Canadian businesses are spending on artificial intelligence, but they're not getting the same returns as their U.S. counterparts, according to new research that reveals a critical gap between investment and execution. While 86 percent of Canadian and U.S. organizations have partially or fully integrated AI into operations, only 43 percent of Canadian respondents say their AI investments have exceeded expectations, compared to 57 percent of U.S. firms.

The RSM Middle Market AI Survey 2026, which polled over 1,000 senior business leaders across both countries, found that Canadian companies are taking a more cautious approach to AI adoption. Just 69 percent of Canadian organizations report partial or full AI integration, trailing the 89 percent of U.S. firms at similar stages. This gap suggests Canadian enterprises are earlier in their AI journey and may be missing opportunities to reshape how they operate.

What's Actually Blocking Canadian AI Success?

The research points to a clear pattern: Canadian organizations have the will to invest in AI, but they lack the operational foundation to scale it effectively. When asked about barriers to scaling AI initiatives, organizations identified several interconnected challenges that go beyond simply choosing the right tools or models.

• Data Quality Issues: Among organizations with moderate or limited pilot success, 53 percent cited data quality problems as the leading barrier to scaling AI across the enterprise.
• Integration Challenges: Forty-seven percent reported difficulty connecting AI systems to existing business processes and legacy infrastructure.
• Unclear ROI Measurement: One-third of organizations struggling to scale AI said they couldn't clearly measure whether their investments were delivering business value.
• Security and Compliance Concerns: Thirty-three percent cited security and privacy risks as obstacles to moving AI from pilots into production environments.

Across all respondents, data quality and availability emerged as the single biggest inhibitor to AI deployment at 34 percent, followed by security and privacy concerns at 30 percent, legacy systems integration at 28 percent, and talent and skills gaps also at 28 percent.

The Leadership-Readiness Disconnect: Why Ambition Outpaces Execution?

One of the survey's most striking findings reveals a growing misalignment between what leaders want and what their organizations can actually deliver. Eighty-five percent of respondents agreed that executive leadership is more enthusiastic about AI than employees, while 88 percent believe their workforce will look fundamentally different within two to three years because of AI. Yet only 67 percent of organizations apply AI governance controls before pilots or production stages, suggesting many are moving forward without adequate safeguards.

"Canadian businesses recognize that AI has quickly become a non-negotiable core business capability. But the data shows many organizations are still working through the foundational issues that determine whether their organization is set up for AI to create real value: data quality, governance, workforce readiness and the ability to measure ROI," stated Sonya King, management consulting director at RSM Canada.

Sonya King, Management Consulting Director at RSM Canada

This gap between leadership enthusiasm and employee readiness creates a real risk. When executives push AI adoption without ensuring teams have the skills, data infrastructure, and clear processes in place, pilots succeed but scaling fails. The result is wasted investment and organizational frustration.

How to Build a Foundation for Scalable AI Adoption

• Establish Data Governance First: Before deploying AI models, audit data quality, create standards for data collection and storage, and ensure teams understand what data is available and how to access it reliably.
• Define Clear Business Outcomes: Move beyond asking "Can we use AI here?" to "What specific business problem does this solve, and how will we measure success?" This clarity helps teams prioritize use cases that deliver measurable ROI.
• Invest in Workforce Readiness: Rather than mandatory training, build adoption through power users and peer learning, allowing teams to pull AI tools into their workflows at their own pace.
• Implement Governance Early: Organizations that apply governance controls before pilots move faster to production and manage risk more effectively as AI scales across the enterprise.
• Bridge the Business-Technology Gap: Ensure business leaders and technical teams communicate clearly about priorities, processes, and constraints, because most AI projects fail not because the model is weak, but because the business context is unclear.

The survey also examined AI adoption within tax functions for the first time, finding that 83 percent of respondents say their tax function currently uses AI tools, with 45 percent pursuing AI-enabled tax planning and optimization use cases. This suggests that even within specialized functions, organizations are experimenting with AI, but the challenge remains moving from isolated pilots to enterprise-wide transformation.

The Spending Gap: Why Canadian Firms Are Investing Less Aggressively?

Investment levels tell part of the story. Sixty-two percent of U.S. respondents said their firms would invest $1 million or more in AI in the current fiscal year, compared to just 41 percent of Canadian respondents. This suggests Canadian organizations are taking a more measured, incremental approach to AI spending, which can be prudent, but it also means they may be slower to build the infrastructure needed for transformation.

"Canadian companies are taking a pragmatic approach to AI, and that can be a strength if it is paired with clear strategy and strong governance. The risk is that a focus on incremental gains alone may leave organizations behind as competitors begin using AI to reshape entire functions and business models," King added.

Sonya King, Management Consulting Director at RSM Canada

The federal government's recently announced "AI for All" strategy aims to help Canadian businesses move from experimentation to scalable, responsible execution. For organizations to succeed, they'll need to address the foundational gaps the survey identified: data quality, governance, workforce alignment, and clear measurement of business impact.

The opportunity for Canadian enterprises is significant, but the path forward requires more than buying AI licenses or deploying chatbots. It requires disciplined, enterprise-wide transformation that starts with process clarity, data readiness, and alignment between leadership ambition and organizational capability. Organizations that invest in these foundations now will be positioned to scale AI effectively and compete with their U.S. counterparts in the years ahead.