Where are you on the Data & AI maturity curve?
12 capability layers, 5 stages and evidence-based scoring — built for enterprises in regulated industries moving from AI pilots to supervised AI agents.
Value arrives late — then fast
The first two stages build capability but return little. Once AI runs inside production workflows on governed data, value compounds. Hover or tab through the stages to see what each looks like in an organisation.
Twelve capability layers, four planes
Data foundation at the bottom; governance and the AI lifecycle cut across every plane. Hover a layer to see what it builds on; select it to open its level descriptors in the matrix.
What each level looks like
Twelve layers across five stages. Move through the stages, open any layer for its indicators, evidence and how SCIKIQ helps — or compare two stages to see exactly what changes.
Organisational enablers
Scored the same way and reported alongside the index — not blended into it. The tracks follow the stage selector above until you pick a level.
Evidence first, then a number
Scores follow the "weakest link" discipline of CMMI and DCAM: a level counts only when it is evidenced. The index, stage and agentic readiness are then calculated the same way for every organisation.
The Maturity Index
A weighted average of the twelve layer scores, rescaled so level 1 is 0 and level 5 is 100.
From index to stage
Index bands place the organisation on the curve; the gating rule then stops weak foundations being averaged away.
Stage ≤ lowest critical layer + 1
Same formula, agent-dependency layers only
Score your organisation in ten minutes
Pick the level whose descriptor you can evidence today, then the level you need in 12–18 months. Results update as you go and stay in this browser only. A self-assessment is indicative — the engagement below validates it with evidence.
Plane averages
Weighted average level per plane (1–5). Bar = current, marker = target.
Your action plan: close these three gaps first
The largest weighted gaps between where you are and where you need to be, each with the first move, what to measure and the evidence to gather.
Six weeks from baseline to roadmap
The self-assessment is a starting point. In the engagement SCIKIQ evidences every score, calibrates it with the layer owners and turns the gaps into a funded, dependency-aware plan.
What you receive
Board-ready outputs, not a questionnaire score.
Know your stage — with evidence
Get a scored heatmap, your Maturity Index and Agentic Readiness, and a first accelerator pilot scoped for your gaps.
Built on established models
The framework combines them rather than inventing a new scale: stages from MIT CISR, evidence-based scoring from CMMI DMM and EDM Council DCAM, governance and lifecycle indicators from NIST AI RMF and ISO/IEC 42001, and the agent and action layers from Microsoft’s agentic AI adoption model.