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Prova artifact
AI Readiness Scorecard for Marketing Teams
Score whether a marketing team has the signal, data, operating rhythm, and codified judgment needed to pilot AI workflows responsibly.
Template fields
- Readiness item
- What 4+ looks like
- Current score from 1-5
- Evidence
- Gap
- Owner
- Next 30-day fix
Worked example
The filled examples below stay in English because Prova reviews submitted artifacts in English.
An in-house growth team preparing to pilot AI-supported reporting and campaign recommendations.
First-party conversion signals are reliable
First-party data strategy is in place
AI-native campaign types are understood and operational
Paid, owned, and earned are planned as one system
Emerging channels are considered deliberately
Primary KPIs are separated from optimization metrics
Campaign data is clean, labeled, and queryable
Planning principles and brand guides are codified for AI use
Weak version vs strong version
Weak version
| Item | Data is clean |
|---|---|
| Score | 4 |
| Evidence | We have dashboards |
| Gap | Some naming issues |
| Owner | Analytics |
| Next fix | Improve tracking |
Why it fails
- The score is unsupported.
- Dashboards do not prove the data is queryable or trustworthy.
- "Some naming issues" hides the operational impact.
- The owner is a department, not a person or role.
- "Improve tracking" is not a 30-day fix.
Strong version
| Item | Campaign data is clean, labeled, and queryable |
|---|---|
| Score | 2 |
| Evidence | Google Ads and Meta naming conventions differ; LinkedIn uses old campaign taxonomy; weekly report still relies on manual spreadsheet cleanup |
| Gap | AI cannot compare cross-platform performance without manual reconciliation |
| Owner | Marketing analytics lead with paid media lead as reviewer |
| Next 30-day fix | Standardize naming for new campaigns and create a one-page exception log for legacy campaign data |
Why it works
- Evidence is concrete.
- The score is honest.
- The gap explains why AI output would fail.
- Ownership is specific.
- The next fix is narrow enough to complete.
What Prova reviews that generic AI often misses
- Whether readiness scores are backed by evidence
- Whether "we have dashboards" is being mistaken for usable operating data
- Whether gaps are prerequisites or nice-to-haves
- Whether the team is ready for a pilot or needs foundation repair first
- Whether the next sprint should be workflow audit, measurement architecture, rollout planning, or diagnostic repair
Next step
Want feedback on your version? Prova starts with a short assessment so your review standard matches your role, goal, and first audience. After that, you enter the sprint that fits your current work.
Prova is currently available in English only.
Before submitting: remove client names, confidential numbers, and anything your team would not want stored in a training or coaching system.
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