Internal prototype. Synthetic data. Provisional methodology. Not for client use.
PCG AI Maturity PlatformCross-Practice Concept Prototype
Internal concept prototype · synthetic data · methodology in development

One AI maturity platform. Four PCG practices.

A cross-practice assessment capability that combines shared methodology with sector-specific evidence, language, and client deliverables.

Start with the organization's AI goals. Measure the capabilities required to support them. Turn evidence-backed gaps into an actionable path forward.
Decision requested: align the four practices around one shared instrument and nominate one subject-matter lead from each practice to refine the practice-specific content.
How the capability works

From ambition to measurable improvement

The platform is designed around a repeatable consulting journey rather than a questionnaire and a single score.

AI ambitionWhat does the organization want AI to do?
AssessmentWhat capabilities and controls exist today?
EvidenceWhat can the organization substantiate?
ActionWhich gaps constrain the stated ambition?
ReassessmentWhat changed and what remains?
Why the model is different

Three design choices shape the platform

Direction before diagnosis

Maturity is interpreted against the client's intended use of AI. In the Education worked example, intent is Determinative while the current assessment supports Advisory.

Evidence before confidence

Consultants record the initial answer, probe for substantiation, and capture evidence strength. Missing knowledge becomes a coverage gap rather than an invented number.

Gates before averages

Critical control failures cap supported AI authority. A strong score elsewhere cannot compensate for an inability to trace, inspect, override, monitor, or stop a system.

Cross-practice design

Shared platform, practice-specific expression

Shared assessment coreCommon dimensions, evidence approach, gate logic, findings structure, roadmap structure, and reporting method.
Practice-specific contentSector questions, organization types, examples, evidence expectations, terminology, and report language.
Client-facing deliverablesGoals and ambitions, maturity domains, constraints, findings, roadmap, and limitations presented in the language of the selected practice.
Methodological caution

Maturity is a profile before it is a single number

Domain-level results are the primary interpretation in this prototype. The application can calculate an overall composite, but PCG has not yet established that the domains should be collapsed into one validated construct.

Pilot data must determine whether an aggregate score preserves meaningful differences, masks critical weaknesses, requires weighting, or should be omitted entirely.