AI Project Discovery Checklist
Evaluate your organization's AI readiness across 5 critical dimensions. Complete this assessment to identify gaps and prioritize preparation.
Your Readiness Score
0%
Significant preparation needed
📊
Data Readiness
- Data is organized and accessible (not scattered across systems)
- Historical data available for model training (12+ months)
- Data quality baseline established (% completeness, accuracy)
- Data pipeline infrastructure exists or can be built
- Privacy/compliance requirements mapped (GDPR, etc.)
👥
Team Assessment
- Executive sponsor identified with decision authority
- Dedicated product owner assigned
- Technical leads available for integration
- Change management resources allocated
- Team has growth mindset for AI/ML concepts
⚙️
Infrastructure Audit
- Cloud infrastructure in place (AWS, GCP, Azure, or on-prem)
- Database infrastructure supports analytics queries
- API infrastructure for model serving exists or planned
- Security/compliance framework established
- Monitoring & logging infrastructure operational
🎯
KPI Definition
- Primary success metric identified
- Secondary metrics defined
- Baseline metrics established (pre-AI state)
- Measurement methodology agreed
- Reporting cadence planned
⚠️
Risk Mapping
- Regulatory/compliance risks identified
- Data privacy risks assessed
- Model bias/fairness risks considered
- Integration risks mapped
- Change management risks understood
Next Steps Based on Your Score
Phase 1: Foundation Building (3-6 months)
- Establish data governance framework
- Hire or allocate ML engineering resources
- Conduct infrastructure audit and modernization plan
- Define and baseline key metrics
- Create AI steering committee
Recommendation: Before starting a production project, focus on infrastructure and team building. Consider a proof-of-concept pilot project.
Ready to Move Forward?
Share your assessment results with our team. We'll provide specific recommendations tailored to your situation.
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