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Module USE-02 sigil: Use Cases pillar, Strategy layer, maturity bands 1 to 3.Deterministic sigil for Module USE-02. The Pillar geometry encodes Use Cases (Pillar 5); the top-right marker S encodes the Strategy layer; the baseline meter encodes maturity bands 1 to 3.SUSE-02
P5· L-E· Bands Operational → Integrated

· USE-02

Pilot Program Design

Pilot Program Design defines how Legal AI use cases move from selection to validated production. The Module sets entry criteria, control conditions, measurement schedule, success thresholds, and exit gates for any AI pilot — whether a contract-review tool, a research assistant, or an agentic workflow. Without disciplined pilot design, the function cannot distinguish AI that works from AI that performs well in a demo. The Module produces the pilot brief, the measurement plan, and the go/no-go evidence package that authorises scaling or sunsets the use case. Methodology v2026.1.

strategic

·

Per-engagement

·

90–180 days per pilot (2–4 weeks planning; 6–12 weeks implementation; 2–4 weeks evaluation)

Methodology v2026.1·Verified 22 May 2026·Reviewed 22 May 2026

Executive Summary

This Module provides a structured, defensible approach for designing and running AI pilots in legal departments. It translates prioritised use cases from USE-01 into tightly scoped pilots with clear governance, KPIs, and escalation paths. The three-phase methodology—Planning and Preparation, Implementation and Monitoring, and Evaluation and Scaling Decision—ensures that each pilot is registered in the AI Bill of Materials (AI BoM), risk-assessed against the Risk Taxonomy 2026, and monitored using GOV-03 and GOV-05. Within 90–180 days, teams can evidence Return on AI Investment (ROAI) across STR-08’s four quadrants, generate DPS-grade artefacts for the Adoption and Sophistication lenses, and make disciplined scaling decisions. The Module includes role definitions, review cadence, success thresholds, and ready-to-use templates for the Pilot Charter, KPI Dashboard, and Post-Pilot Evaluation Report, enabling repeatable, auditable AI experimentation at enterprise scale.

Defensibility Evidence Produced

Phase 3 evaluation report constitutes DPS Adoption and Sophistication lens evidence; Risk Taxonomy 2026 monitoring across all 9 classes throughout Phase 2; AI BoM registration required as Phase 1 gate before any deployment.

Elements:

Decision traceabilityEvidence framework

1. Module Purpose and Context

This Module operationalises AI pilots for legal departments using a disciplined, defensible framework. It is used after USE-01 (Use Case Prioritization Matrix) and before USE-03 (Execution Roadmap).

Objectives:

  • Design AI pilots that prove value within 90–180 days.
  • Minimise legal, regulatory, and operational risk using Risk Taxonomy 2026 and GOV-03.
  • Ensure every pilot is registered and governed via the AI Bill of Materials (AI BoM).
  • Generate STR-08 ROAI evidence and DPS artefacts for Adoption and Sophistication lenses.

Scope:

  • Applies to each new AI use case selected in USE-01.
  • Applies to major AI system implementations, including agentic tools.

2. Three-Phase Pilot Methodology

Phase 1: Planning and Preparation (2–4 weeks)

Strategic objectives

  • Define pilot scope, objectives, and success criteria aligned to USE-01.
  • Assemble cross-functional pilot team with clear roles and accountability.
  • Establish baseline metrics for all STR-08 quadrants (especially Q1 and Q2).
  • Register the AI vendor and system in the AI BoM (gating prerequisite).
  • Complete Risk Taxonomy 2026 assessment and populate GOV-03 entries.

AI BoM Registration Gate

No Phase 2 deployment is authorised until:

  • AI system card is completed (vendor, model version, capabilities, data processing).
  • Data processing terms are reviewed and accepted.
  • Security certification is confirmed (SOC 2 Type II or equivalent).
  • GOV-02 AI Use Policy scope is confirmed (approved use category).
  • Agentic Tier flag is set if the tool executes multi-step tasks autonomously.

