#6: Make Comp & Reward season a breeze with AI

Morning folks, and yes, it’s morning for me because I’m spending this week and next in sunny Orlando ☀️.

Orlando gets a bad rep, because, you know, theme parks and Florida vibes, but the nice parts make it a great city - if you ever come here, head round Winter Park and Baldwin Park for interesting spots, restaurants etc.

Now, talking of things which get a bad rep, let’s move onto Comp Season… 😭 That time of year when we’re running through benchmarking, role mapping, talent planning, equity checkups and performance comparisons all at once…

It’s a nightmare, so let’s take a load off and make it a little easier for you.

This week, I’m going to focus on:

• Running a simple reward process, using an agentic workflow, with some enhancement suggestions for later stage companies.

• Sharing a little report I ‘crafted’.

• Some fun news on a new community I’m starting for builders with Frieda Möcker.

• The news of the week.

Who Am I?

For new subscribers, I’m Matt Bradburn, founded and sold People Collective, previously VP People at Peakon, and spent my entire career in recruiting and HR.

I have been using AI in my work for the last 2 years, starting from nothing and now able to build agents and small apps, or provide deep research to clients to solve their challenges.

Now I want to share my learnings with you all.

🎯 Deep Dive of the Week

So this week I’m diving into Compensation and Reward. It’s always a challenging process for folks to run end to end, but i’m going to get you through it as simply as possible, but suggest some ways folks

The Challenge:

Compensation Benchmarking and planning for comp changes and promotions is an art and a science. Unfortunately the science aspect requires a lot of analytical capabilities, and if you’re a People team without a full time reward person, that might not be top of your skillset.

So how can we:

1. Speed up the process of analysis and get to effective answers faster - Compa-ratios, updated bandings, updated benchmarks etc.

2. Improve the quality of analysis - ensure we’re coupling performance, levels, geographies etc, ensure we include our comp philosophy into this as well.

3. Drive better outcomes - ensure we are paying people equitably based on their level and performance.

Roadmap Buildout:

So the steps we need to take here:

1. Gather our data - in my case, I’m using good old FinEdge again, and I have the Salary and performance data, both from last time and also my new benchmarks at the 75th percentile. (Feel free to copy either of these sheets to use yourself for data structures ❤️)

   1. I used this prompt to get the initial data here:

      “I would like you to create dummy data for this file using the headings. Finedge is a 100 person SaaS company in the Fintech space, employing people in 5 locations, paying at the 75th percentile, with 9 departments - Engineering, Product, Marketing, Sales, Customer Success, HR, Finance, Legal, Operations. Make the data realistic based on a company of this stage. The headquarters are in New York. Please return the data in a format I can easily paste into the file.”
      

2. We need to work out what we want to achieve:

   1. I want to assess and create a spreadsheet report HERE with all the new data based on the updated benchmarks, with new benchmarks at the 75th percentile, new Compa ratios, new bands, new recommendations on promotions, new recommendations on salary increases.

   2. I want a report I can present to the leadership and the board on our compensation structure and plans.

3. We have to build our prompts to make it happen:

   1. Why do I do this? Because this way we get better prompt and therefore a better outcome!

Using the format above, I want you to create me a prompt to do the following.

To start with I have 3 spreadsheets:

Finedge Team
Finedge Report
Comp Data

I want you to review the Finedge Team spreadsheet in detail and the Comp Data Spreadsheet as well. I want you to compare and contrast each employee by their level, latest performance score, role, department, CompA ratio. I then want you calculate the updated: New 75th Percentile amount by role and level, New Band by role and level, New CompA ratio by role and level, amount and % over or underpaid by role and level, whether to promote them based on comp and performance level, whether to increase salary by comp and performance level and if so, what amount to increase the salary by. 

Our compensation philosophy is that we have a global pay band, equitable around the world so you can ignore geographies.

I then want you to create an updated csv table for me to put into the FinEdge Report, which copies the column format perfectly from the FinEdge team sheet, with the updated information by employee as I requested above.

This is the prompt Grok then gave me

I want you to review the Finedge Team spreadsheet and the Comp Data spreadsheet in detail, then compare and contrast each employee based on their level, latest performance score, role, department, and CompA ratio. After that, calculate the following: New 75th Percentile amount by role and level, New Band by role and level, New CompA ratio by role and level, amount and % over or underpaid by role and level, whether to promote them based on comp and performance level, whether to increase salary based on comp and performance level, and if so, what amount to increase the salary by.
Goal
For each employee, return the following details: employee name, level, role, department, latest performance score, current CompA ratio, New 75th Percentile amount (by role and level), New Band (by role and level), New CompA ratio (by role and level), amount over/underpaid (in currency), % over/underpaid, recommendation to promote (Yes/No with reasoning), recommendation to increase salary (Yes/No with reasoning), and if a salary increase is recommended, the amount of the increase (in currency). Then, create an updated CSV table that matches the column format of the FinEdge Team spreadsheet, incorporating all the updated information for each employee to be used in the FinEdge Report.
Return Format
Be careful to ensure that all roles and levels in the Finedge Team spreadsheet have corresponding benchmark data in the Comp Data spreadsheet—flag any mismatches immediately. Double-check that the New 75th Percentile and New Band calculations are accurate and consistent across all employees in the same role and level. Ensure that promotion and salary increase recommendations are logical and align with both performance scores and compensation data (e.g., avoid recommending a salary increase for an overpaid low performer). Verify that the CSV table perfectly matches the column structure of the FinEdge Team spreadsheet, with no missing or misaligned columns, and that all numerical values (e.g., amounts, percentages) are calculated correctly and formatted consistently.
Warnings
For context: Our compensation philosophy is global, with equitable pay bands around the world, so geography should be ignored in all calculations. The FinEdge Team spreadsheet contains employee-specific data (e.g., name, level, role, department, performance score, CompA ratio), while the Comp Data spreadsheet provides benchmark data for roles and levels (e.g., 75th percentile amounts, pay bands). We aim to ensure fairness in compensation, so employees should be paid equitably based on their role, level, and performance, aligning with the New 75th Percentile and New Band. Promotions and salary increases should be recommended only when justified by both performance and compensation data—for example, high performers who are underpaid relative to the New Band should be prioritized for salary increases or promotions. The FinEdge Report is used by leadership to make final compensation decisions, so the updated CSV must be precise and consistent with the FinEdge Team format.

