AI Maturity Assessment
A diagnostic assessment to baseline readiness for governed AI at scale.
This is not a standalone “concept page.” Truzen’s AI Maturity Assessment is a structured, leadership-friendly diagnostic used to establish where you are today, where governance and operating model gaps exist, and what it will take to scale AI responsibly.
Output: a maturity baseline, prioritized gaps, and recommended next steps aligned to the Discover phase and AI Strategy.
Overview
What this assessment is (and what it is not)
What it is
- A diagnostic baseline of AI readiness across strategy, data, governance, risk, and operating model.
- A structured way to identify what is blocking scale (not just technology gaps).
- An entry point into Truzen’s Discover phase within the AI Transformation Framework.
What it is not
- Not a certification or compliance stamp.
- Not a generic scorecard detached from execution reality.
- Not a “strategy deck” without a clear path to prioritization and governance.
The assessment uses a four-level maturity view (Level 1–4) as a simplified leadership language, but the output is diagnostic: what is working, what is missing, what to fix first, and how to sequence the next moves.
Assessment method
How the AI Maturity Assessment works
Inputs
- Leadership objectives and AI ambition
- Current initiatives and portfolio visibility
- Existing governance / risk processes (if any)
- Data foundations and ownership signals
- Operating model realities (roles, decision rights, workflows)
Diagnostic lenses
- Strategy & portfolio governance
- Data readiness and controls
- Governance & risk checkpoints
- Lifecycle practices and evidence
- Operating model and adoption capability
Outputs
- Baseline maturity level (1–4) with dimension-level findings
- Top gaps blocking scale and governance readiness
- Prioritized “next moves” for the Discover phase
- Recommended path into AI Strategy and roadmap prioritization
The assessment is designed to be leadership-friendly and action-oriented: it produces an implementable set of next steps, not just a score.
Maturity levels
The four-level maturity view (used for the diagnostic baseline)
| Level | Label | Short description |
|---|---|---|
| Level 1 | Ad-hoc Experiments | Individual teams run experiments with limited coordination and minimal governance involvement. |
| Level 2 | Structured Pilots | Priority pilots are selected and tracked with early strategy alignment and emerging controls. |
| Level 3 | Scaled & Governed AI | Multiple AI solutions operate with defined lifecycle checkpoints, governance cadence, and oversight roles. |
| Level 4 | Integrated AI Enterprise | AI is integrated into strategy and operating models with repeatable governance, metrics, and evidence of control. |
This maturity view is a directional lens used within the AI Maturity Assessment. It supports structured leadership conversations about “where we are,” “what blocks scale,” and “what should change next.”
Level detail
Level-by-level characteristics (what we look for)
Ad-hoc Experiments
AI work is driven by individuals or isolated teams rather than coordinated enterprise priorities.
- Disconnected proofs of concept
- Weak linkage to enterprise strategy
- Case-by-case data access
- Governance and risk rarely involved early
Structured Pilots
Pilots are selected and monitored with clearer objectives and emerging governance patterns.
- Visible list of priority pilots
- Initial alignment with business objectives
- Identified data sources and owners
- Early governance/risk involvement for higher-impact pilots
Scaled & Governed AI
Multiple AI solutions operate across the organization with defined governance and lifecycle oversight.
- Portfolio view of AI solutions in production
- Defined checkpoints and documentation expectations
- Data governance supports key AI use cases
- Oversight functions have structured roles
Integrated AI Enterprise
AI is integrated into strategy and operating models with repeatable governance and evidence of control.
- AI embedded in planning cycles
- Data and AI governance tightly linked
- Operating model embeds AI roles and workflows
- Leadership uses consistent AI metrics and evidence
Dimensions
Maturity dimensions assessed
The diagnostic baseline is informed by observable patterns across multiple dimensions. Each dimension can progress at a different pace; the assessment provides a consolidated view for leadership discussions and for designing targeted governance, risk, and operating-model interventions.
Strategy & portfolio
How initiatives are prioritized, funded, and tracked against business outcomes, including how value and risk are surfaced.
Data & foundations
Ownership, quality, lineage, access controls, and the reliability needed for explainable and accountable AI.
Governance & risk
Decision pathways, lifecycle checkpoints, approvals, documentation, monitoring, and evidence of control.
Operating model & roles
How responsibilities are distributed across business, data, technology, and oversight functions—and how they interact.
Practices & lifecycle
Consistency of methods across the lifecycle: design, validation, deployment, monitoring, change control, and review.
People & readiness
Leadership and practitioner readiness, oversight comfort, training needs, and evidence interpretation capability.
Connections
How the assessment connects to Discover and AI Strategy
Starts the Discover phase
The AI Maturity Assessment is the entry point into the Discover phase of Truzen’s AI Transformation Framework. It establishes baseline readiness, exposes governance gaps, and identifies the most important constraints to address first.
View the AI Transformation Framework (Discover → Design → Embed → Assure) →Informs AI Strategy prioritization
Strategy choices are constrained by readiness. The assessment helps avoid over-investing in initiatives your data, governance, and operating model cannot yet support. It informs portfolio prioritization and sequencing logic.
Explore AI Strategy (Maturity-driven) →Ready to baseline your AI maturity?
Start with the AI Maturity Assessment to establish readiness and define the next best moves for scaling AI with governance. If you already have a strategy, we can use the assessment to stress-test feasibility and risk exposure.