We have started to see where Canada wants to go with AI after seeing some early insights into our countries AI strategy.

It’s great news that we’re finally getting to a strategy, a more useful question is not whether Canada has an AI strategy, It is whether the strategy is strong enough to help Canadian organisations move from interest to adoption, and from adoption to real capability.

Are we moving fast enough?

I took a look at what has been released against other jurisdictions and it’s a little uncomfortable in places.

Canada is aiming to move business AI adoption to 50%+ AI adoption by 2030. This doesn’t feel like an ambitious enough target. But it’s also a reminder that this is not just a technology issue. It is an execution issue. It is a management issue. It is a governance issue. And increasingly, it is a competitiveness issue.

What is it?

Canada’s emerging AI strategy is best understood as a balanced strategy, it seems to be trying to do several things at once:

• Increase AI adoption across the economy
• Support SMEs (which will be really interesting to see)
• Protect Canadians from AI-related harms
• Build sovereign AI infrastructure
• Invest in talent and research
• Modernize government services
• Position Canada in trusted international AI alliances

That is a sensible mix and also reflects a very Canadian pattern: careful, inclusive, values-led, and somewhat cautious. A lot of the emphasis is on broad adoption, especially by small and medium-sized businesses, which matters because the practical AI gap in Canada is not mainly about whether we have smart researchers - we do, no doubt.

The issue is whether organisations are actually using AI to improve operations, service delivery, productivity, decision-making, and competitiveness.

The global comparison makes the picture more interesting, my interpretation is:

• The United States is framing AI as a race for economic and national security leadership, with huge private-sector momentum and massive infrastructure investment.
• China is treating AI as a national development priority, using state direction, industrial policy, targets, and large-scale deployment across sectors.
• The UK seems to have shifted toward a more explicit pro-growth and pro-adoption approach, with an emphasis on moving quickly from opportunity identification to pilots and scaling.
• Europe is trying to combine regulation, sovereignty, industrial strength, and major investment, with France and Germany seemingly linking AI to competitiveness and industrial transformation.
• The Middle East, particularly the UAE and Saudi Arabia, is treating AI as part of national transformation, public-sector modernization, and economic diversification.
• Australia looks more similar to Canada in some ways, with a balanced approach, concern about adoption, and a need to scale capability without the same resources as the US or China.

Canada’s position is not weak, but it is measured - and maybe too timid?

The strategy includes useful ideas but the practical question is whether these ideas turn into enough action, fast enough, at the organisational level. That is where strategy often succeeds or fails - not in the announcement but in the implementation.

What does it mean from a business perspective?

Canada’s AI challenge is adoption, not awareness. Most leaders I speak with are aware of AI. Many are experimenting. Some have policies. A smaller number have moved into repeatable, governed, value-producing use. The real gap is the distance between interest and embedded capability.

• The strategy is strongest where it focuses on SMEs and practical adoption. This is important because most Canadian organisations are not going to build frontier models or national-scale AI infrastructure. They are going to use AI to improve writing, analysis, reporting, customer service, procurement, finance, HR, operations, project delivery, and decision support - and need support to make it happen.
• Compute matters, but it is not the whole story. Sovereign AI infrastructure is important, especially for research, sensitive data, public-sector work, and long-term national capability. But many organisations are not stuck because they lack a supercomputer. They are stuck because they lack use-case discipline, data readiness, governance, training, confidence, and implementation capacity.
• The global leaders are linking AI to execution. The more aggressive jurisdictions are not just talking about responsible AI. They are creating adoption pathways, sector targets, public-sector pilots, procurement levers, AI hubs, compute access, talent pipelines, and industrial programs. The lesson for Canada is that principles need delivery mechanisms.
• There is a risk that Canada over-indexes on trust and under-indexes on use. Trust matters. Governance matters. Privacy, safety, transparency, and accountability matter. But if the result is hesitation rather than responsible movement, we will fall further behind countries that are learning by doing.
• There is also a risk that organisations wait for perfect clarity. I wonder if many Canadian leaders are still looking for a stable regulatory environment, a mature tool market, or a definitive best practice before moving. That is understandable, but AI capability is developing through iteration. The organisations that start carefully now will be in a much better position than those waiting for certainty.
• Public sector has a major role to play. Government can help by using procurement, pilots, and service modernization to create demand, demonstrate responsible use, and support Canadian AI vendors. But this needs to be practical. Long procurement cycles, unclear risk ownership, and fragmented approval processes can easily cancel out good strategic intent.
• Talent is broader than specialists. Canada needs AI researchers, engineers, data scientists, and infrastructure experts. But most organisations also need AI-capable managers, analysts, project leads, policy people, procurement staff, finance teams, HR teams, and frontline supervisors. AI literacy cannot be limited to technical teams.
• The business case is not labour savings. The value will come from time saved, but a lot will come from better throughput, faster analysis, improved service responsiveness, better use of institutional knowledge, and reduced friction in document-heavy work. Leaders need to look beyond simple headcount reduction models and the ‘race to the bottom’ that will drive.
• Governance needs to be light enough to use. Many organisations either have no AI governance or are at risk of creating governance that is too heavy for everyday adoption. The goal should be practical guardrails: what tools can be used, what data can be entered, what outputs require review, where human approval is required, and how risks are escalated.
• The competitive issue is cumulative. A single AI use case may not change much. But hundreds of small improvements across reporting, analysis, client service, operations, compliance, and internal knowledge work can compound. That is where organisational advantage starts to show up.

