Most teams don’t need a giant governance program to start. They need a minimum viable AI risk management framework that puts five things in place:
- inventory + ownership
- boundaries + policy
- review + measurement
- monitoring + incident response
- change control for tools/vendors
Let’s crunch the numbers: you can stand up a workable operating model in 30 days if you timebox decisions, assign owners, and ship v1 deliverables each week.
Who This AI Risk Management Framework Roadmap Is For (and Who It’s Not)
This roadmap is for:
- Organizations adopting AI across multiple teams and tools (marketing, finance, ops, support, HR)
- Mid-market leaders moving from pilots to repeatable adoption
- COOs/CIOs/Heads of Ops/Risk who need control without bureaucracy
- IT/Security leaders being asked to “approve AI tools” with incomplete context
This roadmap is not for:
- Pure R&D experimentation with no operational or customer impact
- Teams building frontier models from scratch (you’ll need deeper model governance)
Rule of thumb: scale rigor by data sensitivity (PII/client/regulatory) and impact (client-facing, high-stakes decisions). Measure twice, cut once.
What You’ll Have at the End of 30 Days (Deliverables)
By Day 30, you’ll have v1 of the operating model—enough to run a defensible cadence:
- AI use case/tool inventory (v1) (what, where, owner, data types, risk tier)
- Approved tools list + prohibited tools list (with an exception path)
- Data handling boundaries (v1) (what can/can’t go into AI tools)
- AI use policy (v1) (allowed/restricted/prohibited)
- Governance roles + decision rights (who approves what)
- Starter risk register (top risks, controls, owners, status)
- Review checklist for AI-assisted outputs (v1) (human-in-the-loop by tier)
- Metrics starter set + reporting cadence (quality, risk, adoption, ops)
- Incident response/escalation path (v1) (triage → contain → correct → communicate → learn)
30-Day Roadmap Timeline
The Framework in One Minute (NIST as Backbone, Brief)
This roadmap uses the NIST AI RMF* as the backbone because it’s credible, widely recognized, and scales from “minimum viable” to mature governance without forcing a single industry-specific model. The four functions—Govern, Map, Measure, Manage—map cleanly to what leaders actually need: decision rights, inventory context, evaluation, and ongoing risk handling.
*U.S. National Institute of Standards and Technology AI Risk Management Framework: Official NIST AI RMF page
If you want the deeper “what responsible AI governance means in plain English,” see:
https://www.fletterconsulting.com/responsible-ai-governance-plain-english/
Week 1 (Days 1–7) — AI Risk Management Framework: Inventory + Ownership + Boundaries
Objective: Create visibility and assign accountability. No inventory = no control.
Deliverables (ship by Day 7)
- Inventory sheet (v1): use cases + tools + owners + data types + output type (internal/client-facing) + risk tier
- Data classification boundary: what can/can’t go into AI tools (by data class)
- Approved vs prohibited tools list (v1): even if short
- Accountable owner per category: e.g., Marketing AI, Finance AI, Ops AI, HR AI
Owners (make it explicit)
- Exec Sponsor: COO/CIO (sets policy authority + prioritization)
- Program Owner: Ops/Risk lead (or Fractional CAIO)
- IT/Security: tool/security constraints + access controls
- Legal/Compliance: data + external commitments + disclosure rules
- Use Case Owners: the business leaders who benefit from outputs
If you do only 3 things this week…
- Build the inventory (even if incomplete—v1 is fine).
- Define the data boundary (what never enters AI tools).
- Assign owners (one accountable name per category, not a committee).
Week 2 (Days 8–14) — AI Risk Management Framework: Policy + Governance Cadence (GOVERN)
Objective: Convert boundaries into a repeatable operating cadence.
Deliverables (ship by Day 14)
- AI use policy (v1): allowed / restricted / prohibited
- Decision rights: approvals for tools + approvals for use cases
- Governance cadence: meeting schedule + agenda template
- Documentation minimums: what gets logged, where, and by whom
Minimum viable AI use policy sections (5–7 bullets)
- Scope (who/what is covered)
- Approved vs prohibited tools + exception process
- Data handling boundaries (PII/client/regulatory)
- Output rules (internal vs client-facing; disclosure expectations)
- Human review requirements by risk tier
- Recordkeeping/logging expectations
- Escalation path for issues/violations
Governance cadence (keep it lean)
- 30 minutes weekly (Weeks 2–4), then monthly
- Agenda: new use cases/tools, exceptions, incidents, metrics, vendor changes, decisions needed
If you want help setting up the operating model behind this cadence, start here:
[AI Risk Management Services]
Week 3 (Days 15–21) — AI Risk Management Framework: Measurement + Review Workflow (MEASURE)
Objective: Put “human-in-the-loop review” into an actual workflow, and define what “good” looks like.
