Canadian business leaders are planning a workforce where humans and AI agents work as peers, but they're struggling to prove it actually works. A new KPMG Canada survey of 306 executives reveals that 77% of organizations are already using AI agents for tasks like knowledge sharing and research, while 66% are moving toward fully integrated human-AI teams. Yet only 3% have achieved measurable returns on their AI investments, compared to 8% globally.

The disconnect points to a fundamental problem: companies are deploying AI agents faster than they're preparing their workforce to manage them effectively. As AI takes on research, coordination, and analysis work, organizations are discovering that the real challenge isn't building AI systems,it's redesigning how people work alongside them.

Why Are Companies Changing How They Hire?

The shift is already visible in hiring practices. More than half of Canadian survey respondents (59%) say AI agents have changed how they hire entry-level workers, while 63% report the same impact on experienced talent recruitment. Organizations are no longer prioritizing the technical skills that AI can automate. Instead, they're emphasizing creative thinking (46%), problem-solving (44%), and adaptability (43%) when bringing on new employees.

This reflects a broader recognition among business leaders: 65% now say social and interpersonal capabilities matter more than technical expertise. The logic is straightforward. If AI agents handle routine analysis and coordination, humans need to focus on judgment, decision-making, and accountability. That requires a different kind of worker.

"As organizations move quickly to scale AI agents, we're seeing a generational shift in how work gets done. Business leaders are starting to design roles, teams and workflows on the assumption that humans and agents will work together, with agents taking on work such as research and coordination, and people focusing on judgement, decision-making and accountability," said Stephanie Terrill, Canadian Managing Partner of Digital and Transformation at KPMG Canada.

Stephanie Terrill, Canadian Managing Partner of Digital and Transformation, KPMG Canada

What's Happening to Performance Reviews and Promotions?

The changes extend beyond hiring. Organizations expect AI agents to reshape how employees are evaluated and advanced. According to the KPMG survey, anticipated changes include the following:

• Performance Review Criteria: 39% of respondents expect AI collaboration competencies to be built into performance reviews and role requirements
• Skill Emphasis Shift: 39% anticipate more emphasis on human capabilities like critical thinking and contextual awareness over tasks now handled by AI
• Promotion Standards: 36% predict promotion criteria will be redefined to prioritize AI literacy and effective agent delegation

Within two to three years, business leaders predict that AI agents will either lead project management for teams (39%) or work alongside humans as peers to complete tasks (31%). This represents a fundamental restructuring of how organizations define success and advancement.

Why Is Canadian Adoption Lagging Behind Global Peers?

Despite the optimism, Canadian organizations are struggling more than their global counterparts to extract value from AI investments. While 70% of Canadian organizations say AI is delivering meaningful business value, only 3% have achieved measurable returns on their AI investments. Globally, 8% have successfully realized a return on their investments.

The primary obstacle is a workforce skills gap. But there's another critical factor: employee resistance. In Canada, 31% of employees show resistance to AI technology, compared to 16% globally. Over half of those resistant employees (51%) cite trust and ethical concerns, while nearly 40% worry about job security or lack confidence in their AI skills.

"When organizations are looking at upskilling their workforce, they must have a clear picture of how their people can use AI agents in ways that deliver meaningful impact on the business. A skilled employee can build an agent to automate tasks and get work done faster, but if it's not being used on work that produces results, companies won't see the returns they're looking for," explained Lewis Curley, a Partner in the People and Change practice at KPMG Canada.

Lewis Curley, Partner in the People and Change practice, KPMG Canada

How to Build an AI-Ready Workforce

Experts across multiple organizations agree that closing the ROI gap requires more than training employees to use AI tools. It demands a fundamental shift in how organizations think about work design and management capability. Leading organizations are taking these concrete steps:

• Establish Clear Protocols: Define when and how to use AI agents, with role-specific guidelines for effective collaboration and delegation
• Create Sandbox Environments: Provide safe spaces where employees can practice interacting with AI tools before deploying them on critical work
• Develop Management Skills: Train all employees to act as managers of AI agents, responsible for allocating work, assessing quality, and providing feedback
• Align Training to Business Outcomes: Connect upskilling programs directly to how AI agents will deliver measurable impact on specific business processes

The management capability piece is particularly important. In the new AI-augmented workplace, every employee becomes a manager of sorts, responsible for overseeing and delegating work to agents. This requires a different skill set than traditional technical training provides.

Organizations are also discovering that workforce readiness varies by role and seniority. A data analyst needs different AI training than a procurement officer or IT specialist. Tailored learning pathways that match training intensity to actual job requirements produce better adoption and faster ROI than one-size-fits-all programs.

What Happens If Companies Get This Wrong?

The stakes are high. Research from Gartner suggests that 40% of agentic AI projects will be canceled by the end of next year, primarily because organizations deployed agents "without a clear strategy" and lacked knowledge about the complexity involved. Many projects were launched due to hype or fear of being left behind, only to fail during the pilot phase.

For Canadian companies, the challenge is particularly acute. The combination of a larger skills gap, higher employee resistance, and lower ROI realization suggests that many organizations are moving too fast without adequate preparation. The lesson is clear: AI adoption isn't a technology problem. It's an organizational and people problem.

Companies that succeed will be those that treat AI agents as more than productivity tools. They'll redesign workflows, redefine roles, and invest as heavily in developing workforce management capabilities as they do in AI literacy. For Canadian business leaders, the next two years will determine whether AI becomes a source of competitive advantage or another expensive technology that failed to deliver on its promise.