AI in compensation: Why momentum doesn’t yet equal transformation 

Canadian compensation professionals often assume they are working a year or more behind their U.S. counterparts on artificial intelligence. However, recent WTW research suggests a different story, and perhaps a more useful one.

In a pulse survey of 96 compensation and total rewards professionals across Canadian organizations, 82% reported that they are already using or actively exploring AI in their compensation work, while 68% said their use has increased in the past year. More notably, 77% expect AI adoption to expand further in the next 12 months. By any reasonable measure, momentum is broad-based and accelerating.

Viewed alongside large-scale international total rewards surveys, however, the Canadian picture looks less like a laggard market and more like the global mainstream. Those broader studies suggest that most organizations worldwide remain in the earliest stages of AI maturity in total rewards, with relatively few using AI in deeply integrated or transformational ways. Canadian organizations are not behind the world; they appear broadly aligned with where most organizations currently stand.

The more meaningful distinction is not Canada versus the United States. It’s the difference between the broad mainstream where most organizations remain, and a smaller leading edge that has moved beyond experimentation into deliberate, governed, purpose-built use of AI in compensation. That leading edge is defined less by geography than by how organizations have chosen to build their capabilities.

Momentum is real, depth is still shallow

The most revealing finding from WTW’s Canadian survey is not how many organizations are experimenting with AI, but how few have fundamentally changed the way compensation work is performed. Not a single respondent described AI as embedded across compensation workflows. Only one organization reported using AI in a coordinated, systematic way across multiple compensation activities.

Meanwhile, the operational foundation beneath much of today’s AI activity remains largely unchanged. Nearly three-quarters of respondents said their compensation data preparation remains completely manual. AI is therefore being layered onto manual processes, such as drafting communications, summarizing information or accelerating individual tasks rather than fundamentally redesigning compensation workflows.

Most organizations have reached the point where AI is part of the conversation and increasingly part of the workflow. Very few have reached the point where AI is changing how compensation work is actually performed.

The real constraint is not reluctance

If leadership hesitation were the primary barrier to deeper AI adoption, the survey would tell a very different story. Instead, leadership hesitation ranked last among reported barriers, cited by only 6% of respondents. Governance concerns and data quality challenges were identified far more frequently.  

That distinction matters because it reframes the discussion from willingness to readiness. Organizations are not holding back because they doubt AI’s potential. They are holding back because they lack the governance frameworks, trusted data and operational foundations needed to move beyond low-risk productivity gains into higher-value applications such as benchmarking automation, pay equity analytics, incentive modeling and AI-supported decision making.  

In that sense, governance functions less like a brake than the mechanism that makes speed possible. The organizations making the greatest progress with AI are not those with the least governance. They are the ones that have built enough governance to trust AI at scale.  

Deliberate design, not ad hoc experimentation

Among Canadian survey respondents, only one organization described AI as operating systematically across multiple compensation activities. Its small compensation team uses AI to support benchmarking, job evaluation, drafting and analysis for a workforce many times larger than would traditionally be expected. Asked to describe the difference, the team said it had moved “from experimentation to deliberate design.”  

That phrase captures the distinction between the mainstream and the leading edge more effectively than any maturity statistic. The difference is not access to technology. Most organizations today have access to broadly similar tools. The difference is whether AI has been intentionally designed into governed workflows, or whether it has simply accumulated through isolated experimentation across a team.  

WTW’s own 2026 Tech Industry Pay Actions Survey provides a useful view into where more advanced organizations are directing AI investment. Among U.S. technology companies, AI use is more focused on applications such as automated benchmarking, AI-enhanced decision support, and job architecture or skills-based pay integration. These use-cases point to where value may concentrate as organizations mature, but they also require the data infrastructure, job architecture and governance that many organizations are still building.  

Where AI is creating value today

For most organizations, the most accessible and effective current use case is drafting and documentation. In the Canadian survey, drafting and documentation was the most widely deployed AI application in compensation and produced the strongest satisfaction signal. The reason is straightforward: It delivers immediate value, requires limited data integration and allows human review to remain central.  

That does not make drafting unimportant. In fact, it may be the right entry point for many organizations. It allows compensation teams to build familiarity, develop prompting skills, improve consistency and create early wins without introducing AI directly into high-stakes pay decisions.

But drafting is not the destination. The higher-return opportunities, such as benchmarking automation, job matching, pay equity analytics and decision support, depend on stronger foundations. They require clean and integrated data, coherent job architecture, clear governance and explainable outputs that compensation professionals can validate and defend.

The implication for compensation leaders

When adoption statistics show this much momentum, the instinct is often to conclude that organizations simply need to more quickly deploy more AI tools. The survey points to a different conclusion.

The organizations most likely to distinguish themselves in the next several years are unlikely to be those that adopted AI first. They are more likely to be those that use this period of experimentation to strengthen the data, governance and operating foundations that meaningful AI adoption requires.

That may be a less exciting message than declaring that AI is already transforming compensation, but it is likely the more useful one. The technology is increasingly accessible. What separates the mainstream from the leading edge is not access. It is design.

For compensation leaders, the next competitive advantage will come from building the organizational capability to use AI confidently, responsibly and at scale.

Compensation work will change long before compensation functions disappear. Organizations that create the most value will treat AI as something to be deliberately designed into compensation workflows, not simply widely deployed. 

WTW would like to acknowledge the contributions of Krista McBeth, Vice President of People Operations, ServiceTitan, to this article.

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