Canada’s AI Adoption Gap: What the ALL IN 2026 Top 100 Shows

Canada solved research. Usage is the open question.

For years, Canada’s AI story was primarily a research story. The country produced world-class talent, groundbreaking research, and globally respected institutions. Yet much of the commercial value created from those innovations ended up elsewhere.

That is beginning to change. Today, Canadian founders are building companies designed not to invent the next AI breakthrough, but to put AI to work inside hospitals, insurance companies, factories, banks, and government institutions.

The biggest challenge for Canada’s adoption of AI is no longer research. It is enterprise implementation. It is no longer about whether Canada can build AI, but whether Canadian organizations will actually use it.

What the ALL IN 2026 Top 100 actually shows

More than 330 startups applied, with 100 companies selected to present in Montreal this September. While the list is not a ranking, it provides one of the clearest snapshots of where Canadian AI entrepreneurship is heading.

The selected startups span healthcare, enterprise software, insurance, financial services, manufacturing, agriculture, cybersecurity, construction, and public infrastructure. One pattern stands out immediately: very few companies are building foundation models or consumer AI chatbots.

Instead, founders are tackling operational problems where:

• Workflows are long and complex
• Data is highly sensitive
• Regulations matter
• Mistakes are expensive
• Legacy systems cannot simply be replaced

These are significantly harder problems than consumer AI, but they also represent where most of the economy operates.

The 12% to 60% opportunity for Canada

The shift toward enterprise AI is not accidental. Canada already has strong industries in healthcare, insurance, energy, banking, and public infrastructure. What Canada lacks is widespread adoption.

Statistics Canada reports that only 12% of Canadian businesses used AI to produce goods or services between mid-2024 and mid-2025. Recognizing the gap, the Government of Canada’s AI for All strategy has set a national target: 60% of Canadian businesses using AI by 2034.

That gap represents one of the biggest commercial opportunities in Canadian technology today. It is no longer a research problem. It is no longer a funding problem. It is an adoption problem.

The ecosystem is building rooms for buyers, not vendors

Canada’s AI ecosystem has recognized this shift. Scale AI, Mila, NEXT Canada, and MaRS have spent years moving the conversation beyond research papers and demonstrations toward real-world deployment.

The ALL IN conference reflects the same philosophy. Instead of filling conference halls with technology vendors pitching other technology vendors, the event intentionally prioritizes enterprise buyers, industry operators, business leaders, and public institutions. The goal is not to showcase innovation. The goal is to get AI deployed inside organizations.

Why the model matters less than the workflow

Enterprise buying decisions have quietly changed. For several years, AI conversations focused almost entirely on the model. Questions included:

• Which model is larger?
• Which one reasons better?
• Which one produces more accurate responses?

Those questions still matter, but they are no longer the deciding factor. As AI models continue to converge in capability, the real competitive advantage increasingly exists outside the model itself.

Success depends on how well AI integrates with existing software, business rules, user permissions, exception handling, approval workflows, and accountability systems. The model is becoming less important than the workflow.

From answering questions to completing work

The strongest enterprise AI companies are changing what they sell. Instead of selling answers, they sell completed work: invoices automatically reconciled, compliance cases assembled for review, clinical notes generated during patient appointments.

This represents a fundamental shift. Most organizations are not lacking information. They already have documentation, training material, and experienced employees. The real bottleneck is execution. Someone still has to:

• Open multiple applications
• Navigate complex workflows
• Handle undocumented exceptions
• Complete tasks without making mistakes

AI explanations help people understand work. They do not complete the work. And in industries like healthcare, finance, and insurance, fully autonomous AI is often unacceptable. What organizations actually need is controlled execution. That means AI performs work:

• Inside existing enterprise software
• According to company policies
• With approval gates where necessary
• While maintaining a complete audit trail

Where AgenQ fits

This is exactly the problem we are solving at AgenQ, and we are proud to be one of the companies included in the ALL IN 2026 Top 100.

AgenQ builds an execution layer that sits inside enterprise software. Users describe what they need in plain language, and AgenQ completes the task inside the organization’s existing software while respecting business rules, user permissions, and approval workflows. The organization’s existing software remains the system of record. AgenQ becomes the system of action.

GPS for enterprise software

We often describe AgenQ as GPS for software. GPS never replaced roads. It simply understood where you wanted to go and guided you along infrastructure that already existed.

An execution layer works the same way. Organizations have already invested years, and significant resources, building their software ecosystems. Those systems do not need replacing. Users simply need an easier way to accomplish work inside them.

We began in the insurance industry, where workflows are lengthy and mistakes are costly. But the underlying challenge exists across nearly every enterprise sector, and stronger enterprise workflows will accelerate Canada’s AI adoption across regulated industries.

What closes the gap

Canada has already proven it can lead in AI research. That debate is settled. The next opportunity is far more practical: the winners will be the companies that make AI usable, trusted, deployable, and measurable inside industries Canada already understands.

Talent, capital, infrastructure, and public support are increasingly aligned. Closing Canada’s AI adoption gap will not come from another foundation model. It will come from hundreds of companies solving specific operational problems where the work is real and the business impact is measurable. Judging by the ALL IN 2026 Top 100, that transformation is already underway.