Day 9: Research Salary Benchmarks and Write a Compensation Brief

The concept

Compensation decisions should not be made from instinct, pressure, or the loudest manager in the room.

Pay affects attraction, retention, fairness, trust, and cost discipline. It also becomes harder to explain when decisions are made informally. A salary band that made sense two years ago may now sit below market. A retention adjustment may create internal equity issues. A new hire offer may solve one problem while creating another for existing employees.

AI can help structure a compensation brief, especially when the data is messy. But compensation work requires honesty about uncertainty. The model should help you label assumptions, not hide them.

Plain English

A good pay brief explains what we know, what we assume, what risk exists, and what decision is being recommended.

Market data is useful but imperfect

Salary benchmarking sounds precise, but the data often is not.

Different sources use different definitions, sample sizes, locations, company sizes, and compensation elements. A job board range may reflect advertised salaries, not actual offers. Recruiter feedback may be current but anecdotal. A formal salary survey may be reliable but expensive or slightly dated. Internal offer data may be real but limited.

AI can help compare sources, but HR must understand their reliability.

Common sources include:

• salary surveys
• recruiter conversations
• job adverts
• platform salary insights
• compensation consultants
• internal offer acceptance data
• exit interview or retention-risk data
• competitor job postings

No single source tells the whole story. The brief should explain how much confidence you have.

Define the comparison correctly

Pay benchmarking fails when the comparison is too broad.

A "HR Manager" in a 50-person nonprofit is not the same market as an "HR Manager" in a global technology company. A remote role across a national market is different from a role requiring three days per week in a high-cost city. A role with equity, bonus, and strong benefits cannot be compared only by base salary.

Before asking AI to synthesise data, define:

• role scope
• level
• location
• industry
• company size
• reporting line
• people management
• technical depth
• total reward elements
• hiring or retention context

The cleaner the comparison, the more useful the recommendation.

Internal equity matters

Market data is only one side of the decision. Internal equity is the other.

If you increase the salary range for a hard-to-fill role, what happens to current employees in the same or adjacent roles? If a retention adjustment is approved for one person, are there others with similar contribution and similar pay position? If a new hire offer exceeds the pay of an experienced internal peer, how will you explain that?

This does not mean every pay adjustment must trigger company-wide change. It does mean HR should name the equity implications before leadership decides.

A strong brief includes:

• current range
• proposed range
• market position
• internal comparators
• compression risk
• retention or attraction risk
• estimated cost
• recommendation

Frame the same data for different stakeholders

Finance and people leaders may care about the same decision for different reasons.

Finance will ask:

• What is the cost?
• Is the evidence strong enough?
• What precedent does this create?
• What happens if we do nothing?
• Can the decision be contained?

People and talent leaders will ask:

• Can we hire at this range?
• Are we losing strong people?
• Is our employer brand affected?
• Are we creating equity concerns?
• Does this support the workforce plan?

The recommendation should be consistent, but the framing can change. AI is useful for drafting both versions so the same evidence lands with different audiences.

Be transparent about assumptions

Compensation briefs become risky when they present estimates as certainty.

Use language such as:

• based on the sources available
• this suggests, but does not prove
• the strongest data point is
• the weakest assumption is
• further validation is needed before
• the recommendation is proportionate because

This is not weakness. It is professional judgement.

Connect pay to business risk

Leaders are more likely to act when the risk is concrete.

For hiring, the cost of inaction may include:

• longer time-to-fill
• declined offers
• weaker candidate pools
• recruiter fees
• delayed projects
• manager time spent interviewing unsuitable candidates

For retention, the cost may include:

• replacement cost
• lost institutional knowledge
• disruption to client relationships
• productivity loss during backfill
• morale impact if pay concerns spread

The brief should not dramatise these costs, but it should name them.

Include options, not only one answer

Compensation decisions often improve when leadership can compare options.

Instead of presenting only one recommendation, consider a short options table:

• maintain current range
• adjust range partially
• move to market median
• approve a targeted retention adjustment
• redesign the role level
• add non-salary reward elements

For each option, note cost, risk, fairness implications, and likely talent impact.

This helps leaders see that "do nothing" is also a decision with consequences. It also gives finance a way to engage with the recommendation without feeling boxed in.

Watch for compression and precedent

Pay changes rarely affect only one role.

If a new salary range overlaps with more senior roles, you may create compression. If a retention adjustment brings one employee above peers with similar contribution, you may create fairness concerns. If a hard-to-fill role receives a special adjustment, other managers may ask for the same treatment.

These risks do not always mean the adjustment is wrong. They mean the brief should name the implications.

Before finalising, ask:

• Who else is paid near this range?
• Does the change affect promotion differentials?
• Are there gender, ethnicity, or other equity patterns to review?
• Is this a one-off exception or a band change?
• How will we explain the decision if challenged?

Good compensation work is not only market-aware. It is internally coherent.

Make the recommendation decision-ready

A weak brief ends with "leadership should consider reviewing the range." A stronger brief says what decision is needed.

For example:

Approve an adjusted hiring range of X to Y before reopening the role, subject to an internal equity check for current employees in adjacent roles.

That sentence gives leaders something to approve, reject, or amend.

AI can help sharpen the recommendation, but HR should own the decision logic.

Today's practice

Choose one pay decision. Gather the data you have, even if imperfect. Run the prompt and review the output carefully.

Ask:

1. Are the comparison roles genuinely comparable?
2. Which data source is strongest?
3. Where are we making assumptions?
4. What internal equity risk exists?
5. What decision does leadership actually need to make?

By the end, you should have a clearer compensation brief and a more honest view of what the evidence supports.