How we built the
AI Maturity Assessment

The problem with AI conversations at the leadership level

Most leadership teams are having one of two AI conversations, and neither one is particularly useful.

The first is aspirational: AI is going to transform everything. We need to move faster. Why aren't we doing more? The second is reactive: We tried a pilot. It didn't work. We're being careful.

What's missing from both is an honest assessment. Not a vendor briefing, not a consulting firm's maturity model designed to sell the next engagement — but a clear-eyed look at where the organization actually is across the dimensions that determine whether AI investments produce returns.

The fundamental error in most AI strategy conversations is treating AI maturity as a single variable. It isn't. An organization can have a sophisticated data infrastructure and a completely absent governance framework. It can have strong executive vision and no operational embedding. The shape of the gap matters as much as the size of it.

The five dimensions

The assessment examines five dimensions that, together, determine whether AI investments produce durable organizational returns. They are not equally weighted in importance — but they are all necessary. A weakness in any one creates a ceiling on the others.

The five-level framework

Each dimension is scored on a 1–6 scale derived from the average of four Likert-rated questions. The overall score is the average across all five dimensions. Scores are translated into a maturity level — not to label, but to locate. The level tells you where you are, which determines what comes next.

How scoring works

Each of the 20 items uses a 6-point Likert scale with no neutral midpoint, anchored from "Strongly disagree" (1) to "Strongly agree" (6). Removing the neutral option forces a directional response — respondents must lean one way or the other, which produces more useful signal. Higher scores always indicate stronger capability — no reverse-coded items are used, because the research evidence that reverse-coding reduces response bias is weak, while the evidence that it increases confusion is strong.

The per-dimension score is the arithmetic mean of the four questions in that dimension. The overall score is the mean of the five dimension scores. Level boundaries are:

• Level 1 (Exploring): score < 2.2
• Level 2 (Piloting): 2.2 – < 3.2
• Level 3 (Scaling): 3.2 – < 4.2
• Level 4 (Optimising): 4.2 – < 5.2
• Level 5 (Leading): score ≥ 5.2

The two lowest-scoring dimensions are surfaced as your priority areas. The fastest path to overall maturity improvement is closing the floor, not raising the ceiling.

What you get

Results are immediate — your full profile displays the moment you submit. No waiting for a report to be emailed or a follow-up call scheduled.

• An overall AI maturity level (1–6 score, translated to a 5-level framework) with a plain-language description of what it means in practice
• Individual scores for all five dimensions, displayed as a radar chart and bar breakdown
• Role-specific insight — what the scores mean for a CEO is different from what they mean for a CTO or COO
• Your two highest-priority dimensions, with specific context on why they're limiting overall progress
• A future-state target you set for each dimension, so results anchor a 12-month planning conversation
• A copy of your results emailed to you for sharing with your leadership team