Understanding Canada’s AI Strategy
Global AI ambitions are real, and Canada is the latest in a string of countries to formalise them through a national strategy aimed at boosting the economy, upskilling broad sections of the national workforce, and capitalising on its position as a leader in AI research.
Freya Scammells
AI Practice Lead
freya.scammells@caspianone.co.uk
As Artificial Intelligence becomes entrenched in our everyday lives, governments around the world are developing plans and strategies to harness the power of AI for economic growth and to improve fundamental public services. Just last year, the UK introduced its AI Opportunities Action Plan, aiming to make significant investments in AI across infrastructure and the wider economy. Now, the Canadian Government have released their own National AI Strategy with the aim of expanding Canada’s reputation beyond a global AI research leader and into a national leader in AI adoption, commercialisation, and governance.
AI has fast become a driving force behind economic growth, productivity, and political influence, and the plans laid out in Canada aim to build an AI ecosystem that is trusted, safe, widely adopted, and less reliant on foreign infrastructure. The country already has strong roots in AI research, but the Canadian AI strategy recognises the opportunity for growth, job creation, and enhanced innovation across the region.
From an industry perspective, this shift in emphasis is significant. Strong research foundations alone do not guarantee economic impact, and what ultimately matters is how effectively AI is embedded into organisations, workflows, and decision-making. This is a reality many enterprises are already grappling with as they attempt to move beyond experimentation and into real-world use.
What is Canada’s AI strategy?
Canada’s AI strategy is described as a national roadmap to transition Canada from an AI research leader to an AI adoption and economic leader. The roadmap is built around six key pillars, including safety, skills, adoption, infrastructure, scale, and partnerships, all centred around one core premise of “AI for All”. When broken down, the strategy intends to make AI accessible, useful, and beneficial across society while establishing trust in AI products to drive further adoption and economic value. Here is a simple overview of the six pillars.
Ensuring AI is Safe and Trusted
The first pillar of the strategy focuses on AI safety, governance, and responsible use, emphasising that AI development should address risks such as bias, privacy concerns, misinformation, and misuse. Throughout the strategy, trust is recognised as a precondition for adoption, and here, within this pillar, is where trust is won or lost. Without confidence from the public or through organisations, AI uptake will be limited. Therefore, it is imperative that standards, guardrails, and partnerships are developed to encourage safe development and deployment of AI, rather than creating blanket restrictions on its use.
In practice, trust is rarely built through policy alone. It is shaped by how AI systems are designed, tested, and deployed day to day. Being transparent about data use, model limitations, and human oversight tends to accelerate progress because trust enables adoption.
Empowering Individuals Through Preparation and Supporting Skills Development
While safe adoption and responsible use remain paramount and underpin Canada’s proposed AI strategy, it is also understood that AI literacy needs to be broadly understood across the country’s workforce. The proposal also seeks to implement advanced skills development for technical roles and to offer support to individuals affected by AI-driven change. The bottom line here is to reskill and upskill broadly and at scale to create a baseline of AI understanding.
This focus closely mirrors what we see across industry. For technical professionals, particularly those coming from traditional software engineering backgrounds, transitioning into AI-focused roles is often an achievable step rather than a complete career reset. The greater challenge tends to sit with non-technical teams, where understanding how and when to use AI effectively requires a different type of enablement, one focused on practical application rather than theory.
Accelerating AI Adoption
With foundations and skills covered, driving AI uptake in businesses, especially SMEs, is high on the agenda. Without this uptake and buy-in from organisations, the downstream economic effects become negligible. Currently, Canada’s AI adoption rate is behind other comparable countries, and it is recognised within the strategy that for AI to become an integral driver for economic growth, it needs to move beyond pilots and into production. The aim is to foster practical enablement through incentives and support structures.
This is often the point where ambition meets reality. Many organisations are able to launch proof-of-concept projects, but struggle when it comes to scaling AI across the business. Integration with legacy systems, data quality, and change management frequently prove more challenging than the technology itself.
Strengthening AI-Ready Infrastructure
Compute capacity, data access, and digital and cloud infrastructure are all critical to Canada’s AI posture. AI development, deployment, and ongoing use are all dependent on the infrastructure, and a dependency on providers outside of Canada will lead to high costs and potential security risks. While the development of infrastructure is central to the proposed national strategy, it is centred around that infrastructure being built within Canada to retain sovereignty and domestic capability without the need to rely on external providers.
From a delivery standpoint, infrastructure can quickly become a limiting factor. Access to reliable, scalable compute is often one of the first bottlenecks organisations encounter when moving AI into production, making this pillar foundational to the success of all others.
Supporting AI Commercialisation and Scale
Canada has historically struggled with retaining IP and scaling domestic organisations, particularly around AI products and services. This has a knock-on effect on economic returns in the country when organisations either relocate or are acquired. The AI strategy states that under its fifth pillar, plans will be put in place to support AI companies to grow from start-up to scale-up and enterprise models with a focus on commercialising an already strong research sector.
To bridge the gap from research to productisation, technical excellence must be matched with operational maturity, customer readiness, and access to expertise that can support growth beyond early-stage development.
