AI Maturity Assessment

Map your organisation's AI readiness against a model-based maturity matrix. Mark what you have, set where you want to go, and get a dependency-ordered roadmap plus a personalised report.

Assess current Set target
Implemented (valid) Invalid — prerequisite missing Not implemented Target gap Target blocked Target profile
Recommended engagement

Get your personalised report

Receive a PDF report with your maturity profile, gaps, and full dependency-ordered roadmap — plus a tailored engagement recommendation. Felix will follow up personally to discuss the findings.

How this tool works — documentation
The workflow: two steps

Use the toggle at the top to switch between the two steps. Assess current (blue): click every capability your organisation already has in place. Set target (green): flip the toggle and click the capabilities you want to reach. The tool then shows the gap and builds a roadmap.

How to read the maturity levels

Levels are computed, not self-rated. Some capabilities build on others, so the columns (ML 1 upward) reflect a realistic order of maturity: foundational capabilities sit toward the left, and capabilities that build on them appear further right. It keeps the picture honest — you can't be "advanced" in one area while the groundwork it rests on is still missing.

Order checks

If you mark a capability as implemented before an earlier one it builds on, it turns solid red — an out-of-order state worth resolving. In target mode, a target that isn't ready yet shows as blocked. The dependency-ordered roadmap below the matrix always lists your steps in a workable order.

The profile line and roadmap

The green stepped line traces your current maturity profile. Once a target is set, the tool computes a dependency-ordered roadmap, automatically pulling in missing prerequisites.

Scope and limitations

This is a structured, model-based assessment. Expert calibration is ongoing, and the capability set is a working draft. Treat the output as a structured starting point, not a certification.

Method & attribution

This assessment independently applies the Focus Area Maturity Model — a published method — to the domain of AI. The method originates with Sjaak Brinkkemper (maturity-matrix method) and was formalized as the Focus Area Maturity Model by Marlies van Steenbergen et al. (2013). A separate tool applying the same method to Model-Based Systems Engineering was developed in the publicly funded SPEDiT research project at Validas AG (with Andreas Vogelsang, Wolfgang Böhm, Peter Gersing, Oscar Slotosch and Felix Schaller). This AI tool — its capability set, dependencies, implementation and report — was built independently by Felix Schaller on the same public method.

References: van Steenbergen, M. et al. (2013), Improving IS Functions Step by Step: the Use of Focus Area Maturity Models, Scandinavian Journal of Information Systems, 25(2). · Brinkkemper, S. (2014), Situational Assessment with SPM Maturity Matrix.

AI Maturity Assessment & Roadmap — felixschaller.com
Independently applies the Focus Area Maturity Model (van Steenbergen et al., 2013; original maturity-matrix method by Brinkkemper). A separate tool applying the same method to MBSE was developed in the publicly funded SPEDiT project at Validas AG (Andreas Vogelsang, Wolfgang Böhm, Peter Gersing, Oscar Slotosch, Felix Schaller). This AI tool was built independently by Felix Schaller on the same public method.