Trustgent
Free tool

AI readiness assessment

Ten questions, five to eight minutes, no login. Score across the ten dimensions AI-readiness practitioners actually check — from data to governance to executive sponsorship — and get tier-specific next steps. Built by Trustgent so buyers can decide when to procure and what to procure for.

0 / 10 answered
1.How well-defined is the AI use case you want to build?

A concrete problem beats a broad ambition — this dimension weighs most against successful delivery.

2.How ready is the data the AI system will use?

Data readiness is the single most common delivery blocker in enterprise AI programmes.

3.What is your technical infrastructure posture for AI workloads?
4.What in-house AI/ML capability do you have today?

You do not need a full team — but you need someone accountable for the technical decisions the provider will not make for you.

5.Do you have an AI governance framework or policy?

For EU AI Act deployers and regulated-sector buyers, this is not optional past 2026.

6.What is the regulatory context for the intended AI system?
7.How strong is the executive sponsorship for this AI initiative?
8.How prepared is the organisation to change process around the AI system?

AI systems that require no process change tend to be automations, not transformations.

9.What is the budget posture for the AI initiative?
10.How are success metrics defined for the AI system?

A system without pre-agreed success metrics tends to end up judged on the sponsor's mood on the day of the review.

What the assessment measures

The ten dimensions align with the risk and readiness factors that recur across published AI-readiness frameworks, the NIST AI Risk Management Framework's four functions, and the delivery-blocker patterns documented in enterprise AI failure post-mortems. Each dimension has a specific reason to appear: skipping it correlates with a well-understood failure mode in AI programmes.

  1. Business-case clarity

    Concrete use case with a defined outcome and success metric.

  2. Data readiness

    Inventoried, accessible, quality-checked, governance in place.

  3. Technical infrastructure

    Cloud posture, MLOps maturity, model-serving and observability.

  4. In-house talent

    Named accountable owner for the technical decisions the provider will not make for you.

  5. Governance

    Written AI policy, risk-assessment process, alignment with ISO/IEC 42001 or NIST AI RMF.

  6. Regulatory context

    EU AI Act risk-tier classification, HIPAA BAA scope, DORA financial-sector obligations mapped.

  7. Executive sponsorship

    C-level accountability, budget agreed, quarterly reviews.

  8. Change-management capacity

    Process redesign scoped alongside the technical build, not after it.

  9. Budget posture

    Approved through pilot into production, not just pilot alone.

  10. Success metrics

    Quantitative KPIs with baselines and contractual acceptance criteria.

After the assessment

The score is a starting point, not a verdict. Whichever tier the result places you in, the next move is grounded in evidence rather than provider marketing.

  • Vendor-evaluation scorecard. The ten dimensions a buyer should score three-to-five shortlisted providers against, weighted before the sales cycle shifts them.
  • AI-DD checklist. A due-diligence checklist for the artefacts a provider should be able to produce before a build engagement begins.
  • Verified providers. Filter the directory by capability and verification level. L2 and above have been cross-referenced against sources outside the provider's control.
  • Methodology. Read the six-level verification spectrum so the tier you filter to matches the evidence bar you actually need.

Frequently asked

What is an AI-readiness assessment?
An AI-readiness assessment measures how prepared an organisation is to successfully deliver an AI system into production. It typically evaluates business-case clarity, data readiness, technical infrastructure, in-house talent, governance, regulatory context, executive sponsorship, change-management capacity, budget commitment, and success-metric definition.
How long does the Trustgent AI-readiness assessment take?
About five to eight minutes. Ten multiple-choice questions with four options each. There is no login and no email capture on the tool.
What do the readiness tiers mean?
Four tiers based on a 0-30 score. Early exploration (0-9) means foundations first, do not procure yet. Emerging (10-17) means ready for a discovery engagement but not yet a build. Ready (18-24) means the inputs for a real build are in place and provider fit becomes the primary risk. Advanced (25-30) means procurement can focus on delivery evidence — L3 customer-rated, L4 analysed-projects, L5 outcome-verified providers.
Is the assessment result saved?
No. The scoring runs entirely in your browser; closing the tab loses your progress. This is intentional — the value of the assessment is the conversation it enables, not a captured lead.
What frameworks does the assessment draw on?
The ten dimensions align with the risk and readiness factors that recur across published AI-readiness frameworks: the NIST AI Risk Management Framework's GOVERN / MAP / MEASURE / MANAGE structure, ISO/IEC 42001's management-system requirements, and the delivery-blocker patterns documented in enterprise AI failure post-mortems.
Should our organisation use this before speaking to providers?
Yes. Providers who are useful to work with will not be defensive about a low score — they will use it to structure a discovery engagement that closes the gaps efficiently. Providers who insist that they can build without addressing the open dimensions are surfacing a signal you should notice.