Canada’s Competitiveness Depends on Building AI‑First Business Models

In Canada, business leaders are betting big on AI. According to a recent IBM study, an overwhelming 84% feel confident about their company’s future, largely thanks to their AI ambitions. But there’s a catch. 

While optimism is high, execution is proving more complex. The pace of business has fundamentally changed. In the latest survey, 72% of Canadian executives warn that companies unable to operate in real-time will fall behind. This creates immense pressure to keep up. 

Why Canada Needs AI-First Business Models

That pressure is especially acute in Canada. For more than a decade, Canada’s productivity growth has trailed peer economies. Incremental efficiency gains are no longer enough. As global competitors redesign entire enterprises around AI, Canadian organizations that treat AI as a bolt‑on tool risk entrenching the very structures that have held productivity back.

The problem isn’t the technology itself. It’s the approach. 

From AI Adoption to Business Model Reinvention

A clear divide is emerging. Some companies are simply plugging AI into their old ways of working, hoping for a quick productivity bump. The true leaders, however, are rebuilding their playbook from the ground up. They’re focused on creating a genuine partnership between their people and their new digital tools, then reinvesting those efficiency gains into real, sustainable growth rather than short-term cost savings



Canada’s challenge isn’t ambition or access to innovation. It’s structural. Fragmented data environments, legacy operating models, persistent skills gaps, risk‑averse governance, and a tendency to pilot rather than scale have slowed impact. AI exposes these constraints, and without addressing them holistically, Canada will struggle to compete in a global economy increasingly defined by speed, scale, and adaptability.

By 2030, when 75% of Canadian executives expect AI to drive revenue, this approach will define who thrives and who gets left behind.

Three Choices That Define AI-First Business Models

The difference comes down to three fundamental choices that separate the true AI-first companies from everyone else: reimagining core operations, empowering the workforce, and redesigning the customer experience. These aren’t technology initiatives; they are business model decisions.

Here are examples of companies that are leading the way in each area.

Transforming Core Operations

By embedding generative and agentic AI into its application development lifecycle, one large Canadian financial institution is empowering its developers to move beyond routine tasks and focus on innovation. Solidifying its ambition with the recent launch of a dedicated AI Group, the financial institution is embedding AI directly into its core technology engine.

Instead of just automating surface-level tasks, the organization is using AI to streamline the maintenance and modernization of core applications, which accelerates code updates, test case generation, and incident resolution. Automating these processes dramatically shortens the “idea-to-market” cycle for new client services. As a result, this operational shift allows the organization to innovate more securely and respond to market demands with greater speed and agility.

Empowering the Augmented Workforce

A Canadian manufacturer in the packaging and hygiene sector has implemented AI to augment its workforce and enhance efficiency. By automating repetitive administrative tasks, the company has enabled its employees to shift their focus to higher-value activities, improving productivity and client service.

To retain invaluable institutional knowledge, the organization developed a conversational virtual agent that acts as a centralized repository for employee expertise, ensuring critical information is preserved and accessible for real-time, data-informed decision-making, even as the workforce evolves.

Canada’s Productivity Future Depends on Structural Change

These examples point to a common lesson: AI delivers meaningful economic value only when organizations are willing to redesign how work actually gets done, from operating models and governance to skills, incentives, and decision‑making.

For Canada, this moment is decisive. Competitiveness will not be determined by how many AI tools organizations adopt, but by whether they are willing to rethink the structures that shape performance. AI‑first business models that do not bolt‑on technologies will determine whether Canada can close its productivity gap and compete globally in the decade ahead.