Critical Phase 1 deliverables

  • Pilot Charter Document (see Section 5 template).
  • Risk Taxonomy 2026 assessment across all 9 classes for the use case.
  • GOV-03 Risk Register entries for Classes 2, 4, and 7 at minimum.
  • AI BoM registration confirmation for the pilot vendor.
  • Baseline metrics for all STR-08 tracking dimensions.
  • Success criteria and ROAI targets with executive sign-off.

Phase 1 success criteria

  • Executive sponsorship secured with budget and resources.
  • Pilot charter approved by all stakeholders.
  • Baseline measurements documented and linked to STR-08.
  • AI BoM registration confirmed.
  • GOV-03 Risk Register updated for this use case.
  • Risk Taxonomy 2026 assessment completed.

Operational Signals

use-02.pilot-go-no-go-rate

→ Defensibility Posture Statement

Pilot decisions made on schedule against gate criteria — DE-1 Decision traceability record.

Quarterly

use-02.measurement-fidelity

→ Annual Legal AI OS Index

Pilot measurement plan completeness against canonical template feeds the Annual Legal AI OS Index.

Per Module run

use-02.time-to-decision

→ Console

Mean time from pilot start to go/no-go decision for Console intelligence substrate.

On change

Recommended Stakeholders

Owner

  • Head of Legal Operations

Approvers

  • Head of Legal Operations
  • General Counsel

Contributors

  • AI Task Force
  • Engineering / IT

Informed

  • Risk & Compliance
  • Finance Partner

Inputs · Outputs

Inputs

  • · Prioritised AI use cases from USE-01
  • · Current-state process maps and baseline performance data
  • · GOV-02 AI Use Policy and approved use categories
  • · Risk Taxonomy 2026 framework and prior incident history
  • · Vendor documentation, security certifications, and data processing terms
  • · Existing GOV-03 Risk Register entries and templates
  • · STR-08 ROAI Matrix Framework and metric definitions
  • · Organisational DPS model and lens definitions
  • · AI Bill of Materials (AI BoM) structure and registration process

Outputs

  • · Approved Pilot Program Charter for each AI use case
  • · Completed AI BoM system card and registration for the pilot vendor
  • · Risk Taxonomy 2026 assessment across all nine classes for the use case
  • · Updated GOV-03 Risk Register entries for relevant classes (2, 4, 7 minimum)
  • · Baseline and in-flight KPI data mapped to STR-08 quadrants
  • · Weekly Pilot Performance Dashboards and risk incident logs
  • · Phase 3 Post-Pilot Evaluation Report with four-quadrant ROAI results
  • · DPS evidence package for Adoption and Sophistication lenses
  • · Formal scaling decision (Immediate / Conditional / Delayed / No-Go) with rationale
  • · Lessons-learned log feeding future USE-01 scoring calibration

Framework Crosswalk

NIST AI Risk Management Framework

NIST

Phase 1–3 pilot controls and risk monitoring align with NIST AI RMF functions of Govern, Map, Measure, and Manage.

ISO/IEC 42001 Artificial Intelligence Management System

ISO

Pilot governance, AI BoM registration, and continuous monitoring support ISO 42001 requirements for operational control and performance evaluation.

EU AI Act (enterprise readiness perspective)

Risk-class monitoring, incident response, and documentation of AI system characteristics help prepare for obligations on high-risk and general-purpose AI systems.

ABA Model Rules 1.1, 1.6, 5.3

American Bar Association

Class 2, 4, and 7 controls and attorney oversight of AI outputs support competence, confidentiality, and supervision duties.

Operational Artefacts

  • Pilot Program Charter Template

    docx · v2026.1

    Gated

  • Pilot KPI Tracking Dashboard

    xlsx · v2026.1

    Gated

  • Post-Pilot Evaluation Report Template

    docx · v2026.1

    Gated

  • AI Pilot Risk and Incident Log (Risk Taxonomy 2026)

    xlsx · v2026.1

    Gated

  • Untitled artefact

    asset · v2026.1

    Gated

Sequence

Run before

Run next

Diagnostic Relevance

Running Pilot Program Design strengthens the Adoption lens — expected Band progression: Operational → Integrated.

