Compensation benchmarking is expensive (subscription data + analyst time) for most small businesses. Claude does not replace the underlying data — but it accelerates the synthesis and the band-setting decisions. Here is the workflow.
Comp benchmarking has two phases: gathering market data, and translating that into specific bands for your roles. Claude is good at the second phase if you give it the data.
You still need to subscribe to comp data sources (Pave, Carta data, Levels.fyi, BLS, or industry-specific). AI does not replace the underlying data. It accelerates how you use it.
I am setting compensation bands for [ROLE] at [COMPANY]. Market data I gathered (paste tables): [PASTE] Our company stage: [STAGE] Our geography: [REMOTE / SPECIFIC METRO] Our compensation philosophy: [LEAD MARKET / MEET MARKET / LAG MARKET] Our budget constraints: [SPECIFIC] Recent hires in similar roles and their accepted offers: [PASTE] Return: 1. Recommended bands (low / mid / high) for this role with reasoning 2. How our bands compare to market (above / at / below) 3. Specific rationale for the band split 4. Other compensation components to consider (equity, bonus, benefits, remote allowance) 5. Risks of these bands (e.g., losing candidates above midpoint, internal equity issues) 6. Year-over-year band adjustment recommendation given typical market movement Be direct about trade-offs. If our budget cannot support competitive bands, say so and recommend non-cash levers.
Final band approval. CEO and CFO own this, always.
Individual offer decisions. Within the band, where to place each candidate.
Equity philosophy. Strategic decision tied to fundraise plans.
Compensation conversations with employees. Communication of comp decisions is human work.