The promise of artificial intelligence in HR technology has caught the attention of many leadership teams — and compensation management is one area where interest is growing fast.
With rising expectations around pay transparency, regulatory compliance, and pay equity, many HR departments are wondering whether AI could help manage these increasing demands more efficiently.
At the same time, it’s important to take a realistic view of what current tools can deliver — and, more importantly, what it takes to implement AI systems that are truly effective and reliable in compensation work. While there is clear potential, significant investment and expertise are still required to move beyond basic applications.
Why Salary Data Remains a Limiting Factor
The usefulness of AI in compensation depends first and foremost on the quality of the data it can access. Unlike some other areas of HR, compensation data is largely private. Salary details reside within companies or specialist benchmarking services and are subject to confidentiality, compliance, and commercial sensitivities. Publicly available data — such as figures scraped from job boards or self-reported on crowdsourced sites — is often incomplete, inconsistent across markets, or skewed toward particular industries and seniority levels.
General-purpose AI models trained on open data (such as ChatGPT or similar tools) do not have access to the kind of verified salary data used in professional compensation management. Attempting to generate salary benchmarks based solely on such models carries a high risk of inaccuracy — which could lead to poor decisions on pay levels, legal exposure, or damage to employee trust.
Building Useful Tools Requires Expertise and Investment
There is genuine potential for AI to support compensation work — but delivering useful tools is not a matter of simply “adding AI” to existing systems. It is important to be clear what is meant here. Having access to an AI chatbot (such as one embedded in a broader HR system) is very different from building a bespoke AI application that has been trained and configured using a company’s internal data, role structure, pay philosophy, and specific business requirements.
Many of the most promising use cases require exactly this kind of customisation. For example:
- Automatically generating salary ranges for new or evolving roles
- Identifying pay disparities or inconsistencies across levels, regions, or business units
- Simulating the financial and structural impacts of salary changes
- Monitoring compliance with pay transparency regulations
To achieve this, AI models must be tailored to a company’s compensation framework, legal environment, and data structures. Developing such solutions involves technical expertise, time, and a significant financial investment — both to build the system and to ensure it can be trusted and maintained in practice. The costs of doing this — and of ensuring results remain auditable and aligned with changing pay practices — can be substantial.
Human Oversight is Built Into the Process
Even when custom AI tools are implemented, compensation work remains a sensitive and business-critical area. Outputs need to be reviewed and contextualised by experienced professionals — not just because of current limitations in the technology, but because compensation decisions often involve balancing objectives that are not easily reduced to data alone.
It is this complexity — combined with the need for regulatory compliance and employee trust — that makes careful human oversight an integral part of any AI-supported process.
Areas Where AI is Already Proving Useful
While the more advanced applications require considerable investment, there are areas where AI can already deliver value in a more straightforward way:
- Data cleaning and preparation, especially when combining multiple survey sources
- Drafting job descriptions or compensation documentation with natural language models
- Flagging anomalies or outliers in pay data for further review
- Supporting pay equity reviews by highlighting trends and patterns
These practical applications can help free up HR and compensation teams to focus on higher-value analysis and decision-making. Still, while some of these use cases may be served with ready-to-use models on a subscription or even free basis, others will require bespoke implementation that will come at significant cost. Using online models also opens up a plethora of data confidentiality questions that should not be taken lightly.
A Measured Path Forward
AI is unlikely to transform compensation management overnight — not because of a lack of potential, but because building models that genuinely reflect the complexity of compensation requires considerable customisation and resources. For many companies, the path forward will be gradual: using AI first to support data processing and review, while investing in more advanced tools where business needs and resources align.
In the coming years, AI will no doubt play a larger role in compensation management. But as with many areas of HR, its value will depend not just on the technology itself, but on the care and expertise brought to its implementation.
How Vencon Research Can Support Your Compensation Work
At Vencon Research, we work with consulting firms to ensure their compensation decisions are grounded in accurate, relevant data. Whether you're exploring how AI might support your internal processes or simply need reliable benchmarking to build on, we can help you get the foundations right.