AI data readiness assessment

60% of AI projects are abandoned without AI-ready data. We find out if yours is — before you spend on a pilot.

Why first

The unglamorous reason most AI projects fail

Analyst data is blunt: a majority of stalled AI projects lack AI-ready data, and more than half of GenAI initiatives are abandoned after proof of concept. The model is rarely the problem — the data layer is. Four systems of record, duplicate records, missing permissions, no evaluation set: that's what kills pilots.

Our assessment is the cheapest way to de-risk an AI programme. It costs a fraction of a pilot and tells you whether to proceed, what to fix, and in what order.

Scope

What we assess

Quality

Duplicates, gaps, stale records, and inconsistent formats across your systems of record.

Permissions

Who can see what. AI agents inherit your permission model — if it's undocumented, the agent is unsafe.

Structure

Whether documents and records are retrievable in chunks an LLM can cite — or a swamp.

Volume & freshness

Enough data to ground answers, current enough to trust, and flows to keep it updated.

Integration surface

Which systems the agent must read and write, and how clean those APIs actually are.

Governance

Audit trails, evaluation sets, and the compliance constraints your industry imposes.

Process

Five steps, two weeks, one honest report

1. Inventory

Map the systems, documents, and flows your target use case depends on.

2. Profile

Sample and score the data: quality, duplication, coverage, sensitivity.

3. Gap report

A scored list of what blocks AI — ranked by impact on your use case.

4. Roadmap

A remediation plan with effort estimates: what to fix before any pilot.

5. Re-score

A go / fix-first / no-go verdict with the numbers to back it.

Deliverables

Readiness scorecard, gap list, remediation roadmap, and a fixed-scope pilot estimate if you're clear to proceed.

Fix the foundation before the demo

A two-week assessment that tells you honestly whether your data can support AI — and exactly what to fix first.

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FAQ

Frequently Asked Questions

What is AI data readiness?

AI data readiness is the state where your data is clean, structured, permissioned, and current enough for AI systems to reason over reliably. Analysts attribute most stalled AI projects to missing AI-ready data — it's the step before any pilot.

How long does the assessment take?

A focused assessment for one use case takes about two weeks: inventory, profiling, gap analysis, and the final scored report with a remediation roadmap.

What do we get at the end?

A readiness scorecard across quality, permissions, structure, volume, integration and governance; a ranked gap list; a remediation roadmap with effort estimates; and a fixed-scope pilot estimate if you're clear to proceed.

Can't we just start the pilot and fix data later?

You can — that's what the 50%+ abandonment statistic is made of. Fixing data after a pilot means re-doing integration and evaluation. Fixing it first is cheaper and faster.

Does the assessment include the pilot?

No — it's deliberately separate. You get an independent verdict first. If you proceed, the pilot is scoped against the remediated data, which is why our pilots survive.