What do I do with it?

• Treat AI adoption as an operating capability. Do not leave it as a collection of experiments, individual subscriptions, or disconnected pilots. Decide where AI fits into your operating model, service model, workforce plan, and technology roadmap.
• Map the work before applying AI. Look at where people spend time, where work queues build up, where documents are repeatedly created, where decisions require synthesis, and where staff are copying, summarising, reformatting, searching, comparing, or drafting.
• Start with real workflows, not impressive demos. A compelling demo can be useful, but it does not prove business value. Pick work that happens often, has clear inputs and outputs, and matters enough that improvement would be noticed.
• Separate judgement from production. GenAI can help produce drafts, summaries, mappings, options, first-pass analysis, briefing notes, policy comparisons, meeting summaries, and structured outputs. Humans still need to validate, interpret, decide, approve, and own the result.
• Build a simple adoption portfolio. You do not need to bet everything on one major AI transformation program. Identify a mix of low-risk productivity use cases, medium-complexity workflow improvements, and a few strategic opportunities that may need deeper investment.
• Create practical guardrails early. Define acceptable use, data handling, human review, tool approval, record keeping, and escalation. Keep it simple enough that people will actually use it.
• Invest in managers, not just technical users. Managers are the people who will decide where AI fits into work, what quality means, what risks are acceptable, and how teams change their processes. If managers do not understand AI well enough to lead adoption, progress will stall.
• Use pilots to learn, not to avoid decisions. A pilot should answer specific questions. Does this improve quality? Does it save time? Does it reduce backlog? Does it create risk? Can staff use it consistently? Can it scale? If the pilot works, have a path to operational use, it not - stop.
• Look at procurement and vendors differently. AI is increasingly embedded in software, platforms, and services. Organisations need better questions around data use, model behaviour, explainability, security, intellectual property, auditability, and vendor accountability.
• Measure more than time saved. Track cycle time, quality, user satisfaction, rework, backlog, consistency, risk reduction, service responsiveness, and staff capacity. AI value is often broader than simple productivity numbers.
• Do not wait for the national strategy to become your organisational strategy. This is key - national direction helps. Funding helps. Standards help. But every organisation still has to decide where AI belongs in its own work, and literacy is key.

The broader lesson from the global comparison is that AI leadership is not only about who has the best model - lots of countries and geographic areas are pushing forward harder than we are. It is about who can turn AI into useful capability.

Canada has some real strengths: research depth, talent, public trust, democratic values, strong institutions, and a sensible instinct for responsible adoption. but the countries moving fastest are not waiting for AI to become neat and settled. They are building infrastructure, running pilots, setting targets, using public-sector demand, developing talent, and pushing adoption into real sectors of the economy.

For Canadian organisations, the next move is not to admire the strategy from a distance, It is to ask a much more practical question - ‘Where should AI be changing the way we work this year?’