How Agentic AI Could Reshape the Canadian Workforce

A new KPMG survey of Canadian business leaders suggests agentic AI—systems that can make decisions and act autonomously—will significantly reshape the workforce. Executives see both productivity gains and serious disruption to roles, skills, and management practices. This article distills what agentic AI is, how leaders expect it to affect Canadian jobs, and practical steps organizations and workers can take now. While the details will vary by industry, the direction of travel is clear: agentic AI is moving from pilot projects to the core of business operations.

Understanding Agentic AI: More Than Just Automation

Agentic AI refers to AI systems that can take actions toward a goal with a high degree of autonomy. Unlike traditional software that simply follows a predefined script, agentic systems can plan, make decisions, interact with multiple tools, and adapt based on feedback.

For Canadian employers, this is a major shift. Instead of AI merely assisting workers with narrow tasks, agentic systems can manage end-to-end workflows, trigger actions across departments, and operate continuously with minimal human intervention.

• Traditional AI: Classifies data, generates content, or makes predictions when prompted.
• Agentic AI: Sets sub-goals, calls tools and APIs, monitors outcomes, and adjusts its own behaviour.

That autonomy is what has Canadian executives—according to the KPMG survey—expecting significant workforce impact, from role redesign to reskilling and new governance models.

What Canadian Leaders Expect From Agentic AI

While the KPMG survey details vary by sector, a few broad themes are emerging from Canadian boardrooms and C-suites as they look at agentic AI:

• Productivity and efficiency gains across white-collar and operational roles.
• Redesign of jobs as routine tasks are handed off to AI agents.
• New roles and functions focused on AI oversight, ethics, and orchestration.
• Shifts in required skills toward data literacy, problem framing, and AI collaboration.

In practice, leaders are less focused on replacing entire jobs overnight and more on restructuring workflows so that AI agents handle repetitive, rules-based work while people tackle exceptions, judgment calls, and relationship management.

How Agentic AI Could Reshape Canadian Jobs

The KPMG findings align with a wider pattern: agentic AI is expected to both automate and augment work. The impact will depend heavily on the type of tasks inside each role.

Roles Likely to Be Heavily Augmented

Many white-collar jobs in Canada contain a large share of repeatable, digital tasks. Agentic AI is well-suited to these, including:

• Finance and accounting: AI agents reconciling accounts, flagging anomalies, and preparing draft reports.
• Customer support: Autonomous agents triaging tickets, drafting replies, and escalating only complex issues.
• HR operations: Agents managing routine employee queries, onboarding steps, and document workflows.
• Supply chain and logistics: Systems reordering inventory, updating shipping information, and optimizing routes.

In these areas, the expectation is not that humans disappear, but that fewer people will be needed for transactional work and more will be redeployed to analysis, strategy, and stakeholder engagement.

Roles Facing Deeper Disruption

Jobs built primarily around rule-based, digital tasks—especially those already highly standardized—may face stronger pressure:

• Back-office processing roles
• Entry-level data entry and reporting jobs
• Routine compliance checking and document review

Canadian leaders surveyed are increasingly factoring this into hiring plans, often freezing headcount growth in such areas while investing in AI pilots and training.

Implications for the Canadian Labour Market

The survey results reinforce a core tension: agentic AI can boost Canada’s productivity—a longstanding national challenge—while also introducing short- to medium-term worker displacement in certain segments.

Potential Benefits

• Higher output per worker: Firms may become more competitive globally, especially in services.
• New AI-adjacent roles: From AI operations and governance to prompt engineering and data stewardship.
• Improved service quality: Faster response times, fewer errors, and more personalized experiences.

Key Risks

• Uneven impact across regions: Office-heavy urban centres may feel changes first, leaving pockets of local disruption.
• Skills gaps: Workers without access to upskilling risk being left behind.
• Trust and fairness concerns: If AI decisions appear opaque or biased, employee confidence and public perception can erode.

For policy makers and business leaders in Canada, the challenge is to maximize productivity gains while cushioning and managing the transition for affected workers.

Where Agentic AI Fits in Canadian Industries

Based on the types of work involved, some sectors are better positioned for early adoption of agentic AI, while others may focus more on augmentation than automation.

