Where’s the Pay? AI in HR Must Include Rewards

We’ve made real progress in applying AI across HR. Recruitment is faster, learning is more tailored, and workforce planning is more predictive. But one area seems to be continually left behind: rewards.

While pay remains one of the most powerful drivers of behaviour and retention, it is still managed through disconnected systems and spreadsheets. And it’s barely featured in the conversation on AI in HR. That needs to change.

Compensation decisions shape what organisations value, how they signal priorities, and how they attract and keep their best people. If other HR processes are being redesigned using intelligent tools, rewards must be included.

Start with What AI Can Readily Improve

Many compensation tasks are still manual and time-consuming. AI is well-suited to take over this type of work, saving time and reducing error. Here are five areas where it is relatively easy to make an impact with AI in rewards:

• Job evaluation: AI can write consistent job descriptions, which includes standardized language to ensure they are accurate, assess content against internal frameworks and market data, and assign levels consistently.

• Offer preparation: AI tools can generate tailored and compliant job offers, taking into account salary ranges, internal equity, and candidate history.

• Benchmarking: Algorithms can match internal roles to market benchmarks and update recommendations as market data shifts.

• Pay documentation: Systems can automatically generate personalised pay or bonus letters at scale.

• Trigger-based actions: Tools can identify when compensation adjustments are needed based on employee changes such as promotion, transfer, or location shift.

These are precisely the kinds of tasks where AI can add efficiency without requiring complex design decisions.

Bring Core Rewards Work Back In-House

Some tasks happen less frequently, but carry significant strategic impact. These have often been outsourced or deferred due to resource constraints. AI can change that.

• Salary structure design: Tools can identify clusters of jobs, suggest grade frameworks, and test internal compression risks.

• Merit matrix modelling: Data-driven simulations help identify how to allocate increases within budget constraints while maintaining fairness.

• Range calibration: Algorithms flag situations where pay practices are likely to create compression or misalignment.

• Cost forecasting: Compensation planners can model multiple pay strategies and understand the impact of each on overall costs.

• Localisation: AI systems help adjust compensation designs to account for geography-specific tax, regulatory, or cost-of-living requirements.

These capabilities allow reward teams to regain control over core design work while reducing reliance on external parties.

AI in Planning: A Strategic Advantage

AI is most valuable when it supports decisions that align people strategy with business outcomes. Compensation planning is one of the best examples.

For example, Visier’s platform allows companies to simulate multiple compensation strategies and see their impact on key metrics like retention risk, performance distribution, and pay equity. These tools make it possible to link pay planning with business results, instead of running it as a separate, budget-driven cycle.

Using AI in this way helps organisations prioritise where to invest, test different pay-for-performance models, and manage risk across a complex workforce.

Use Analytics to Test Assumptions

Compensation analytics powered by AI can help organisations move beyond averages and reports to get real answers to important questions. Examples include:

• Are our increases meaningfully differentiated based on performance?

• Where are our bonus distributions reinforcing, or undermining, intended behaviours?

• Are we confident that pay is equitable across gender, role, and location?

• How does pay mix affect engagement or turnover in our most critical jobs?

• Which parts of our rewards system are driving outcomes, and which are not?

This is how analytics should be used—not as reporting on dashboards, but as a design tool.

Tools to Explore

Here are some platforms already helping organisations modernise rewards through AI and analytics:

• Visier: Enables smart compensation planning aligned with performance, equity, and retention priorities, with scenario-based modelling.

• Tallect: An AI-powered design platform that supports compensation structuring, equity diagnostics, and incentive plan modelling.

• Syndio: Focused on workplace equity, enabling real-time analysis of pay gaps and risk exposure.

• Salary.com: A comprehensive compensation management system offering data, planning tools, and benchmarking that help organisations “get pay right.”

• Compport: Offers automation across salary reviews, bonus cycles, LTI plans, and analytics, all built with AI at the core.

• Payfactors by Payscale: Provides AI-enhanced compensation data and tools to maintain competitive and equitable pay structures.

• CompTool and Squirrel: Together offer automated compensation cycle management, with dynamic dashboards and modelling through Squirrel365’s analytics platform.

Let’s Not Miss the Moment

The transformation of HR through AI is already underway, but we can’t leave pay behind. Compensation is where strategy meets execution. In order for our people systems to reflect business goals and workforce realities, rewards must be part of the design.

AI can help reward teams become more agile, more transparent, and more aligned with outcomes that matter. This is the moment to bring pay into the conversation, and to give it the tools it needs to support the rest of the talent management efforts.