1. Purpose and Position in the OS
MAT-01 is the central maturity assessment module in the P7 — Legal AI Maturity Mapping pillar. It provides a standardized, defensible way to measure how far a legal function has progressed in adopting and governing AI, and how sophisticated its deployed AI capabilities are.
The model is explicitly two-dimensional:
- Vertical axis — Adoption Stage (S1–S5): Organizational readiness, strategy, governance, implementation scope, and value realization.
- Horizontal axis — Augmentation Sophistication (L1–L4): Depth of AI capability and human–AI collaboration, from Advisor to Agentic Executor.
MAT-01 sits in the ecosystem as:
- GOV-01 → MAT-01 → STR-02 → USE-05 → MAT-01
- It also activates STR-03 and GOV-04 requirements for Level 4 (Agentic Tier) deployments.
The assessment is run at baseline and then quarterly, feeding the Annual Legal AI OS Index and Board-level reporting.
2. Risk Taxonomy 2026 Cross-Walk
MAT-01 embeds specific coverage of the Risk Taxonomy 2026 classes most relevant to legal AI:
- Class 1: Hallucination and accuracy — Assessed via Implementation Scope and Value Realization criteria, focusing on accuracy, validation rigor, and ROAI evidence.
- Class 3: Bias and fairness — Governance Maturity requires GOV-04 bias testing protocol compliance, especially for scaled and Agentic Tier deployments.
- Class 6: Shadow AI — Stage 1 explicitly surfaces shadow AI and defines remediation via AI BoM registration and governance activation.
- Class 7: Regulatory compliance drift — Governance Maturity checks regulatory monitoring and GOV-02 policy alignment.
- Class 9: Operational resilience — Level 4 (Executor) triggers STR-03 Class 9 risk modifier and mandatory Agentic Tier controls.
MAT-01 is therefore a cross-class risk instrument, with particular emphasis on Classes 1, 3, 6, 7, and 9.
3. Adoption Stage Maturity (Vertical Axis)
The Adoption Stage dimension measures how systematically the organization approaches AI. Stages are determined by a 100-point scorecard and mapped as follows:
- Stage 1 (0–19%): Exploring — Pilots in silos, no governance
- Isolated experiments, no AI governance, shadow AI prevalent.
- No AI BoM; vendor AI footprint unknown.
- Advancement requires: AI Task Force, GOV-01/GOV-02, STR-03 risk assessment, AI BoM initiation, basic VEN-02.
- Stage 2 (20–39%): Planning — Strategic alignment, prioritized use cases
- Documented AI strategy, active AI Task Force, prioritized use cases.
- Initial governance framework and GOV-02 approved use categories.
- Advancement requires: structured pilots, DAT-03 data governance, change management, ROAI evidence, integration capabilities.
- Stage 3 (40–59%): Implementing — Formal projects, early ROAI demonstrated
- Structured pilots with metrics, ROAI across multiple use cases.
- Formal change management, DAT-03 alignment, GOV-04 bias testing for all deployments.
- AI BoM current; VEN-03/VEN-04 on new vendors.
- Stage 4 (60–79%): Scaling — Enterprise-wide rollout and governance
- AI Center of Excellence in place; comprehensive governance applied consistently.
- Standardized vendor management with VEN-01 continuous scoring.
- GOV-03 Risk Register and DPS Defensibility Posture Statement operational.
- Stage 5 (80–100%): Realizing — AI embedded, continuous innovation
- AI fully embedded in appropriate workflows; continuous innovation culture.
- Advanced analytics and strategic vendor co-innovation.
- AI BoM integrated with continuous monitoring and quarterly reconciliation.
Governance Maturity is a core criterion within this axis and directly feeds the DPS Defensibility lens.
4. Augmentation Sophistication (Horizontal Axis)
The Augmentation Sophistication dimension measures the technical depth and human–AI collaboration model, independent of organizational maturity.
- Level 1: AI as Advisor — Provides insights and summaries
- Research summarization, document analysis, regulatory monitoring, case law analysis.
- High human involvement; low risk with strong human validation.
- Level 2: AI as Assistant — Automates discrete tasks
- Drafting assistance, data extraction, basic clause generation, matter intake routing.
- Medium human involvement; structured validation and exception handling required.
- Level 3: AI as Co-Creator — Collaborates on complex drafting
- Collaborative contract drafting, brief writing, complex review, regulatory filings.
- Medium human involvement; higher risk, requiring professional oversight and multi-level review.
- Level 4: AI as Executor (Agentic Tier) — Runs workflows under human oversight
- End-to-end workflow execution, constrained automated decisions, process orchestration, real-time compliance monitoring.
- Low human involvement; high risk and mandatory Agentic Tier governance.
