No owner, no metric, no tier
Use cases start without a business owner or risk tier, then stall at the first risk review.
AI transformation consulting
Governed AI, from pilot to production.
A data-first delivery method, governance built into every gate, and change management that makes adoption stick, run by one accountable program lead.
The challenge
AI rarely stalls because the model is weak
It stalls everywhere around the model.
Data that won't pass review
Data isn't lawful, documented or representative enough for validation or fair-lending tests.
Governance arrives last
Controls bolted on at sign-off add months of rework instead of shaping the design.
Front lines don't adopt
Fraud, Risk and Operations teams don't trust the tool, so the business case never lands.
The rules keep moving
In 2026 alone, US model risk guidance was rewritten, EU AI Act dates moved, and Colorado replaced its AI law.
The result
Dozens of pilots, a handful in production, and examiners asking for evidence nobody kept.
The framework
The Governed AI Lifecycle
Stage 0 runs once per firm. Stages 1 to 6 repeat for every use case. Stage 7 manages the portfolio.
| Stage | Purpose | Governance gate | Approver |
|---|---|---|---|
| 0. Mobilize and govern | Strategy, operating model, policy, inventory, risk tiers, literacy | G0: policy, risk appetite and committee charter approved | Executive sponsor, CRO |
| 1. Frame the use case | Business problem, AI pattern, value, owner, initial tier | G1: intake approved; prohibited or high-risk uses flagged | AI governance committee |
| 2. Prove the data | Inventory, lawful basis, ownership, quality, bias exposure | G2: privacy and data-rights review; sufficiency confirmed | CDO, Privacy, Legal |
| 3. Prepare the data | Clean, label, lineage, representativeness | G3: data dictionary and lineage; proxy-variable review | Data owner, Model risk |
| 4. Build | Train or configure models, or contract a vendor | G4: model card, design documentation, vendor due diligence | Model owner, Third-party risk |
| 5. Validate | Performance, fairness, explainability, robustness, security | G5: independent validation; impact assessment signed | Model risk, Compliance |
| 6. Deploy and operate | Workflow fit, human oversight, monitoring, rollback | G6: go-live readiness; monitoring thresholds; incident playbook | Business owner, Operations |
| 7. Scale and sustain | Value tracking, drift, revalidation, decommissioning | Ongoing: annual review, revalidation triggers | AI governance committee |
Three workstreams run across every stage
Governance and risk
Inventory, tiering, policies, impact assessments, third-party oversight, monitoring, incidents.
Change and adoption
Impact analysis, sponsor roadmap, change agents, role-based training, reinforcement.
Program and value
Portfolio intake, prioritization, budget, benefits tracking, RAID, executive reporting.
The framework is built on the PMI-CPMAI, IAPP AIGP, Prosci ADKAR and PMP bodies of knowledge.
Proportionate control
Risk tiers set the weight of control
Low-risk wins move fast. Heavy controls go where examiners look.
Tier 1 · High
Examples
Credit decisioning, fraud models that block transactions, AML alert scoring, collections treatment.
Controls
Full independent validation, fairness and adverse-action tests, human oversight, quarterly monitoring.
Tier 2 · Medium
Examples
Analyst decision support, dispute triage, complaint classification, call summaries with review.
Controls
Targeted validation, documented testing, monthly monitoring.
Tier 3 · Low
Examples
Internal productivity genAI with no customer decision.
Controls
Acceptable-use policy, data-loss controls, usage monitoring.
