Your agents won’t fail because the model is weak. They’ll fail when they act on uncertified data. Every agent runs on a five-layer stack: sources, storage, a trust layer, orchestration, and the apps where agents act.

This assessment measures your AI data readiness. In just about 5 minutes, you'll get:

A data management maturity score
A benchmark against peers in your industry
Your specific risk exposure
Prioritized plan you can put in front of your board

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A note on scope

Full AI readiness is bigger than data — talent, operating model, executive sponsorship, budget, and more all matter, and any one of those can stall a program. This assessment focuses on data and the 3 foundational layers that decide whether everything above it can be trusted.

Answer these questions to get a personalised
AI data readiness assessment

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AI data readiness assessment Overall progress

Context questions

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Layer 1 — Data sources

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Layer 2 — AI data cloud

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Layer 3 — The trust layer

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Context questions

Which best describes your industry?

Financial services Insurance Life sciences & pharma Utilities & energy Healthcare Retail & consumer goods Technology Other

Which best describes how data management actually runs in your organization today?

Ad hoc — reactive, no SLAs Foundational — one documented path, a named approver, basic tracking Federated — risk scoring, tiered SLAs, ownership per domain Systematic — automated, measured, continuously optimized

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Layer 1 — Data sources & origination

Your data is born in structured systems (ERP, CRM, billing) and unstructured sources (documents, emails, logs). This layer measures whether AI can reach it, and whether quality is caught at the source, before problems spread.

Can AI access your core operational systems (ERP, CRM, finance, billing, etc.)?

No — agents can't reach them Yes — but only via manual or ad hoc extracts Yes — agents query them directly, without governance Yes — through a governed access layer, not raw system querying

Are data ingestion pipelines automated (i.e. resume automatically after extraction failures or wait for manual intervention) and monitored (i.e. observed for schema changes/failures or only basic failures)?

Manual or one-off Some automation, little monitoring Mostly automated, basic monitoring Fully automated and continuously monitored

Where in the lifecycle are data quality issues typically caught and fixed?

Downstream, after they cause a problem In the warehouse, case by case During ingestion, with some automated checks At the source systems, before they propagate

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Layer 2 — AI data cloud

Powerful AI needs an engine that can run it. This section gauges whether your data platform can support AI at scale — consolidating your data and handling the compute, retrieval, and processing that agents demand.

How consolidated is your data onto a modern cloud platform (e.g., Snowflake, Databricks)?

Scattered across silos Consolidation underway Most AI-relevant data consolidated Fully consolidated on a scalable platform

Is promotion to the AI-ready (gold) layer gated by quality certification, or does data advance with the assumption that it was loaded cleanly?

No formal promotion; AI uses whatever is available Advances once it loads cleanly Some manual review before AI-ready Gated by automated quality and certification checks

Do the governance features built into your cloud data platform (e.g. access controls, lineage tracking, metadata tagging) also guarantee the data itself is accurate and trustworthy?

We assume platform/catalog governance means trustworthy We rely on platform-native controls We supplement with some quality checks We layer dedicated quality and certification on top

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Layer 3 — The trust layer

This is the layer that decides whether your data can be acted on safely. It measures whether your data is proven, trusted, and fit for purpose before an agent ever touches it.

Before an agent acts, can you prove its data is trustworthy — and is that proof machine-readable so agents can check it themselves?

No proof of trust; agents use whatever is in the tables they consume Ad hoc checks when something looks wrong Defined quality rules on critical datasets, metadata exists but agents don't use it Continuously measured and governed, queryable by agents

When a quality issue is detected, what happens?

Noticed only after it causes a downstream problem Flagged, but remediation is manual and slow Routed to owners, but observation outpaces resolution Routed to owners with clear root cause for fast resolution

Is reference data centrally governed and consistent across systems?

Each system keeps its own codes and lists Some shared lists, reconciled manually in files Governed reference data in key domains Centrally managed, versioned, consistent everywhere

Are critical entities resolved to a single authoritative record, with duplicates detected and resolved automatically?

Conflicting records coexist, and we accept it De-duplicated reactively, in limited cases Mastered records in some domains Authoritative master records everywhere, duplicates auto-resolved

If an AI system encounters conflicting versions of the same entity, what happens?

It uses whichever record it finds first It may flag it, but there's no authoritative source A human can resolve it, but not in real time The trust layer resolves to the authoritative record before the agent acts

Does every AI-critical dataset and discrepancy have a named, accountable owner?

Ownership is unclear or contested Ownership exists on paper but inactive Named stewards, but inconsistent resolution Named owner with a resolution history for every dataset and discrepancy

Are retention, classification, and access policies enforced with auditability built in?

Applied inconsistently or manually Defined but not consistently enforced or logged Enforced in regulated domains, with partial auditability Consistently enforced across the estate, with auditability built in

Can data lineage be traced end-to-end for the datasets agents rely on?

Lineage can't be reconstructed Partial lineage, with significant manual effort End-to-end lineage for critical datasets Full, explainable end-to-end lineage across AI-relevant data

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