Level classification is derived from a 100-point scorecard across Technical Capability, Human–AI Collaboration, Process Integration, and Risk Management.
5. Agentic Tier Governance Requirements (Level 4)
Any Level 4 deployment is designated Agentic Tier AI and must satisfy all of the following before deployment is authorized:
- Kill-switch mechanism — Documented emergency stop protocol with activation within 15 minutes; STR-07 notified on activation.
- Intervention logging — Full logging of AI decisions and exceptions; patterns recorded in GOV-03 Risk Register.
- Scope limitation verification — Clearly defined operational scope; any expansion requires STR-07 approval.
- Escalation protocol — Documented human escalation paths with response SLAs for all exception types.
- Continuous bias monitoring — GOV-04 continuous monitoring and monthly bias audits for all Level 4 workflows.
Failure on any control means the Level 4 deployment is not authorized.
6. Adoption Stage Assessment Methodology
Adoption Stage is scored on four equally weighted criteria (25 points each):
- Strategic Alignment (25%)
- Strategy alignment, executive sponsorship, resourcing, cross-functional collaboration, and regular strategic review.
- Governance Maturity (25%)
- GOV-01 framework, GOV-02 policy, STR-03 risk management, GOV-04 bias testing, AI BoM currency.
- Score 5: DPS operational, GOV-01 deployed, GOV-04 active, AI BoM current, STR-07 escalation, GOV-03 maintained.
- Score 3: Basic framework and GOV-02 in place.
- Implementation Scope (25%)
- Number and breadth of use cases, coverage, integration depth, adoption, and training effectiveness.
- Value Realization (25%)
- ROAI measurement across Protect, Comply, Grow, Transform; baselines, cost reduction, competitive advantage, and continuous optimization.
The total (0–100) maps to Stage 1–5 as defined above.
7. Augmentation Sophistication Assessment Methodology
Augmentation Sophistication is scored on four criteria:
- Technical Capability (30%)
- Technology sophistication, integration complexity, automation level, real-time processing, adaptive learning, and objective performance metrics.
- Human–AI Collaboration (25%)
- Oversight model, collaborative workflows, quality assurance, exception handling, and user experience.
- Process Integration (25%)
- Workflow automation depth, cross-system integration, end-to-end orchestration, monitoring, and continuous improvement.
- Risk Management (20%)
- Risk assessment, automated compliance monitoring, fail-safes, error recovery, and audit trail with explainability.
The total (0–100) maps to Levels 1–4:
- Level 1: 0–24%
- Level 2: 25–49%
- Level 3: 50–74%
- Level 4: 75–100%
8. DPS Lens Mapping
MAT-01 produces structured evidence for three DPS lenses:
- Adoption lens — Adoption Stage score (S1–S5) and quarterly progression.
- Sophistication lens — Augmentation Sophistication score (L1–L4) and oversight model.
- Defensibility lens — Governance Maturity sub-score (within Adoption Stage), including:
- AI BoM currency and coverage.
- GOV-04 bias testing status.
- STR-07 escalation log.
- GOV-03 Risk Register maintenance.
A Governance Maturity score below 3 indicates the function is below the minimum defensibility threshold for regulated and high-risk AI use. Advancement to Stage 3+ requires Governance Maturity ≥ 3.
9. Strategic Archetypes and Grid Positions
Grid positions are denoted as Stage × Level (e.g. S2×L2). Six archetypes guide interpretation:
- Governance-First — High Stage, Low Level: strong governance, limited AI depth; focus on sophistication.
- Technology-First — Low Stage, High Level: advanced AI, weak governance; DPS deficit requiring governance catch-up.
- Balanced Laggard — S1–S2 × L1–L2: early-stage on both axes; build foundations in parallel.
- Balanced Achiever — S3 × L3: mid-stage balance; scale and deepen simultaneously.
- Balanced Leader — S4–S5 × L3–L4: advanced balance; optimize, innovate, and shape standards.
- Agentic Early Adopter — Any Stage × L4: must satisfy Agentic Tier governance before sustaining Level 4.
The canonical progression is “up and to the right”. Stage-leading-Level indicates under-utilized governance; Level-leading-Stage indicates elevated STR-03 risk and DPS gaps.
10. AI BoM Integration and Maturity Gates
AI Bill of Materials (AI BoM) status is a key Governance Maturity indicator and is tied to stage gates:
- Stage 2 entry — AI BoM registry initiated; all current vendor AI systems catalogued.
- Stage 3 entry — AI BoM current; all new vendors complete VEN-03/VEN-04 and are recorded.
- Stage 4 entry — AI BoM includes sub-processors, risk classifications, and GOV-04 completion dates.
- Stage 5 sustaining — AI BoM integrated with continuous monitoring and quarterly reconciliation with contracts.