Where are you on the Data & AI maturity curve?
12 capability layers, 5 stages and evidence-based scoring — built for telecom operators moving from AI pilots to supervised AI agents.
Becoming an AI-native operator is a journey, not a purchase. Knowing your stage — with evidence — tells the board what to fund first, which layers gate value, and when agents can safely act on BSS and network systems.
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 operator.
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 DaasLabs 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 operator.
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 operator 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 operator 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.
Top 3 gaps to close
Largest weighted gaps between target and current; critical and foundation layers come first on a tie.
Six weeks from baseline to roadmap
The self-assessment is a starting point. In the engagement DaasLabs 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 Gartner and 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.