Prova का artifact
Marketing teams के लिए AI Readiness Scorecard
Score करें कि marketing team के पास responsible AI workflow pilot के लिए signal, data, operating rhythm और codified judgment है या नहीं.
इनके लिए सबसे उपयोगीMarketing VPs जो तय कर रहे हैं कि team AI pilot के लिए ready है या नहीं · Agency leaders जो delivery ready होने से पहले AI transformation बेचना नहीं चाहते · Fractional CMOs या consultants जो client team assess कर रहे हैं · Marketing ops leaders जो tool खरीदने से पहले foundation gaps दिखाना चाहते हैं
Template के fields
- Readiness item
- What 4+ looks like
- Current score from 1-5
- Evidence
- Gap
- Owner
- Next 30-day fix
भरा हुआ example
An in-house growth team preparing to pilot AI-supported reporting and campaign recommendations.
नीचे भरे हुए examples English में हैं, क्योंकि Prova submitted artifacts को English में review करता है.
कमज़ोर version vs मजबूत version
कमज़ोर version
- 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 |
यह क्यों कमजोर है
- 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.
मजबूत 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
| 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 |
यह क्यों काम करता है
- 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 क्या review करता है जो generic AI अक्सर छोड़ देता है
- 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
अगला step
अपनी version पर feedback चाहिए? Prova एक छोटे assessment से शुरू होता है ताकि review standard आपकी role, goal, और पहली audience से मेल खाए. उसके बाद आप अपने current work के लिए सही sprint में जाते हैं.
Prova अभी केवल English में उपलब्ध है.
Submit करने से पहले client names, confidential numbers, और ऐसी कोई भी चीज़ हटाएं जिसे आपकी team training या coaching system में store नहीं करना चाहेगी.
English में Prova पर जाएं