Announcing o11's backing from Y Combinator.
HR Workforce Planning in Google Sheets with AI
Every HR team runs on spreadsheets. It does not matter whether your company has 200 employees or 20,000. The HRIS holds the records, but the real workforce planning happens in Google Sheets: headcount models that project hiring needs by quarter, compensation analyses that compare your pay bands to market data, attrition trackers that try to predict which departments are going to lose people next. These spreadsheets are how HR leaders make the case for budget, flag risks to the executive team, and keep hiring on track.
The challenge is that workforce planning spreadsheets are uniquely complex. A single headcount model might span a dozen sheets: current roster, planned hires by department, backfill assumptions, contractor conversions, cost projections by level. Each sheet feeds into the next, and a change in one assumption ripples through the entire model. When the CFO asks “what happens if we delay the Q3 engineering hires by one quarter,” the People Analytics team spends hours manually adjusting formulas, recalculating loaded costs, and updating the summary tab.
Most HR professionals are strong communicators and strategic thinkers, but they are not spreadsheet power users. The gap between the question they want to answer and the formula chain required to answer it is where time disappears. A compensation benchmarking analysis that should take an afternoon turns into a two-day project because of VLOOKUP debugging and pivot table configuration.
Headcount Planning Models with Growth Scenarios
Headcount planning is the backbone of HR’s contribution to business strategy. You need to model not just current state but multiple futures: what does the org look like if revenue hits the aggressive target versus the conservative one? What if attrition in engineering runs 5 points above forecast? What if the board approves the new product line and you need to staff it from scratch?
o11 For Google Sheets understands multi-sheet workbook structures and can build scenario models from your existing data rather than requiring you to start from a template.
“Using the current headcount on Sheet 1 and the hiring plan on Sheet 2, create three growth scenarios on a new sheet: conservative at 10% headcount growth, base at 18%, and aggressive at 25%. Show quarterly headcount by department and total loaded cost using the salary bands on Sheet 3.”
“Add an attrition assumption row for each department. Default to last year’s attrition rate from the data on Sheet 4, but let me override individual departments. Recalculate net headcount and total cost for each scenario.”
o11 builds the formulas that connect your sheets, handles the conditional logic for overrides, and produces a summary table that is ready for your CFO review. The scenarios are not static snapshots. They are live models with real formulas, so when you update a single assumption, every downstream number recalculates automatically.
This is the difference between asking a chatbot to describe what a headcount model should look like and having an AI that actually builds the model in your workbook, connected to your real data.
Compensation Benchmarking Analysis
Compensation analysis is one of the most formula-intensive tasks in HR. You need to compare internal pay data against market benchmarks, identify employees who fall below band minimums or above maximums, calculate compa-ratios across the organization, and model the cost of bringing everyone to target.
The raw data is usually a mess: your HRIS export has its own column structure, the market data from Radford or Mercer comes in a different format, and the job-level mapping between your internal titles and the survey titles requires a manual crosswalk.
“Match employees on Sheet 1 to market benchmark data on Sheet 2 using the job level mapping in column D. Calculate compa-ratio for each employee as current salary divided by the benchmark midpoint. Flag anyone below 0.85 or above 1.15.”
“Create a compensation summary by department showing headcount, average compa-ratio, number of employees below band minimum, and the total cost to bring all below-band employees to the minimum. Group by job level within each department.”
o11 handles the joins between your internal data and the benchmark data, builds the compa-ratio calculations, and produces the summary analysis on a new sheet. When the VP of Engineering asks why three of their senior engineers are flagged, you can trace every number back to its source formula.
The output is a working spreadsheet, not a PDF or a slide. Your compensation committee can open it, filter by department, drill into individual records, and see exactly how every recommendation was derived.
Attrition Prediction from Historical HR Data
Predicting attrition is where HR analytics gets genuinely hard. You have historical data: tenure at departure, department, manager, performance rating, time since last promotion, compensation relative to band. The question is which combinations of these factors predict higher turnover, and which current employees match those patterns.
Most HR teams do this analysis manually, looking at pivot tables of attrition by department and tenure bucket. The more sophisticated teams run logistic regressions in R or Python. But there is a middle ground: structured analysis in your spreadsheet that identifies risk patterns without requiring a statistics degree.
“Analyze the attrition data on Sheet 1 covering the last 3 years. Create a summary showing attrition rate by department, tenure bucket (0-1 year, 1-2, 2-3, 3-5, 5+), and performance rating. Highlight any segment where attrition exceeds 20%.”
“Using the patterns from the attrition analysis, flag current employees on Sheet 2 who match high-risk profiles: tenure over 2 years with no promotion, compa-ratio below 0.90, or in a department with above-average attrition. Add a risk score column.”
o11 processes your historical data, builds the segmentation analysis, and applies the identified patterns to your current roster. The risk flags are transparent: you can see exactly which criteria triggered each flag, and you can adjust the thresholds based on your organizational context.
This is not a black-box attrition prediction model. It is structured spreadsheet analysis that any HR business partner can understand, explain to a manager, and use to have a targeted retention conversation.
Before and After: Quarterly Workforce Review
Before o11: The People Analytics team spends four days preparing for the quarterly workforce review. Day one is pulling and cleaning data from the HRIS. Day two is building headcount and cost models across scenarios. Day three is running compensation analysis and attrition reports. Day four is formatting everything into presentable tables and charts. By the time the review happens, the data is already a week old.
After o11: The team pulls the HRIS export and loads it into Google Sheets. Over the course of a morning, they use o11 to clean the data, build scenario models, run compensation benchmarking, and generate attrition risk flags. The afternoon is spent on what actually matters: interpreting the results, identifying the stories in the data, and preparing recommendations for leadership. The analysis is fresher, more thorough, and took a fraction of the time.
Why o11 Instead of Generic AI Tools
HR data is sensitive. Pasting employee salary information, performance ratings, and personal details into a general-purpose chatbot is a compliance risk that most HR leaders will not accept. Even if your company allows it, the workflow is broken: you copy data out of Sheets, paste it into a chat, get a text response, and then manually rebuild the analysis back in your spreadsheet.
o11 works inside Google Sheets as a native layer. Your data stays in your workbook. The analysis is built with real formulas and real cell references that anyone on your team can audit. When your CHRO asks how a number was calculated, you can click on the cell and show them the formula, not point them to a chat log.
For HR teams that need to balance analytical speed with data governance, auditability, and the ability to share working models with finance and leadership, o11 fits the way you already work.
