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Trustworthy AI Maturity Model (TAIMM)

The Trustworthy AI Maturity Model (TAIMM) translates EU AI Act obligations into clear lifecycle questionnaires so research teams, public bodies, and industry can evidence their governance supporting international AI management system standards such as ISO/IEC 42001.

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Sample readiness snapshot

Overall maturity 68%

Developing · Evidence collected across 3 stages

  • Design72%
  • Development64%
  • Operation67%

Scores use the 0-4 maturity scale anchored to EU AI Act obligations and informed by ISO/IEC 42001 lifecycle controls.

Developed by researchers at the ADAPT Centre, University of Galway.

ADAPT Centre logo University of Galway logo

Structured support for compliance teams

Lifecycle coverage

Design, development, and operation questionnaires benchmark progress against Trustworthy AI principles.

Evidence capture

Each response includes a short comment box to document rationale, references, or links to artefacts.

Printable outputs

Results summarise overall maturity, stage-level scores, and action notes for governance follow-up.

Trustworthy AI Maturity Model

TAIMM translates EU AI Act obligations and the Commission's Trustworthy AI principles into structured questionnaires for Design, Development, and Operation. Participants capture evidence inline, lock scoring when complete, and export printable governance packs.

  • Lifecycle-aligned: Design, Development, Operation surveys plus AI system context.
  • Evidence-ready: comment prompts, ISO/IEC mappings, and audit trail.
  • Secure access: activation keys, autosave, and optional researcher debriefs.
TAIMM lifecycle circle showing Design, Development, Operation

About TAIMM

The TAIMM study examines how organisations operationalise the Trustworthy AI principles across the AI lifecycle. It is conducted by Louise McCormack as part of doctoral research through the ADAPT Centre at the University of Galway.

The instrument aligns each question with EU AI Act recitals and the Commission’s seven Trustworthy AI requirements. Findings support evidence-led governance frameworks for public and private sector deployments.

Contact [email protected] for research enquiries.