AI can spot anomalies in attainment data, model the impact of plan changes, and draft rep statements in seconds. But only if your plan is set up for it. Garbage in, garbage out applies double to AI — the model will happily produce a confident, well-formatted answer from broken data, and you'll never know.
Most comp teams skip the readiness check and dive straight into AI experiments. They get one good demo, then a string of confusing or wrong outputs, and conclude "AI doesn't work for comp." It's not the AI. It's that the inputs aren't structured, the rules aren't documented, the calculation trail is missing, or no one owns the human-in-the-loop step.
This scorecard checks all four. You answer 12 yes/no/partial questions, get a 0–100 readiness score, see which dimension is dragging you down, and walk away with a prioritised gap list.
Why these four dimensions
1. Data foundation (40% of the score)
The biggest predictor of useful AI output. If your plan rules live in a PDF, your attainment data lives in 14 spreadsheets, and your crediting adjustments are in free-text comment fields, AI cannot help you. Weighted highest because every other dimension depends on it.
2. Plan documentation (25%)
AI is a literal interpreter. Ambiguous terms, undefined edge cases, and missing version history produce confidently-wrong outputs. A plan AI can analyse is a plan a new hire could administer correctly without asking questions.
3. Calculation transparency (25%)
If you can't show the formula trail for a single rep's payout, AI cannot validate it, explain it, or model alternatives. Exception payouts and mid-year quota changes need to be logged, not absorbed silently.
4. Governance & access (10%)
Lower weight because it gates deployment, not analysis quality. But you need it before any AI workflow goes live: PII sanitization, a human reviewer for every AI-generated output, and stakeholder agreement on what AI is allowed to decide vs. only suggest.
Each "Yes" = full credit, "Partial" = half credit, "No" = zero. Each dimension scores 0–100, then dimensions are weighted (40/25/25/10) into an overall score. 80+ means ready to deploy production AI workflows. 60–79 means ready for supervised pilots. 40–59 means foundation work needed first. Below 40 means start with the basics — AI will create more problems than it solves.
AI Plan Analysis Readiness Scorecard
12 questions. About 3 minutes. No data leaves your browser.
ℹ️ How this tool works +
The question it answers: Is my comp plan in good enough shape that AI analysis will produce trustworthy results — or will I get confident-sounding nonsense?
What to enter: For each of the 12 questions, click Yes, Partial, or No. Be honest — "Partial" exists for a reason. If you're not sure, default to "No"; that's the answer AI will effectively get.
What you'll get back:
- An overall 0–100 readiness score with a band (Ready / Pilot / Foundation / Not ready)
- A per-dimension breakdown so you can see which area is weakest
- A prioritised gap list — every "No" or "Partial" turned into an action item, ranked by how much it's costing your score
- A recommendation on what to fix before your first AI workflow goes live
Sample answers are pre-loaded so you can see the output shape before you start. Hit "Reset to sample" to restore them.
Weights and bands reflect Falcon's experience across AI deployments in comp ops. They are directional. Calibrate them to your own risk tolerance — a regulated industry should set the "Ready" bar higher than 80.
For each question, click Yes, Partial, or No.
How to act on your score
Score 80–100: Ready to deploy.
Your plan can support production AI workflows: anomaly detection on attainment data, automated rep statement drafting, scenario modelling for plan changes. Start with one workflow, prove the value, then expand.
Score 60–79: Ready for supervised pilots.
Run AI in a sandbox alongside your existing process. Compare outputs side-by-side for a quarter. Use that time to close the gaps flagged below before promoting any workflow to production.
Score 40–59: Foundation work needed.
Don't deploy AI yet. Spend the next 60–90 days on the high-priority gaps — usually data structure and documentation. Re-take this assessment before piloting.
Score below 40: Not ready.
AI will accelerate your existing problems. Get the comp plan documented, structured, and audited the old way first. AI can come later — it'll work better when you do introduce it.
The temptation is to score yourself a 75 and start running AI experiments. The cost of overstating readiness is high — you'll burn trust on outputs that look right but aren't, and the comp team's credibility takes a hit you spend a year recovering from. Score yourself the way you'd score a peer.
Want help closing the gaps?
We help comp ops teams build the data foundation and governance AI needs to work. Talk to us about your readiness score and the path forward.
Book a 20-minute consultation →FAQ
Governance gates whether AI ships, but data determines whether AI is useful. A perfectly governed AI workflow that runs on bad data still produces bad answers — you'll just block the bad answers more carefully. Fix data first.
Only if the PDF defines every term, covers every edge case, and has a version history. A 4-page summary deck doesn't count. The test: could a new comp analyst administer the plan from your documentation alone, with zero verbal handoff?
Usually getting the plan rules out of PDF/Word into a structured format (YAML, JSON, or even a well-formed spreadsheet with one rule per row). That single move tends to bump the data score 15–20 points and unlocks most of the analysis use cases.
Yes — that's the point. The output is designed to be screenshot-and-share. Use it to justify foundation investment before you commit to an AI-in-comp programme that's set up to fail.