End-to-end playbook
Seven plays, 26 weeks, lighthouses in production
The first wave assesses, builds the governance foundation and takes two or three lighthouse use cases live. Durations are planning defaults.
| Play | Weeks |
|---|---|
| 1 · Mobilize | Weeks 1 to 2 |
| 2 · Assess current state | Weeks 2 to 6 |
| 3 · Governance foundation | Weeks 4 to 10 |
| 4 · Prioritize portfolio | Weeks 5 to 6 |
| 5 · Lighthouse delivery | Weeks 7 to 22 |
| 6 · Operate and monitor | Weeks 20 to 26 |
| 7 · Hand over and scale | Weeks 23 to 26 |
| Change and adoption | Weeks 1 to 26, continuous |
By day 90
- Maturity scorecard and stack heat map
- AI governance committee chartered and meeting
- AI policy, genAI standard and risk tiering
- One AI inventory, vendor AI included
- Ranked backlog; lighthouses in build
By week 26
- Lighthouses live with an evidence pack each
- Monitoring, incident playbook, revalidation triggers
- Benefits measured against baseline
- AI center of excellence designed
- Wave-2 roadmap approved
Technology stack assessment
Can you build, run and evidence AI safely at scale?
A 3 to 4 week review that scores eight layers from 1 to 5 against a target set by your ambition and risk mix, then shows which gaps block which use cases.
Data foundation
Cataloged data with lineage, quality scores and owners.
Data access and privacy
Purpose-based access; tokenized card and PII data.
ML and AI platform
Standard environments, feature store, model registry.
Generative AI
LLM gateway, governed retrieval, guardrails, evaluations.
MLOps and LLMOps
Versioned release path with approvals and rollback.
Monitoring
Drift, fairness, cost and safety alerts on thresholds.
Security and resilience
Prompt injection, leakage, poisoning; red-teaming.
Governance tooling
One AI inventory; intake-to-evidence workflow.
Scoring rubric
- 1 · Ad hocManual, no standards
- 2 · EmergingSome shared tools
- 3 · DefinedStandard; evidence on request
- 4 · ManagedAutomated; evidence by default
- 5 · OptimizedPolicy enforced in code
Target: 3 or better on every layer before a Tier 1 use case goes live; 4 on monitoring, MLOps and governance tooling to scale.
Regulatory readiness
2026 changed the rules. Every gate maps to them.
April 2026 · US banking
New model risk guidance replaces SR 11-7
Fed SR 26-2, OCC Bulletin 2026-13 and FDIC FIL-15-2026 scale model risk management to risk and add vendor-model detail. Generative and agentic AI are left for separate guidance.
Dec 2027 · EU AI Act
High-risk duties, including credit scoring, deferred
The Digital Omnibus moved Annex III obligations from August 2026. Chatbot and synthetic-content transparency applies from August 2026.
Jan 2027 · Colorado
SB 26-189 replaces the Colorado AI Act
Notice before consequential automated decisions, and explanation, correction and human review within 30 days of an adverse outcome.
Status as of November 2026. Not legal advice.
Engagements
Start small. Expand on proof.
Most clients begin with the assessment, then add a governance foundation and a lighthouse wave.
AI Readiness and Stack Assessment
4–6 weeksMaturity scorecard, technology heat map, regulatory applicability map, ranked use-case backlog, 18-month roadmap.
Discuss this: AI Readiness and Stack AssessmentAI Governance Foundation
8–12 weeksCommittee charter, AI and genAI policy, risk tiering, AI inventory, impact assessment, RACI, literacy program; NIST AI RMF and ISO/IEC 42001 aligned.
Discuss this: AI Governance FoundationLighthouse Delivery
12–16 weeks per waveTwo or three use cases through every gate to production, with change management and benefits tracking.
Discuss this: Lighthouse DeliveryAI Transformation Office
6–18 monthsFractional or full-time program leadership: PMO, value tracking, governance operations, change, executive reporting.
Discuss this: AI Transformation OfficeAdoption Accelerator
6–10 weeksFor tools that are live but under-used: Prosci impact assessment, sponsor roadmap, change agents, training, adoption metrics.
Discuss this: Adoption AcceleratorAdvisory seat
MonthlyA standing session with your AI governance or steering committee.
Discuss this: Advisory seatDownloads
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We'll look at where your AI work stands, what is blocking production, and whether a readiness assessment, a governance foundation or a PriorVue pilot is the right first step.