The KPMG survey highlights a broad consensus: nearly every sector anticipates some form of agentic AI integration, but pace and depth will depend on regulation, data readiness, and leadership appetite for change.

How Organizations Can Prepare Now

Canadian executives surveyed are moving from experimentation toward structured programs. Organizations that prepare proactively tend to focus on a few practical steps.

1. Map Work, Not Just Jobs

Instead of asking which jobs to automate, leading employers break roles into tasks and workflows. This lets them identify where agentic AI can plug in without destabilizing operations.

2. Establish Clear Governance

Agentic AI’s autonomy raises governance stakes. Firms are creating cross-functional AI councils to define:

• Acceptable use cases and risk thresholds
• Approval processes for new AI agents
• Monitoring, audit, and incident response procedures

3. Invest in Data Foundations

Autonomous agents are only as effective as the data and systems they can access. Many Canadian organizations are using this moment to modernize data infrastructure, standardize APIs, and improve data quality.

Practical Steps for Leaders Implementing Agentic AI

To move from intention to impact, organizations can follow an ordered implementation path that balances experimentation with control.

1. Identify candidate workflows: Look for repeatable, rules-based processes with measurable outcomes.
2. Run controlled pilots: Start in low-risk areas and measure productivity, quality, and employee sentiment.
3. Define human-in-the-loop points: Decide where people must approve, review, or override AI actions.
4. Create new performance metrics: Track not just cost savings but also error rates, satisfaction, and fairness.
5. Scale gradually: Extend to adjacent workflows only after controls, training, and governance prove effective.

What This Means for Canadian Workers

For individual employees, the KPMG survey signals that staying still is the highest-risk option. Agentic AI is likely to become a standard tool in many roles, not a niche technology.

Skills That Become More Valuable

• Problem framing: Translating business challenges into clear goals for AI agents.
• Data literacy: Interpreting AI outputs, understanding limitations, and challenging results.
• Collaboration and communication: Working across functions to design and refine AI-enabled processes.
• Ethical awareness: Spotting potential harms, bias, and unintended consequences.

Workers who can combine domain expertise with these meta-skills will be well-positioned as roles evolve.

How Employees Can Proactively Adapt

Adapting to agentic AI does not require every worker to become a data scientist. It does, however, reward those who lean into change rather than resist it by default.

• Experiment with AI tools: Use approved systems at work to understand their strengths and gaps.
• Ask to join AI projects: Volunteer as a subject-matter expert for pilot initiatives.
• Seek targeted learning: Focus on short courses in analytics, automation, or AI fundamentals.
• Document your unique value: Identify tasks where your judgment, relationships, or creativity are central.

These steps not only build resilience but also make workers more visible as contributors to their organization’s AI strategy.

Building a Responsible Canadian Approach to Agentic AI

Canadian business culture and regulatory trends typically favour prudence and consultation. The KPMG survey suggests this will carry over into how agentic AI is adopted.

That likely means more emphasis on:

• Transparency with employees about where and how AI is being deployed.
• Consultation with unions and worker councils where relevant, especially in public and quasi-public sectors.
• Alignment with emerging AI regulation at federal and provincial levels, including privacy and human rights requirements.

A deliberate, socially aware approach can help Canada capture the upside of agentic AI while reducing backlash and mistrust.

Final Thoughts

The KPMG survey underscores a pivotal moment for the Canadian workforce. Agentic AI is no longer a distant prospect; it is becoming a strategic priority for business leaders who expect it to reshape jobs, skills, and organizational structures. The technology’s autonomy amplifies both its promise and its risks, making governance, training, and transparent communication critical.

For employers, the task is to integrate agentic AI in ways that boost productivity and competitiveness while creating new opportunities for people. For workers, the imperative is to build adaptable, AI-fluent careers. How Canada navigates this transition over the next few years will shape not just individual companies, but the country’s broader economic trajectory.

Editorial note: This article is an independent analysis based on publicly reported themes from a KPMG survey about Canadian business leaders’ expectations for agentic AI. For further context, visit the original source at talentcanada.ca.