AI and pay: Enriching your compensation strategy with artificial intelligence

Since the release of ChatGPT at the end of 2022, every day brings an announcement of new uses for AI. You could argue AI has reached the Peak of Inflated Expectations in the Gartner Hype Cycle. This is where expectations around emerging technology reach their highest point with lots of buzz in the media.

However, people may have not fully understood or tested the actual capabilities and limitations of the technology yet. As people get excited, and then possibly disappointed, views on AI will change rapidly. Recently, over one thousand technology founders and scientists called for a halt to new AI development. We will see if this slows the deluge of new uses for AI. Most of the new applications for AI are in the world of marketing, media, and administrative occupation. But what about the future of compensation?

Human Resources is not immune to the wave of AI excitement. The use of chatbots in recruiting has been happening for the past five years, but with the advances in Large Language Models (LLMs) from organizations like OpenAI, the possibility of screening and filtering resumes and online profiles has exploded. Even the performance management function is becoming a place where AI applications will help managers write annual reviews.

The impact of AI on compensation

One HR function that gets less attention in the AI space is compensation. As a compensation practitioner, I spend time thinking about how to make my teams more effective. Five years ago, I spoke at a future of work conference about the uses of AI for HR. At the time, the most realistic application of AI for compensation was a chatbot to answer frequent questions. Today, the advances in LLMs open many more possibilities for compensation.

Job descriptions

No one likes to write job descriptions. The good news is you never have to write one again. There are AI tools to do this for you. Type in your job title and ask AI to write the job description. This is a pragmatic place to start learning about the capabilities of AI in compensation.

Job matching

I’ve worked on a lot of acquisitions over the years. One of the first steps in compensation is to match the job titles at the acquired company to our existing job architecture. This process is manual and requires a lot of effort from my team. This effort can be reduced by teaching an AI model about job families, functions, and levels. Let AI read the job descriptions from the acquired company and provide the best match in your job catalog. Of course, a compensation team will need to validate the results and “coach” the model to make it more accurate, but this use of AI can add capacity to your compensation team in a quick time frame.

Compensation survey matching

If you can match jobs for acquisitions, it’s logical you could do it for your survey matches. I expect salary survey companies are already working on this, but the ones that build the best AI assistant to gather information about a company’s jobs and match them to the survey jobs will increase the speed of accessing market data for their clients. In addition, having AI do the matching will lead to more consistency across clients which will lead to more reliable market data for all clients.

Job analysis bot

Building on AI that writes job descriptions and matches jobs, you can use a conversational bot to ask employees about their daily responsibilities. It could conduct structured interviews across large employee populations to ask what tasks employees do and don’t do in their current job descriptions. Collecting this data and identifying patterns will help the compensation team, and all the HR functions, get a better idea of which skills and experiences are used on the job.

Compensation analyst bot

Once we start asking employees about their jobs, you could provide a resource for managers who need to get a compensation recommendation for a role or an employee. Leveraging the data about the jobs and the internal and external market data that has been matched and collected, a manager could describe the role they would like to know about and get a recommendation on the pricing for the job.

Pay equity analysis

Most organizations do these once a year, and there is a high level of effort to analyze and take action on the data. With AI, analysis can happen all the time. An AI model can scan the pay data on the existing employee population and identify pay equity trends earlier. You may choose to take make adjustments at pre-defined intervals, but the early warnings would allow your compensation and HR teams to work on mitigating the trends. It’s not difficult to imagine an AI assistant reviewing a manager’s adjustments as they enter them into the HRIS and recommending a different adjustment amount to prevent pay equity concerns.

Keeping up to date with rapid changes to AI

The world of AI is changing so rapidly that most companies creating new applications weren’t in existence a year ago. Some hardly have any online presence. As an HR practitioner, if you want to stay up to date, you’ll have to venture outside of the standard HR sources of information. If you wait for an HR Industry report to learn what’s new, you’ll be behind the curve. It’s possible by the time you read this, the information will have changed.

There are plenty of places to start looking at what’s new in AI. ProductHunt is an online catalog updated with new applications every day. Not all of them apply to HR, but you’ll see what new ideas are out there. Another online source is ‘There’s an AI for That.’ Type the problem you are trying to solve. It will tell you which AI product address your need. Subscribe to newsletters that follow this fast-changing technology. I follow Built for AI to read daily updates on AI tools. Finally, connect with your compensation colleagues. I’m a huge proponent of networking for knowledge. Reach out to your compensation colleagues around the world and ask them what they are hearing about AI in compensation. This is the exact approach I used to find a solution for my job mapping problem in acquisitions.

Your organization is probably considering AI for all sorts of business applications. Unfortunately, compensation is unlikely to be at the top of the priority list, so you’ll need to educate yourself and bring new ideas and their benefits to your organization.

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Greg Roche is a VP of Compensation at large healthcare company. He’s spent the last 25 years working in the healthcare, consulting, cybersecurity, and multifamily real estate.