Confidence: high

Key Takeaways

  • Translate prioritised AI use cases into controlled pilots with clear scope, KPIs, and governance.

  • Use a three-phase methodology to move from planning to implementation to scaling decisions in 90–180 days.

  • Gate all pilots through AI BoM registration and Risk Taxonomy 2026 assessment before deployment.

  • Continuously monitor all nine risk classes and trigger GOV-05 for defined incident thresholds.

  • Measure ROAI using STR-08’s four-quadrant framework with baselines captured in Phase 1.

  • Produce DPS-grade evidence for Adoption and Sophistication lenses from Phase 3 outputs.

  • Apply a scaling decision matrix (Immediate, Conditional, Delayed, No-Go) based on must-have thresholds.

Run this Module

Operational artefacts available to Practitioner Membership members. Methodology v2026.1.

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Targeting

Audience

GC / CLOLegal OperationsRisk & Compliance

Strengthens

Adoption lensSophistication lens

Module Details

Format
Module
Difficulty
Operational
Pillar
P5
Layer
E · Execution
Owner
Head of Legal Operations
Access
Practitioner Membership
Certification
Practitioner

Maturity Bands

OperationalIntegrated

Risk Classes Mitigated

Operated within

In the Ecosystem

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Related Modules

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USE-01

P5

Use Case Prioritization Methodology

The strategic use case selection instrument that evaluates AI opportunities across five dimensions including Risk Taxonomy 2026, gates vendor adoption via AI BoM, and connects the deployed portfolio to DPS Adoption lens evidence.

USE-03

P5

Baseline Metrics Methodology

Establishes pre-implementation performance benchmarks across four measurement dimensions to enable objective ROAI demonstration and AI optimisation for legal departments.

VAL-01

P1

ROAI Matrix Framework

Measure and demonstrate the return on AI investment across four value dimensions — operational, financial, cultural, and strategic — with direct linkage to the DPS Defensibility lens.

BUS-01

P1

Business Case and Cost of Inaction

The foundational investment approval instrument that quantifies legal AI's cost of inaction, builds CFO-grade ROAI projections, and authorises the governance and measurement machinery.

Referenced in Intelligence

Where this Module is anchored

Canonical Vocabulary

Terms this Module anchors

ROAI 4-Quadrant

The canonical value lens for measuring and communicating Return on AI Investment in legal functions. The 4-Quadrant maps outcomes across four dimensions: efficiency, quality, risk reduction, and strategic value. 'ROI' alone is forbidden in AI contexts; 'ROI 4-Quadrant' and 'Value 4-Quadrant' are forbidden synonyms. ROAI (Return on AI Investment) is the canonical term.

AI Lifecycle

The canonical five-stage sequence governing any AI use case from inception to retirement — Concept, Build, Deploy, Operate, Sunset. The AI Lifecycle provides the operational frame for governance, risk management, and performance measurement across any use case. It applies at both the individual tool level and the portfolio level. 'Project lifecycle' and 'AI project lifecycle' are forbidden synonyms.

ROAI

The four-quadrant return framework for AI investment in legal functions, comprising productivity value, Defensibility value, institutional value, and category positioning value. Productivity-only cases underweight the investment by a factor of three or four; the full ROAI frame presents the board with the complete case for funding AI at institutional scale.

Where this Module lives

Pilot Program Design is the validation gate between use-case selection and full deployment. Its measurement plan draws baselines from USE-03 and feeds outcomes into VAL-01 ROAI scoring. Pilot evidence becomes a DE-1 (Decision traceability) record in the Defensibility Posture Statement. Without this Module, pilots produce anecdote, not evidence.

Advisory

When this Module sits inside a Programme.

Modules are operated in-house by GC and Legal Operations teams. When the capability transformation is multi-Pillar — or when the regulator timeline tightens — Advanta operates the canonical Module sequence as a Programme.

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