AI Strategy
From Pilot to Production
A practical framework for scaling AI from PoC to business-critical systems.
The Roadmap
Most AI projects die between pilot and production. Here's how to avoid that.
Phase 1: Data Foundation (Months 0-3)
Audit existing data sources, establish governance policies, and build data pipelines. This phase looks boring but unlocks everything after.
Phase 2: Model Development (Months 3-6)
Build and train your model using production-quality data. Establish monitoring and feedback loops. Start thinking about deployment infrastructure.
Phase 3: Governance & Compliance (Months 6-9)
Set up model governance, audit trails, explainability, and compliance. Define who owns the model and who watches it in production.
Phase 4: Deployment & Operations (Months 9-12)
Deploy to production with proper monitoring, alerting, and rollback procedures. Train teams. Establish runbooks for common failure modes.
Phase 5: Continuous Improvement (Months 12+)
Monitor model drift, retrain regularly, and gather feedback from users. Scale to additional use cases.