Advanced

AI Project Delivery

Getting an AI model from a notebook to production is where many projects fail. Learn the deployment strategies, impact measurement techniques, and handoff processes that ensure your AI project delivers lasting business value.

Deployment Strategies

Choose the deployment approach based on your risk tolerance and use case:

• Shadow mode: Run the AI system alongside the existing process without affecting users. Compare outputs to validate performance.
• Canary deployment: Route a small percentage of traffic (1-5%) to the AI system. Monitor closely and gradually increase.
• A/B testing: Split users randomly between the old system and the AI system. Measure business metrics for both groups.
• Gradual rollout: Deploy to one team, region, or customer segment first. Expand after validating success.
• Full cutover: Replace the existing system entirely. Only appropriate for low-risk applications or when other strategies are impractical.

Production Readiness Checklist

Measuring Business Impact

Technical metrics alone do not justify an AI project. You must connect model performance to business outcomes:

Technical Metric	Business Translation	Measurement Method
Model accuracy: 94%	6 out of 100 customer queries need human escalation	Escalation rate tracking
Latency: 200ms p95	Users experience instant responses	User satisfaction surveys
Automation rate: 70%	Team handles 3x more requests with same headcount	Throughput comparison
Cost per prediction: $0.01	$50K annual savings vs. manual process	Cost comparison analysis

Handoff and Operations

The project team must ensure a smooth handoff to the operations team:

• Knowledge transfer: Document system architecture, data pipelines, model details, and known limitations
• Runbooks: Create step-by-step guides for common operational tasks: retraining, debugging, rollback
• Monitoring dashboards: Build dashboards that surface the key health indicators for the AI system
• Retraining schedule: Establish when and how the model will be retrained as new data arrives
• Support period: The project team should remain available for 4-8 weeks after launch for questions and issues

Post-Launch Review

Conduct a formal post-launch review 4-6 weeks after deployment:

• Is the system meeting the success metrics defined in planning?
• What unexpected issues arose and how were they handled?
• What would you do differently on the next AI project?
• Should the project scope be expanded or adjusted based on initial results?