What the ALL IN 2026 Top 100 Says About Canada AI Adoption and the Problems Canadian Founders Are Choosing to Solve

By Varun Mishra, Founder & CEO, AgenQ
Published: July 30, 2026

In Short

• Canada AI Adoption is gaining momentum, but only 12% of Canadian businesses used AI to produce goods or services between mid-2024 and mid-2025, according to Statistics Canada. Canada’s national AI strategy aims to increase that to 60% by 2034.
• The ALL IN 2026 Top 100 selected 100 Canadian AI startups from more than 330 applicants. Almost none are building foundation models or consumer chatbots.
• The selected companies are focused on operational industries including healthcare, insurance, financial services, manufacturing, agriculture, construction, and public infrastructure, helping drive Canada AI Adoption in high-impact sectors.
• For enterprise buyers, the workflow now matters more than the model. The real value lies in software integrations, approvals, permissions, and execution—not just AI-generated answers—accelerating Canada AI Adoption across enterprises.
• A new category is emerging: controlled execution—AI that completes work inside an organization’s existing systems while respecting business rules, approval processes, and audit requirements, making Canada AI Adoption more practical and trustworthy.

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 AI Adoption is no longer research—it is enterprise implementation.

The challenge is no longer whether Canada can build AI.

The challenge is whether Canadian organizations will actually use it.

What the ALL IN 2026 Top 100 Actually Shows

This is what makes the ALL IN 2026 Top 100 particularly interesting.

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 industries such as:

• Healthcare
• Enterprise Software
• Insurance
• Financial Services
• Manufacturing
• Agriculture
• Cybersecurity
• Construction
• 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.

Canada AI Adoption: The 12% to 60% Opportunity

The shift toward enterprise AI is not accidental.

Canada already has strong industries in:

• Healthcare
• Insurance
• Energy
• Banking
• Public Infrastructure

What Canada lacks is widespread AI adoption.

Statistics Canada reports that only 12% of Canadian businesses used AI to produce goods or services between mid-2024 and mid-2025.

Recognizing this opportunity, 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.

This 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.

Organizations including:

• Scale AI
• Mila
• NEXT Canada
• MaRS

have spent years moving the conversation beyond research papers and demonstrations toward real-world deployment.

The ALL IN conference reflects that same philosophy.

Instead of filling conference halls with technology vendors pitching other technology vendors, the event intentionally prioritizes:

• Enterprise buyers
• Industry operators
• Business leaders
• Public institutions

The goal is not simply 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
• 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.

Examples include:

• 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 simply describe what they need in plain language.

AgenQ then completes the task inside the organization’s existing software while respecting:

• Business rules
• User permissions
• 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.
Stronger enterprise workflows will accelerate Canada 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 companies that make AI:

• Usable
• Trusted
• Deployable
• 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.

Frequently Asked Questions

What percentage of Canadian businesses currently use AI?

According to Statistics Canada, 12% of Canadian businesses used AI to produce goods or services between mid-2024 and mid-2025.

Canada’s national AI strategy, AI for All, aims to increase that number to 60% by 2034.

What is the ALL IN 2026 Top 100 AI Startups list?

The ALL IN 2026 Top 100 is an annual selection of Canada’s most promising AI startups.

Announced on July 21, 2026, the list was organized jointly by Scale AI and Mila, Canada’s largest AI and technology event.

More than 330 startups applied, with 100 companies selected.

It is a selection—not a ranking.

What is an execution layer for enterprise software?

An execution layer is software that sits inside an organization’s existing enterprise applications and performs work on behalf of users.

Instead of clicking through multiple screens manually, users simply describe what they want in plain language.

The execution layer then completes the task while following the organization’s business rules, permissions, approvals, and compliance requirements.

How is an execution layer different from an AI copilot or a digital adoption platform?

An AI copilot primarily answers questions.

A digital adoption platform guides users by showing them where to click.

In both cases, the user still performs the work.

An execution layer, by contrast, performs the task itself inside the enterprise software while respecting business-specific rules, approvals, and governance.

The future of Canada AI Adoption depends on solving real operational problems rather than building larger AI models.

Sources

• AI for All: Canada’s National Artificial Intelligence Strategy — Government of Canada (June 4, 2026)
• Statistics Canada — Business AI Adoption Data (mid-2024 to mid-2025)
• ALL IN 2026 Top 100 AI Startups Announcement (July 21, 2026)