Strengthening International Partnerships
AI has become a global priority, with governments and organisations across the world paying close attention to the potential gains it has to offer from revenue and productivity to scientific breakthroughs. However, there are shared risks involved in the globalisation of this technology that require international collaboration, namely across safety standards and trade. The AI strategy aims to position Canada as a trusted global AI partner, balancing cooperation on the global stage with national interests.
What Are the Main Goals of Canada’s AI Strategy?
With six foundational pillars underpinning the ultimate goal of “AI for All”, Canada’s AI strategy is designed to enhance three main outcomes: economic output, societal advancements, and strategic goals for the region.
When it comes to economic output, it has been outlined that a significant increase in AI adoption across Canadian businesses is key to the success of the strategy. It is also noted that it will be measured across three criteria: gains in productivity, job creation in AI-enabled industries, and long-term GDP growth for the country.
Societal outcomes revolve around improvements to public services and creating a better quality of life across Canada. AI is expected to help make gains across core services by offering broader access to the benefits that the technology holds, not just to large enterprises. This can only be done, however, through reducing the fear and scepticism that follow AI through education.
What Challenges Are Expected?
Cost and ROI uncertainty, legacy systems, and limited in-house expertise are all contributing to a reduced uptake in AI among Canadian businesses. When you take into account the strategy's ambitions for moving out of pilot and into production, structured and scalable deployment of AI is significantly harder in practice. The strategy has a strong and clear aim to raise the economic impact that AI has in Canada, but in my experience, impact depends on how well AI is adopted within an organisation, not just how well it is researched. Without widespread adoption, targets set out in the strategy will become difficult to reach. That is why this proposal is ambitious, and if it embeds into real action being taken, it has the potential for significant change in how AI is viewed and used in Canada.
Aside from the economic impact of AI, there is a challenge to be faced around the public trust in AI. Concerns include how bias is handled and not perpetuated, how individuals’ privacy is handled and how data is used responsibly, and how misinformation and emerging threats like deepfakes are going to be tackled in practice, not just in theory. If people’s trust isn’t earned, adoption rates are likely to be slower than anticipated, and there’s a risk that over-correction can add further pressure to that trend, while under-correction could lead to backlash against AI altogether.
The big talking point, however, has to be around developing the right skills to carry AI adoption from its experimental phases into everyday applications at home and at work. The speed and scale of the training and education needed will be challenging to get right across such a broad intersection of society. For more technical roles, I’ve seen that the movement from traditional software development languages into AI positions is an absolutely achievable bridge to cross, but for non-technical professionals the learning curve will be steeper. A broader challenge also raises its head in the form of keeping that expertise in Canada. AI professionals are in high demand, and the competition for the best engineers is truly global, with many happy to relocate to pursue interesting and well-paying opportunities.
From an execution perspective, this is where strategies often succeed or fail. Investing early in practical skills development and creating clear pathways for people to work alongside AI tends to yield faster and more sustainable progress than approaches that treat AI purely as a technical initiative.
What’s Next For Canada’s AI Ambitions
Canada’s National AI Strategy is not short on ambition, but its real significance lies in what it acknowledges as much as what it proposes. It recognises that leadership in AI is no longer defined by research excellence alone, but by how effectively AI is adopted, governed, and trusted across an economy.
The emphasis on safety, skills, infrastructure, and adoption reflects a clear understanding that AI’s value is unlocked through implementation, not experimentation. This aligns closely with what we see across industry, where progress is often limited not by the technology itself, but by an organisation’s ability to integrate AI into existing systems, teams, and ways of working.
Trust and capability sit at the heart of this strategy. Building AI systems that people understand, can use confidently, and believe are being deployed responsibly is essential if adoption is to extend beyond early adopters and specialist teams. Skills development, in particular, will be a defining factor in whether AI becomes a day-to-day tool or remains concentrated in isolated pockets of expertise.
The success of Canada’s AI Strategy will be measured by its execution. Translating policy into practical action, supporting organisations through the transition from pilots to production, and retaining the talent needed to sustain progress will determine whether AI delivers long-term economic and societal value. If those challenges can be met, the strategy provides a credible foundation for AI to become a meaningful driver of growth and innovation across Canada.
About Caspian One’s AI Practice
Caspian One’s AI Practice helps financial institutions bridge the gap between AI ambition and AI impact by providing specialist AI talent with real financial‑market experience, not generalists or experimenters. Our teams understand trading logic, regulatory constraints, risk sensitivity, and the operational realities of financial markets, ensuring AI initiatives are built for measurable outcomes rather than stalled proofs of concept.
Led by AI & Data expert Freya Scammells, we support banks investing in AI for trading, risk, compliance and automation by aligning talent to business value, delivery requirements and governance expectations. By embedding practitioners who can navigate complex systems and regulated environments, we help institutions scale AI safely, effectively and with confidence.
Disclaimer: This article is based on publicly available, AI-assisted research and Caspian One’s market expertise as of the time of writing; written by humans. It is intended for informational purposes only and should not be considered formal advice or specific recommendations. Readers should independently verify information and seek appropriate professional guidance before making strategic hiring decisions. Caspian One accepts no liability for actions taken based on this content. © Caspian One, 2026. All rights reserved.
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