Bumalik sa lahat ng templates
Artifact ng Prova
AI Readiness Scorecard para sa Marketing Teams
I-score kung may signal, data, operating rhythm, at codified judgment ang marketing team para mag-pilot ng AI workflows nang responsable.
Mga field ng template
- Readiness item
- What 4+ looks like
- Current score from 1-5
- Evidence
- Gap
- Owner
- Next 30-day fix
Halimbawang may laman
Nasa English ang filled examples sa ibaba dahil English ang artifact review sa Prova.
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
Mahinang version vs matibay na version
Mahinang version
| Item | Data is clean |
|---|---|
| Score | 4 |
| Evidence | We have dashboards |
| Gap | Some naming issues |
| Owner | Analytics |
| Next fix | Improve tracking |
Bakit ito pumapalya
- 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.
Matibay na 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 |
Bakit ito gumagana
- 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.
Ang nirereview ng Prova na madalas nalalampasan ng generic AI
- 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
Susunod na hakbang
Gusto mo ng feedback sa version mo? Nagsisimula ang Prova sa maikling assessment para tumugma ang review standard sa role, goal, at unang audience mo. Pagkatapos, papasok ka sa sprint na bagay sa ginagawa mo ngayon.
Sa ngayon, English lang ang Prova.
Bago mag-submit: alisin ang client names, confidential numbers, at anumang ayaw ninyong ma-store sa training o coaching system.
Magpatuloy sa Prova sa English