Enterprise AI Adoption Playbook

Why AI Initiatives Stall

Only 10-15% of AI projects reach production. The gap: unclear objectives, insufficient data infrastructure, talent gaps, and lack of operationalization.

Phase 1: Strategy

Identify and prioritize use cases by business impact, feasibility, data readiness, and capacity.

Phase 2: Data Foundation

Modern platforms — lakes, feature stores, streaming — with governance and quality standards.

Phase 3: Proof of Value

Design PoVs with production in mind using representative data and realistic benchmarks.

Phase 4: Production

MLOps: automated pipelines, monitoring, versioning, and human-AI interaction design.

Phase 5: Scaling

AI CoEs sharing best practices. At scale, new projects take 60-70% less time.