The AI Readiness Academy.
AI readiness is not a platform property. It is a property of the data products that AI agents, copilots, models and decision systems actually depend on. A model is only as trustworthy as the data product feeding it.
This pathway explains how Latttice and Data Tiles measure AI readiness at the data product level — using 37 metrics across eight categories — so business and data leaders know what must be true before a data product can safely support an AI use case.
Data Tiles does not just talk about AI readiness. We measure it.
See AI readiness measured at the data product level
A short demo of Latttice with on-screen placecards — showing how a data product is scored across the 8 categories and 37 metrics, and what AI agents, copilots and decision systems actually see when readiness is measured at the product level.
No narration — the placecards explain each step as the product flows through the readiness framework.
The 8 Categories We Measure
Each category answers a different question an AI system implicitly asks of a data product. Together, they describe what it takes for a data product to be safely consumed by agents, copilots and production AI workloads.
Data Quality
Whether the underlying data is accurate, complete, timely and consistent enough for an AI system to rely on without producing misleading results.
6 metrics measuredSemantic Clarity
Whether terms, fields and values mean the same thing to humans and to AI — so models, agents and copilots interpret the data correctly.
5 metrics measuredFeature Readiness
Whether the data product exposes the features, attributes and aggregations an AI use case actually needs — not just raw tables.
4 metrics measuredDiscoverability & Documentation
Whether AI agents, builders and business users can find the data product, understand what it is for, and know how to use it correctly.
4 metrics measuredGovernance & Policy Enforcement
Whether the right access, privacy and usage policies are enforced at the point an AI system reads the data — not just documented somewhere.
5 metrics measuredLineage & Explainability
Whether you can show where the data came from, how it was transformed, and why an AI system saw what it saw — essential for trust and assurance.
5 metrics measuredInteroperability: Agent & API Readiness
Whether AI agents, copilots, models and applications can actually consume the data product through stable, well-described interfaces.
5 metrics measuredTrust & Usage Signals
Whether the data product earns trust over time — through measurable use, feedback, incidents and the decisions it has actually supported.
6 metrics measuredThe 37 Metrics Behind AI Readiness
The specific metrics inside each category — written for business and data leaders, not just engineers. The aim is shared understanding of what must be true before a data product is safe to put behind an AI use case.
Data Quality
- Accuracy of values against trusted source of truth
- Completeness of required fields
- Timeliness and freshness of updates
- Consistency across systems and time
- Validity against defined business rules
- Uniqueness — absence of unintended duplication
Semantic Clarity
- Business terms are defined and agreed
- Field-level definitions are published and unambiguous
- Code lists and reference values are standardized
- Units, currencies and time zones are explicit
- Synonyms and aliases are mapped
Feature Readiness
- Required features are present and named consistently
- Pre-computed aggregations match decision needs
- Historical depth is sufficient for the use case
- Feature documentation explains intended use
Discoverability & Documentation
- Discoverable in the catalog with clear ownership
- Purpose, scope and intended decisions documented
- Example queries and usage patterns published
- Known limitations and exclusions stated up front
Governance & Policy Enforcement
- Access controls aligned to role and purpose
- Sensitive fields classified and protected
- Policies enforced at query time, not by convention
- Consent and purpose-of-use signals respected
- Audit trail of who and what accessed the product
Lineage & Explainability
- End-to-end lineage from source to product
- Transformation logic is documented and inspectable
- Versioning of schema, logic and reference data
- Change history available to consumers
- Ability to reproduce a past AI input set
Interoperability: Agent & API Readiness
- Stable, documented API or query interface
- Machine-readable schema and contract
- Predictable response shapes and error behavior
- Rate limits and SLAs suitable for AI workloads
- Authentication patterns suited to agents and services
Trust & Usage Signals
- Active consumers and usage patterns
- User feedback and quality signals captured
- Incident history and time-to-resolution
- Decisions and outcomes linked back to the product
- Owner accountability and review cadence
- Adoption by AI agents and copilots tracked over time
Data Tiles does not just talk about AI readiness. We measure it.
Ready to assess your AI readiness?
Use the Data Tiles AI Readiness Assessment to understand how prepared your data products are for AI agents, copilots and production AI use cases.