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08/07/2026 7:02 pm
TL;DR: A flat, balanced AI maturity profile in the upper bands can mask stalled delivery, because capability scores say little about whether teams are willing to surface weak data, unclear ownership, or failed experiments, according to Matrix42. The real governance problem is not the score shape, but the absence of honest signal behind it.
NHIMG editorial — based on content published by Efecte: AI maturity assessment scores in service management: when the profile is flat and what to ask alongside it
Questions worth separating out
Q: How should teams interpret a flat AI maturity score profile?
A: Treat a flat profile as a prompt for evidence review, not as proof of balance.
Q: Why can a strong-looking maturity score still miss governance problems?
A: Because maturity scores measure what respondents say exists, not how candidly the team handles weakness, disagreement, or stalled work.
Q: What do organisations get wrong about AI readiness assessments?
A: They often treat readiness as a number instead of a conversation about evidence.
Practitioner guidance
- Challenge symmetry in maturity scores Ask for evidence that each dimension is independently validated, especially when all four scores sit within a narrow band.
- Require named stewardship for stalled work Assign a specific owner to every deferred AI or knowledge-base item, then review whether that owner can explain the delay and the next decision point.
- Pair self-assessment with candid challenge sessions Run the assessment in a room where a peer or senior leader is expected to disagree with at least one rating.
What's in the full article
Matrix42's full post covers the discussion prompts and scoring context this analysis intentionally leaves for the source:
- The full question-by-question follow-up guide for turning a flat maturity profile into a leadership conversation.
- The worked example showing how evenly distributed responses produce a balanced score across operations, data, governance, and enablement.
- The specific prompt wording used to probe data quality, transparency, measurable outcomes, and reusable templates.
- The practical explanation of why a balanced score should trigger a second conversation rather than a celebration.
👉 Read Matrix42's analysis of flat AI maturity scores in service management →
AI maturity assessment flatlines: what are teams missing?
Explore further
08/07/2026 7:25 pm
Flat maturity is a governance smell, not a success pattern. When four AI rollout dimensions sit within a few points of one another, the programme may be balanced, but it may also be protecting itself from scrutiny. In identity and service governance, real progress usually creates unevenness because some controls harden faster than others. Practitioners should treat perfect symmetry as a prompt to inspect the evidence trail behind each score.
A few things that frame the scale:
- 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with their human identity and access management efforts, according to The 2024 Non-Human Identity Security Report.
- Another 35.6% of organisations cite managing consistent access across hybrid and multi-cloud environments as their top NHI security challenge, which helps explain why evenly scored governance programmes still fail in execution.
A question worth separating out:
Q: How can leaders tell whether a maturity score reflects real progress?
A: Look for operational proof: a named steward, a measurable service change, a documented decision, and a recent challenge to the original assumption. If the score cannot be tied to a concrete improvement in production behaviour, it is probably capturing sentiment or internal consensus more than maturity.
👉 Read our full editorial: Flat AI maturity scores can hide weak service management trust
