A DataRev assessment

Measure your Data and AI maturity

Assess where your organisation stands across 8 dimensions, benchmark it against the market, and get a prioritised action roadmap. Free.

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88%

of organisations already use AI in at least one business function.

McKinsey, 2025

95%

of generative-AI pilots fail to deliver measurable business impact.

MIT, 2025

30%

of projects are abandoned after the POC due to poor data quality.

Gartner

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Dimensions

15%

Strategy & Value

Existence, clarity and funding of an AI strategy aligned to business objectives.

15%

Governance, Risk & Compliance

Policies, AI system inventory, risk classification and auditable controls (NIST AI RMF, ISO 42001, EU AI Act).

18%

Data Foundation

Availability, quality, lineage, cataloguing and accessibility of the data feeding AI. The most frequent bottleneck.

14%

Technology & Architecture

Platform, MLOps/LLMOps, model lifecycle, evaluation, observability and integration with core systems.

12%

Talent & Skills

Defined roles, available skills, career paths and the ability to attract and retain AI and data profiles.

10%

Culture & Adoption

Real use of AI in daily work, change management, trust and the absence of shadow AI.

8%

Operating Model & Process

How AI demand is captured, prioritised, built and run. Covers intake, use-case prioritisation and production support.

8%

Measurement & Value Realization

Baseline, KPIs, benefit attribution and audit of the value actually captured from AI.

5 maturity levels

  1. 1

    Initial

    Isolated, reactive efforts. Outcomes depend on specific individuals, not on the organisation.

  2. 2

    Repeatable

    Local good practice repeated by some teams, but without consistency or enterprise reach.

  3. 3

    Defined

    Policies, roles and processes defined and communicated enterprise-wide. Capability no longer depends on the team.

  4. 4

    Managed

    Everything is measured and tied to business objectives and risk analysis. Managed with data, not opinions.

  5. 5

    Optimized

    Continuous improvement loop with feedback and feed-forward. AI reshapes the business model.

Framework sources

  • MIT CISR — Enterprise AI Maturity Model (n=721)
  • Gartner — AI Maturity Model
  • NIST — AI Risk Management Framework 1.0
  • ISO/IEC 42001 — AI Management System
  • CMMI Institute — Data Management Maturity

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