Generative AI is reshaping analytics, BI, and decision-making. But without a GenAI-ready semantic layer, enterprises face unreliable metrics, costly queries, and AI agents that produce untrustworthy results.
This free readiness checklist helps you evaluate your data foundation, score your current capabilities, and identify the next steps to successfully deploy agentic AI, natural language query (NLQ), and enterprise analytics at scale.
What’s Inside the Checklist
✔ Semantic Modeling & Definitions – ensure consistent business metrics and standardized dimensions
✔ Query & Integration – evaluate APIs, SQL access, and interoperability with GenAI frameworks
✔ Performance & Optimization – test for caching, aggregates, and cost-aware query execution
✔ Security & Governance – confirm role-based controls, data masking, and audit logging
✔ GenAI-Specific Features – check for NLQ, ontology APIs, safety guardrails, and hallucination detection
✔ Developer & Operations Experience – assess CI/CD, observability, and migration tooling
Each section maps directly to the capabilities enterprises need for GenAI adoption. Count your score to see if you’re ready.
Why a GenAI-Ready Semantic Layer Matters
A modern semantic layer isn’t just about BI dashboards; it’s the governed semantic backbone for:
- Delivering consistent business logic to AI agents and LLMs
- Enabling explainable and governed AI outputs
- Preventing vendor lock-in with open, interoperable semantics
- Scaling AI-driven analytics without sacrificing trust or performance
With the right foundation, enterprises can accelerate self-service analytics, trusted AI, and cross-platform interoperability.
Next Steps
Download the Checklist to see your readiness score.
Schedule a Demo to see how AtScale’s Universal Semantic Layer delivers trusted, explainable GenAI at enterprise scale
