Snowflake
AI / ML Strategy
Practical AI on trusted data
Use cases that pay off—not experiments that linger.
We align AI to outcomes: find the high-ROI use cases, check data readiness, pick the right approach (Cortex/Snowpark), and build evaluation harnesses so models are measured, governed, and improvable.
Deliverables
Use-Case Heatmap
Prioritized list of AI/ML opportunities ranked by impact, feasibility, and risk.
Data Readiness Report
Assessment of data quality, coverage, governance, and gaps for AI/ML.
Model Choice
Recommendations on vendor models vs. custom models, including where to use ServiceNow and Snowflake AI.
RAG Blueprint
Architecture for retrieval-augmented generation across your docs, tickets, KB, and logs.
Evaluation & MLOps
Metrics, guardrails, and deployment patterns to monitor, retrain, and safely evolve models.
KPIs
- Time-to-first win
- Precision/recall
- Human approval rate
- Cost per inference