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Prova成果物
俾Marketing Team用嘅AI Readiness Scorecard
評估marketing team有冇足夠signal、data、operating rhythm同codified judgment，去負責任咁pilot AI workflow。
適合判斷team準備好做AI pilot未嘅Marketing VP · 唔想delivery未ready就賣AI transformation嘅agency leader · 評估client team嘅fractional CMO或consultant · 想喺買tool之前揭示foundation gap嘅marketing ops leader
模板欄位
- Readiness item
- What 4+ looks like
- Current score from 1-5
- Evidence
- Gap
- Owner
- Next 30-day fix
填寫示例
An in-house growth team preparing to pilot AI-supported reporting and campaign recommendations.
下面填寫示例會保留英文，因為Prova會用英文review提交嘅artifact。
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
弱版本 vs 強版本
弱版本
- Item
- Data is clean
- Score
- 4
- Evidence
- We have dashboards
- Gap
- Some naming issues
- Owner
- Analytics
- Next fix
- Improve tracking
| Item | Data is clean |
|---|---|
| Score | 4 |
| Evidence | We have dashboards |
| Gap | Some naming issues |
| Owner | Analytics |
| Next fix | Improve tracking |
點解唔夠好
- 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.
強版本
- 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
| 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 |
點解有效
- 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.
Prova會審視泛用AI成日漏咗嘅位
- 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
下一步
想攞feedback？Prova會先做一個短assessment，令review標準配合你嘅角色、目標同第一批受眾。之後你會入去適合而家工作嘅sprint。
Prova暫時只提供英文版。
提交前：請移除客戶名、保密數字，同任何team唔想存入training或coaching system嘅資料。
前往英文版Prova