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“Through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.” — Gartner
“Only 40% of organizations say their data management is highly prepared for AI.” — Deloitte
Each additional system adds an owner, an access path, and a profiling run.
Documents, images, and logs need extraction and labeling checks that tables do not.
Missing common definitions for core entities add reconciliation work before profiling can start.
Personal, health, or financial data adds rights review and often requires on-premises profiling.
Ranking several candidate use cases multiplies the fit and coverage checks per source.
Profiling starts only when samples are available; approval is usually the longest single step.
A kickoff session, profiling on extracts, and a readout session.
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TRAn AI data readiness assessment establishes whether your data can support a specific AI use case, measured on real samples rather than rated by questionnaire. It checks what the data represents, how its quality is measured, whether a machine can interpret it, how fast it can be served, and whether it can be used lawfully and traced to source.
Five stages: fixing the decision the AI must make, inventorying the sources and owners, profiling quality and coverage on real extracts, scoring six dimensions, and sequencing the gaps by what blocks the use case first. You receive a scorecard, a gap register with effort estimates, and a go or no-go read on the use case as scoped.
A general assessment scores four to six organizational pillars, including strategy, people, infrastructure, and governance, usually through 10 to 20 questions, which suits a board conversation but not an engineering decision. This assessment takes the data pillar alone and goes deep enough to say whether one named use case can be built on the data you have; organizational readiness is covered by Zoolatech’s AI strategy consulting.
Free tools from platform vendors are self-completed questionnaires that rely on your own estimate of data quality, and they are published by companies that sell the platform the recommendation points to. This assessment profiles actual data and is not tied to any stack Zoolatech sells.
Timing is driven mainly by the number of source systems in scope and how quickly data access is arranged. Profiling cannot start until samples are available, and that approval is usually the longest single step.
Yes, at least one candidate, because the same dataset can be sufficient for a forecasting model and insufficient for an agent that acts on customer records. If several candidates are open, the assessment ranks them by which your data supports today.
That is a useful outcome and the reason the assessment exists: learning that a use case needs data remediation first is cheaper before the model is built than after. The gap register quantifies what closing each gap involves, so the decision becomes a scoped choice rather than an open-ended risk.
Ask whether the assessment measures data or asks you to rate it, whether the provider sells the platform the recommendation will point to, and whether the people running it have built production systems on data like yours. Public case studies with numbers answer the third question faster than a credentials page.