Deliverables (ship by Day 21)
- Review workflow by risk tier (who reviews what, when)
- Starter metrics across 4 buckets (quality, risk, adoption, operations)
- Thresholds/triggers (what requires escalation)
- Output QA checklist (v1) (for AI-assisted content/analysis)
Risk tiers (simple, workable)
- Tier 1 (Low): internal drafts, no sensitive data, low consequence
- Tier 2 (Medium): internal decisions, moderate business impact, limited sensitivity
- Tier 3 (High): client-facing outputs, sensitive data, regulated or high-stakes decisions
Output QA checklist (v1) — keep it practical
- Source check: is the output grounded in approved inputs?
- Sensitive data check: any PII/client confidential content present?
- Accuracy check: spot-check key claims, numbers, dates
- Tone/brand check (for client-facing)
- Disclosure check (if required)
- Final human approval recorded (who, when)
Starter metrics set (10 max)
Quality
1. Rework rate (how often outputs require rewrite)
2. Error rate on spot checks (accuracy issues found per sample)
Risk
3. Sensitive data incidents (count / severity)
4. Policy exceptions requested vs approved
5. Client complaints linked to AI outputs
Adoption
6. Active users by team (weekly/monthly)
7. Top use cases by volume
Operations
8. Time to approve a new tool/use case
9. Review coverage rate (how many Tier 2/3 outputs got required review)
10. Vendor/tool change events logged
Triggers example: any client-facing Tier 3 output with an accuracy defect → log incident + update checklist + retrain.
Week 4 (Days 22–30) — AI Risk Management Framework: Monitoring + Incident Response (MANAGE)
Objective: Create the “what happens when it fails” muscle.
Deliverables (ship by Day 30)
- Monitoring cadence: weekly (initially) + monthly governance review
- Incident response flow (v1): triage → contain → correct → communicate → learn
- Vendor/tool change triggers: what forces review and re-approval
- Policy/inventory update cadence: how changes get recorded and communicated
First incident playbook checklist (v1)
- Triage: what happened, when, who, impacted systems/clients?
- Contain: stop use case/tool, revoke access if needed, preserve logs
- Correct: fix the output/process; patch prompts/templates; adjust access
- Communicate: internal stakeholders; external if required (legal/compliance involved)
- Learn: update policy, checklist, training; add a new trigger/metric
Vendor/tool change triggers (minimum)
- New model/version/features released
- New integrations/plugins enabled
- Changes to data usage/retention terms
- Significant pricing/usage changes that alter operational behavior
- Tool expands into client-facing workflows
Two Implementation Tracks for Your AI Risk Management Framework (Lean vs Scaling)
| Track | Best for | People | Cadence | Tool/Vendor Reviews | Metrics |
|---|---|---|---|---|---|
| Track A (Lean) | 1–2 primary tools, mostly internal use | 1 accountable owner + SMEs | Quarterly + exception-driven | As-needed | Minimal starter set |
| Track B (Scaling) | Many teams/tools, client-facing use | Working group + executive sponsor | Monthly governance | Formal triggers + periodic review | Full starter set + thresholds |
Numbers speak louder than words: pick the track that matches your real exposure, not your aspiration.
Common Failure Modes (and How to Avoid Them)
- Policy posted, no adoption → add workflow hooks: approvals, checklists, and a cadence
- No tool boundaries → publish approved/prohibited list + enforce procurement and SSO controls
- No ownership → one accountable owner per category; committees advise, owners decide
- No metrics beyond “time saved” → include quality + risk + operational metrics
- No incident response → define the path before the first incident, not after
- Shadow AI continues → make the approved path easier than the unofficial path
Case in Point: Uneven Internal GenAI Use (and How the Framework Fixes It)
Scenario: teams adopt GenAI drafting and summarization unevenly. Marketing uses one tool, Ops uses another, and analysts copy internal notes into prompts inconsistently. Quality varies, sensitive content occasionally slips through, and nobody can say what “reviewed” means.
Using this 30-day plan:
- Boundary rules define what data can be used and where
- Review checklist standardizes “human-in-the-loop” for Tier 2/3 outputs
- Escalation triggers route high-risk failures to the right owners fast
- Monitoring cadence catches drift and repeat issues before they become incidents
Policy helps you say “don’t.” The AI risk management framework helps you run the system.
How FCG Helps
FCG helps leaders implement a minimum viable operating model fast—then keep it operational.
- We build the AI risk management framework (inventory, roles, controls, metrics, playbooks) on a tight timeline
- We run the governance cadence via Fractional CAIO (or set it up and transition ownership)
- We align governance to NIST AI RMF to keep it credible and defensible (without turning it into a bureaucracy)
Start here: [AI Risk Management Services]
Minimum Viable Governance
Start with inventory, boundaries, ownership, and a review habit. Everything else scales from there.
30-Day Deliverables Checklist
- Inventory (use cases/tools/owners/risk tier)
- Approved + prohibited tools list
- Data handling boundaries
- AI use policy (v1)
- Roles + decision rights
- Starter risk register
- Review workflow + QA checklist
- Metrics starter set + cadence
- Incident response path + escalation triggers
- Vendor/tool change control + update cadence
What Are You Waiting For?
If you want to implement this roadmap quickly—and keep it operational—FCG can lead it through our AI Risk Management Services and Fractional CAIO model.
Request a 30-day implementation sprint (deliverables + cadence + handoff). Contact us now!