Smarter pay decisions, less spreadsheet grind.
Get the Compensation & Benefits briefIn 2026, Compensation & Benefits teams are using AI to speed up market benchmarking, merit cycle modeling, and pay equity analysis that used to take weeks of spreadsheet work. AI assistants now draft total rewards statements, summarize complex plan documents, and answer routine benefits questions so analysts spend more time on strategy. The judgment calls on pay philosophy and budget tradeoffs still sit with people, but the data prep behind them is much faster.
Paste these into Claude or ChatGPT and replace the bracketed parts with your own details.
I have market data for these roles: [paste roles with market 25th, 50th, 75th percentile]. Build a salary band structure with [number] grades, showing minimum, midpoint, and maximum for each, using a midpoint progression of about [percent] and a range spread of [percent].Rewrite this benefits plan description for employees at a [reading level] reading level. Keep it accurate, under [number] words, and add a short list of what is covered and what is not: [paste plan text].Our merit budget is [percent] of a total payroll of [amount]. Allocate increases across these performance ratings [list ratings and headcount per rating] so higher performers get larger increases, and show the average increase per rating and total spend.Create a total rewards statement template that includes base salary, bonus target, employer benefits cost, retirement match, and paid time off value. Use these inputs: [paste sample values] and keep the tone clear and factual.Compare these two compensation packages for an employee earning [amount]: [paste package A and package B with base, bonus, equity, benefits]. Show total value, key differences, and which is stronger for a candidate who values [priority].One AI tool, one prompt, and one trick for Compensation & Benefitss, every weekday morning. Free.