AI Project Canvas - Blank Canvas

Project Title:

Data

≡

Which data do you need?

Use Example

• Primary Data Sources:
- Customer interaction records
- Order history and transaction data
- Product information (specifications, pricing)
- User feedback and survey responses

• Technical Requirements:
- Data format specifications
- Volume considerations (expected size)
- Update frequency requirements
- Integration points with existing systems

Skills

🔑

Which skills do you need for development?

Use Example

• Technical Skills:
- Machine learning/AI expertise
- Data engineering and preprocessing
- Backend/frontend development
- DevOps and deployment

• Domain Skills:
- Industry-specific knowledge
- Business process understanding
- Regulatory compliance expertise

• Project Skills:
- AI project management
- Change management
- Stakeholder communication

Value Proposition

🏆

What is the value added by your project?

Use Example

• Efficiency Improvements:
- Process automation (reduce manual work by X%)
- Time savings (reduce processing time by X)
- Error reduction (lower error rates by X%)

• Financial Benefits:
- Cost reduction (estimated $X savings)
- Revenue increase (potential $X growth)
- Resource optimization

• Strategic Advantages:
- Enhanced customer experience
- Competitive differentiation
- Scalability improvements

Integration

⊕

How will the project be integrated?

Use Example

• Systems Integration:
- CRM/ERP connection points
- API requirements
- Database interactions
- Authentication systems

• Process Integration:
- Workflow adjustments
- Business process changes
- Decision points for AI vs. human

• Technical Requirements:
- Data exchange formats
- Real-time vs. batch processing
- Monitoring and logging

Customers

👤

Who are the end customers?

Use Example

• Primary Users:
- Direct users of the AI system
- User demographics and characteristics
- Technical proficiency levels

• Secondary Users:
- Indirect users benefiting from output
- Management and oversight roles

• External Stakeholders:
- Customers/clients
- Partners and suppliers
- Regulatory bodies (if applicable)

Output

🎯

Which key metrics are you optimizing for?

Use Example

• Performance Metrics:
- Accuracy/precision (target: X%)
- Speed/response time (target: X seconds)
- Throughput capacity (X transactions/hour)

• Business Metrics:
- Productivity improvements (target: X%)
- Cost reduction (target: $X)
- Customer satisfaction (target: X point increase)

• Technical Metrics:
- System reliability/uptime (target: X%)
- Scalability measurements
- Quality assurance metrics

Stakeholders

👥

Who are the key stakeholders?

Use Example

• Project Leaders:
- Project sponsor/champion
- Project manager
- Technical lead

• Key Influencers:
- Department heads
- Subject matter experts
- IT and security teams

• Affected Groups:
- End users whose work will change
- Cross-functional team members
- External partners or customers

Cost

💰

What costs will the project incur?

Use Example

• Development Costs:
- AI/ML model development: $XXX,XXX
- Software engineering: $XXX,XXX
- Data acquisition/preparation: $XX,XXX
- Testing and validation: $XX,XXX

• Implementation Costs:
- Integration: $XX,XXX
- Training: $XX,XXX
- Change management: $XX,XXX

• Ongoing Costs:
- Maintenance and updates: $XX,XXX/year
- Infrastructure/hosting: $XX,XXX/year
- Support and operations: $XX,XXX/year

• Total Investment: $XXX,XXX over X months

Revenue/Savings

📈

How will the project generate revenue or savings?

Use Example

• Direct Savings:
- Labor cost reduction: $XXX,XXX/year
- Error/waste reduction: $XX,XXX/year
- Process efficiency gains: $XXX,XXX/year

• Revenue Opportunities:
- New product/service capabilities: $XXX,XXX
- Improved customer retention: $XX,XXX
- Market expansion potential: $XXX,XXX

• ROI Analysis:
- Payback period: XX months
- 3-year ROI: XXX%
- Annual return rate: XX%