have a birth rate drop-off that is going to make Japan's look like it was not an issue, and China's going to backfill that drop-off in birth rate with automation, period.

I think we all understand how we have things work economically, and there is a global race. The U.S. is behind, at 20 to 25 companies. Canada currently has two companies registered that are building humanoids. Our objective with our project, similar to when I built and worked on Canada's first electric vehicle, is to build a foundation so we can compete on a sovereign level globally, where we're not, as Canadians, having to buy Chinese products or U.S. products but rather buying Canadian products to service our own industries.

[Michael Guglielmin, Conservative, Vaughan—Woodbridge, ON]

You have also mentioned that here we have world-class talent. We have world-class critical minerals. In some respects, especially with respect to education and information, we have a head start on artificial intelligence and advanced manufacturing. You've argued that our constraints aren't the capability. Our constraints are execution and capital.

In my view, the government's role is to provide the environment for that to flourish. What do you think our number one risk is if we get that wrong, where we don't create the environment to have these sorts of capabilities flourish, and we become importers of technologies from countries like China or the United States?

[Kulbir Colin Singh Dhillon]

I think that in the context of artificial intelligence—be it digital or physical—it is a moving target, and it's moving at a pace where, if you aren't on top of your large language models over a period of two or three months, you're falling behind.

I think there has been some really positive news over the past few months of capital coming back to Canada, where it is direly needed. I work with multiple start-ups, and I have my own. Every start-up needs two things: customers and capital.

If we don't get this right.... Let me just put into context physical AI, in the context of humanoids. Morgan Stanley's report from April 2025 suggests that by 2050, humanoids will be a $5-trillion revenue industry. In context, that's double the size of the global automotive industry.

I grew up in the auto industry. I spent 25 years there. Let's not kid ourselves. The IP and revenues go outside our country. We don't own a Canadian car company, nor would I recommend that we do in 2026. However, we have a golden opportunity to lead in physical AI because, as you mentioned, we have all the pieces here. I think we're in this imperative period over the next few years to come out and to lead. If we do this, I think we could truly be a global leader.

[The Chair, Liberal, Ben Carr]

Thank you very much.

Mr. Ntumba, you have the floor for six minutes.

[Bienvenu-Olivier Ntumba, Liberal, Mont-Saint-Bruno—L’Acadie, QC]

Thank you, Mr. Chair.

Ms. Piovesan, in your presentation, you said that Canada was a leader in artificial intelligence research. You also added that we need to make AI safety Canada's signature contribution.

I'm going to go back in history a little to see how humanity has evolved and how man has adapted. Today, artificial intelligence has come along, and we're talking about jobs being at risk. In the past, at one time, people used horses. Then the first car was invented, and then it evolved to become the hybrid car and the electric car. That creates parallel professions.

Artificial intelligence is coming in forcefully, and it's evolving at a rapid pace, which will have repercussions.

What should our government do to mitigate these repercussions and find a solution?

[Carole Piovesan]

Absolutely, AI is going to have an impact on the job market. Part of this—and you've heard this before—is about redirecting where some of the jobs will be, and investing in and upskilling much of our labour.

I am in the legal profession, where we are acutely aware of the impact of artificial intelligence on the delivery or support of legal services, and we are actively thinking about ways in which our lawyers across Canada are being deskilled and about how we upskill.

Part of the reason that I suggest a Canadian AI assurance market is because AI is an incredibly important economic force. We have seen this through the inputs received by the government in response to the AI sprints. We need to think dynamically about where jobs will be created and skills will be needed, and redirect in those areas.

I would posit that an AI assurance market is one area where there are specific investments that can be made that leverage our existing strengths, that speak to the responsible made-in-Canada AI brand, and that advance safety mechanisms that can allow us to use this technology in a safe manner that is tested and verified.

[Bienvenu-Olivier Ntumba, Liberal, Mont-Saint-Bruno—L’Acadie, QC]

Thank you very much.

Mr. Dhillon, in your presentation, you said that Canada lacks infrastructure.

Can you tell us what needs to be done to get more? How do we get it built?

The research is done. Canada doesn't have a problem in that regard, but there's a lack of infrastructure.

What would it take, in concrete terms? How can we move forward on this?

[Kulbir Colin Singh Dhillon]

How can we get ahead with regard to the infrastructure? I think we need possibly two national policies that support AI and the implementation of physical AI. I think these have to identify key sectors, and then I think that becomes the foundation to really accelerate the adaption of artificial intelligence.

I've spent several years working with industrial revolution 4.0 digital manufacturing implementations, and I can tell you that it is an absolute struggle having companies adapt sensory technologies, IoT devices, to collect data in manufacturing here in Canada, specifically in the auto industry. I think it could be an uphill challenge if there isn't a national policy on AI and physical AI.

[Bienvenu-Olivier Ntumba, Liberal, Mont-Saint-Bruno—L’Acadie, QC]

When you hear about digital sovereignty and technological sovereignty, what do you think they mean?

How would you explain them to lay people who know nothing about these areas?

[Kulbir Colin Singh Dhillon]

Regarding data sovereignty, a lot of people think that data is magical and that any unit that is collecting data is somehow giving positive data. It doesn't work that way. Data is deemed to be dirty or clean. You have to sift through to get the quality out of the data.

AI is not the software on your phone or on your laptop; it's the data centres. The data used and the compute used need to be the back end of what AI is modelled on. In order for you to model your AI, you need to collect data. One of the reasons a lot of these large language models today have issues like biases is that they scrape the Internet to collect the data. Let's be honest. The Internet isn't exactly the most prestigious and pristine element. It's a bit of a cesspool at times.

Data sovereignty means that Canada is generating terabytes of data in every sector in municipalities, provincially and federally, and we need to find a strategy and build a system where we own our own data. Having that good, high-quality data potentially gives us an edge over our competitors.

[The Chair, Liberal, Ben Carr]

Thank you very much, Mr. Ntumba.

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

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

Thank you, Mr. Chair.

Greetings to my colleagues and the three witnesses. I thank them for being with us today. Their testimony is very enlightening.

My first questions will be for Mr. Jahangir from Oracle.

A few weeks ago, we had Yoshua Bengio here. He told us that a strong regulatory framework would not hinder the development of artificial intelligence, that it was more the financial stakeholders who were reticent about the European or Canadian models.

Do you agree with what Mr. Bengio said?

[Hamza Jahangir]

I'm sorry; I didn't totally get the question. If you don't mind, could you repeat it one more time?

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

I'll repeat my question.

A few weeks ago, Mr. Bengio came to meet with us. According to what he told us, a strong regulatory framework would not hinder the development of artificial intelligence, and the reluctance comes mainly from financial stakeholders, particularly with regard to the European model or the Canadian model.

Do you agree with that statement?

[Hamza Jahangir]

I first want to say that a regulatory framework is necessary to help accelerate AI development. Today—I think my fellow panellists touched on this—AI cannot be its own thing. It has to be part of a larger governance framework. In fact, the safety aspect of AI—delivering the right kinds of AI applications to the right end-users for human benefit—is what we are all working together on in order to get the right balance. Therefore, we continually balance new opportunities to improve the lives of citizens, government organizations and private-sector entities with safety. We truly look to regulatory bodies, whether governmental or non-governmental organizations, to help build that framework and put it in place.

In addition to comments being made around data, like the necessity of having the right, high-quality data powering large language models in order to get the right answers and do it securely and safely.... That is the larger problem statement that the whole industry and the world, right now, are experiencing.

Regulation and regulatory frameworks are, from my perspective, enablers, not hindrances.