Prova-Artefakt
AI-Readiness-Scorecard für Marketingteams
Bewerte, ob ein Marketingteam die Signale, Daten, Arbeitsrhythmen und kodifizierte Urteilskraft hat, um AI-Workflows verantwortungsvoll zu pilotieren.
Am besten fürMarketing-VPs, die entscheiden, ob ihr Team bereit für einen AI-Pilot ist · Agenturleiter, die keine AI-Transformation verkaufen wollen, bevor Delivery bereit ist · Fractional CMOs oder Consultants, die ein Kundenteam einschätzen · Marketing-Ops-Leads, die Grundlagenlücken sichtbar machen, bevor Tools gekauft werden
Vorlagenfelder
- Readiness item
- What 4+ looks like
- Current score from 1-5
- Evidence
- Gap
- Owner
- Next 30-day fix
Ausgefülltes Beispiel
An in-house growth team preparing to pilot AI-supported reporting and campaign recommendations.
Die ausgefüllten Beispiele unten bleiben auf Englisch, weil Prova eingereichte Artefakte auf Englisch prüft.
Schwache Version vs. starke Version
Schwache 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 |
Warum es scheitert
- 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.
Starke 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 |
Warum es funktioniert
- 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.
Was Prova prüft, was generische AI oft übersieht
- 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
Nächster Schritt
Willst du Feedback zu deiner Version? Prova startet mit einem kurzen Assessment, damit der Review-Standard zu deiner Rolle, deinem Ziel und deiner ersten Zielgruppe passt. Danach kommst du in den Sprint, der zu deiner aktuellen Arbeit passt.
Prova ist derzeit nur auf Englisch verfügbar.
Vor dem Absenden: entferne Kundennamen, vertrauliche Zahlen und alles, was dein Team nicht in einem Trainings- oder Coaching-System speichern würde.
Weiter zu Prova auf Englisch