Most enterprises have run AI pilots. Few have scaled them successfully. The gap between pilot and production is where AI dreams die.
Why Pilots Don't Scale
Technical debt: Quick pilots create systems that can't scale.
Data access: Pilots use sample data; production needs real data pipelines.
Integration: Pilots are standalone; production requires integration.
Governance: Pilots skip governance; production requires it.
The Scaling Framework
Stage 1 - Prove: Demonstrate value with controlled pilot.
Stage 2 - Productionize: Rebuild for reliability, security, scale.
Stage 3 - Integrate: Connect to enterprise systems and processes.
Stage 4 - Scale: Expand to additional use cases and users.
Technical Requirements
MLOps: Automated pipelines for training, deploying, monitoring.
Infrastructure: Scalable compute and storage.
Monitoring: Track model performance in production.
Versioning: Manage model versions and rollbacks.
Organizational Requirements
Operating model: Who owns AI in production?
Skills: Train teams to maintain AI systems.
Governance: Policies for AI development and deployment.
Change management: Help organization adapt to AI.
Common Obstacles
- Underestimating production requirements
- Insufficient ML engineering capability
- Lack of cross-functional alignment
- Moving too fast without proper foundation
Keys to Success
Plan for production from day one. Invest in MLOps early. Build cross-functional teams. Be patient but persistent.