Next Steps

Step 1 - Run the thing

Open up a new session in Grok / Claude / OpenAI, and download your sheets as CSV files to your computer.

Then, upload them and run the prompt as follows:

Step 2 - Check and Validate on the fly

Double Check the work and Validate Quickly whilst it builds, and as we can see here, it’s working pretty darn nicely, but why the big change in the CompA ratios? It looks like something is up here as Alex CompA ratio shifted a lot. It could be from the benchmarks, or something else, so let’s check…

So we ask as we go…

Can you double check and validate your work here to be 100% sure, particularly on your CompA ratio calculations?

Looks like maybe we were using an old dataset - most likely, or calculating CompA ratios wrong - human error - also a high potential. I checked it out and it was because of the old comp data, which makes sense.

What was also interesting was the rules Grok decided to apply to its recommendations, with no oversight or input, and they’re not bad:

New 75th Percentile: From Comp Data, first table, by department and level.

• New Band: From Comp Data, second table, by department and level.

• New CompA Ratio: Current Salary / New 75th Percentile.

• Over/Underpaid ($): Current Salary - New 75th Percentile.

• Over/Underpaid %: (Over/Underpaid $) / New 75th Percentile * 100.

• Promote?: Yes if performance score ≥ 4 and New CompA < 0.9 (underpaid) or underpaid relative to next level, unless at Level 8.

• Increase Salary?: Yes if score ≥ 4 and underpaid (New CompA < 1.0), or significantly underpaid (>10%) with score 3. Amount aligns with New 75th Percentile.

Step 3 - Get the data back in our sheet

We take the data in csv format and simply copy and paste it into our report. Then run our final checks to ensure and validate, knowing what we know, that this is useful.

OUR SPREADSHEET 

Step 4 - Then let’s get a nice report for some final flair…

Ok, now I want you to act as the Chief People Officer of FinEdge and create a report for the CFO and CEO, where we have: Useful insights on performance, shifts, distribution etc, insights on comp changes, key recommendations for people to change and why, and any other insights a CFO and CEO would need for a board remuneration committee. Create the report to be copied into Notion.

As always, I like to make sure you can make use of this template, and I am very happy with this one - as I am sure your CEO and CFO will be too!

🤯 PS - I created all of the above in about 15 minutes work…

If you want to build the capability to run fast and get great work done in People Ops, using AI join the other people leaders in my next cohort, now held at 1.30pm on Thursdays UK time, starting April 21st

💡 Quick Win of the Week

This one is more a little fun I had this week, creating a nice report and table showing where AI and People Debt intersect in businesses and how one can be used to solve for the other…

It’s not bad… but it could be better…

LINK TO THE GUIDE

🧠 Product Community Updates

Something slightly different this week, inspired by a conversation with Frieda Möcker where we both realised we were building tools for People Ops.

5 hours later, I had an idea, ran with it and posted this little picture on linkedin:

So the focus this week was to get this up and running…

So far, we have over:

• 80 people interested

• A slack community setup and running with those folks already in

• And we’re planning ways to do co-building sessions every week or two 🥳

If you’re interested - sign up here:

📰 News That Matters for HR

📸 Google releases Gemini Flash 2.0

Why does this matter?

Because one of the biggest parts of People Ops is marketing and communications. The more easy it becomes to move and manipulate imagery, graphs, visuals, storytelling etc, the simpler it makes our work!

I use Canva a lot but will be exploring this more to see what I can create.

🤖 Zapier’s Agents got a powerful uplift

Why does this matter?

Zapier has effectively been making ‘agents’ for a while, but now it combines with ai magic to create something outstanding.

On my course this week, I created an agent to build and deliver a detailed role based 30/60/90 day plan for onboarding new employees.

It took me 5 mins to build it… Go play!

👋 Let's Connect!

Finding this valuable? There's more where this came from!

✨ Share this newsletter with your HR network

🔗 Connect with me on LinkedIn: https://www.linkedin.com/in/mattbradburn/ 

💌 Have questions? Just hit reply!

Remember: AI in HR doesn't have to be complicated. Let's figure it out together, one practical step at a time.