Day 9: Research Salary Benchmarks and Write a Compensation Brief
The Concept
Compensation decisions are among the highest-stakes choices an organisation makes, and they are made surprisingly often on instinct. A manager knows that someone is underpaid because they are always getting calls from recruiters. Finance sets a salary band based on what the last person in the role was paid. A new hire's offer is determined by what they were earning previously. None of these approaches constitutes market benchmarking, and all of them create risk: the risk of overpaying, the risk of losing people, and increasingly, the risk of failing a pay equity audit.
Salary benchmarking is not complicated in concept. You identify what comparable roles pay in comparable organisations in comparable markets, you compare that to what you are currently paying, and you make a decision about where you want to position yourselves relative to that market. The difficulty is that the data is fragmented, the comparisons are rarely clean, and the translation from raw data to a leadership recommendation requires judgment calls that are easy to get wrong under pressure.
Why Compensation Transparency Is Becoming Unavoidable
Pay transparency legislation is advancing across Europe and increasingly influencing UK and US practice. The EU Pay Transparency Directive requires organisations with 100 or more employees to report gender pay statistics and provide salary range information to candidates. Similar requirements are already in force in several US states. Even where it is not yet legally mandated, candidates increasingly expect to see salary ranges in job adverts — and they share offer letter information in ways they never did a decade ago.
This means that informal, ad hoc compensation decisions are becoming harder to sustain. When employees can compare their salaries with colleagues more easily, when candidates can look up your pay ranges, and when regulators can require you to explain your pay structure, the organisations that have done the benchmarking work are in a much stronger position than those that have not.
How AI Synthesises Data From Multiple Sources
The challenge with salary benchmarking is rarely that data does not exist. It is that the data comes from multiple sources with different methodologies, different sample sizes, and different definitions of what counts as a comparable role. LinkedIn Salary Insights, recruiter conversations, industry salary surveys, job board data, and internal pay review records all tell you something, but they do not tell you the same thing in the same way.
AI is useful here as a synthesis layer. When you describe the data you have — even imperfect data — and the role you are benchmarking, it can help you identify where the sources agree, where they diverge, and what that divergence might mean. It will flag where your data is thin or where an assumption has been made. It cannot replace a formal salary survey from a compensation consultancy, but for day-to-day benchmarking decisions, it can turn a collection of disparate data points into a structured, presentable view.
Framing Pay Recommendations for Finance and Leadership
The biggest gap in most compensation briefs is not the data — it is the framing. A recommendation to adjust a salary band means something different to a finance director managing headcount costs than it does to a talent director trying to fill a critical vacancy. The evidence is the same. The argument has to be constructed differently.
Finance wants to understand the cost of action versus the cost of inaction. The cost of inaction in compensation terms is usually turnover: recruitment fees, productivity loss, onboarding time, and the institutional knowledge that walks out with the departing employee. Talent wants to understand market position and what it means for the organisation's ability to hire and retain. A compensation brief that can speak to both audiences — and ideally, that acknowledges the tension between them — is far more likely to result in a decision than one that presents data without context.
Prompt of the day
Copy this into your AI tool and replace any bracketed placeholders.
Prompt
You are a compensation and total rewards specialist. I need to prepare a compensation benchmarking brief for a leadership or finance discussion. Help me structure the research and write a clear recommendation based on the following: - Role being benchmarked: [JOB TITLE, e.g. Senior Data Analyst] - Location or region: [e.g. London, UK or remote across the UK] - Industry or sector: [e.g. financial services, mid-market, approximately 800 employees] - Internal salary range currently in use: [e.g. £48,000–£55,000 base] - Sources of market data I have available: [e.g. data from LinkedIn Salary Insights, one external recruiter conversation, our last salary review was in 2022] - Any equity, bonus, or benefits context: [e.g. 10% annual bonus, 25 days leave, hybrid working, no equity currently offered] - The specific decision this brief needs to support: [e.g. whether to adjust the salary band before opening a new hire requisition, or whether a current employee's retention risk is pay-related] Using this information, please: 1. Identify the key variables that affect compensation for this role and explain which data sources are most reliable for this type of benchmarking 2. Synthesise the available data into a view of where our current range sits relative to market (below, at, or above median — and which percentile you estimate) 3. Draft a one-page compensation brief in plain language, structured as: executive summary, market position, gap analysis, and recommendation 4. Flag assumptions you have made where my data is incomplete 5. Suggest two or three follow-up data sources or questions that would strengthen this analysis before it goes to the leadership team
Your 15-minute task
Gather whatever salary data you currently have access to — even if it is imperfect or partial. Pull a recruiter conversation note, a LinkedIn salary range screenshot, or your last pay review records. Fill in the bracketed fields with the actual role and context you are working on, then run the prompt. When you review the output, focus on the gap analysis and the assumptions flagged — those are the areas where your judgment needs to override the AI's inference.
Expected win
A structured one-page compensation brief you can present to leadership or finance, including a market position assessment, a pay gap analysis, and a clear recommendation — with flagged assumptions so you know exactly where the data is thin.
Power user tip
After the brief is drafted, send this follow-up: 'Rewrite the recommendation section of this brief in two versions: one framed for a finance leader whose primary concern is cost control, and one framed for a people or talent leader whose primary concern is retention and attraction. Use the same underlying data but adjust the emphasis, the order of arguments, and the language to match each audience.' Knowing how to frame the same recommendation for different stakeholders is the difference between a brief that gets read and one that gets filed.