Generic AI fails in enterprise because data is fragmented, evaluation is fuzzy, and governance is missing.
Data contracts and feature stores; RAG with domain context; fine‑tuning/distillation only where ROI justifies; safety evaluation before prod.
Production‑ready models aligned to constraints, measurable quality, and lower inference cost at scale.
Manual decision loops slow work and create inconsistent judgments across teams.
Human‑in‑the‑loop orchestration with deterministic policy checks and clear rollback; full observability with reason codes.
Cycle times down, fewer escalations, and auditable consistency.
Forecasts drift when data quality and context aren’t enforced.
Data quality SLAs, robust features, and ensembles for time‑series and anomaly detection; interpretable outputs for operators.
Higher signal fidelity, earlier anomalies, better decisions.
Models stall in notebooks without reliable deployment, versioning, or rollback.
Containerized deploys, IaC, feature stores, and CI/CD for models and data; drift detection and canary releases.
Faster releases, higher stability, repeatable operations.
Scattered initiatives without prioritization or an operating model for scale.
Strategy → roadmap → operating model; risk management, portfolio control, measurable success criteria.
Aligned investment, accountability, durable operating model.