Responsible AI Governance That Teams Can Actually Use
Pillar
AI Leadership
Company
Accedo
Audience
Executive, Hiring Manager, Product Leader, Design Leader
Date
2026
PLAYBOOK BRIEF
Capability
AI governance, adoption strategy, human accountability
Overview
At Accedo, I defined a client-by-client AI governance model for product and delivery teams. It makes tool permission, data handling, human review, proof-of-concept and production boundaries, API-key storage, and escalation explicit before a use case scales.
Evidence & limits
Evidence: The model turns a general AI approval into nine visible checks: permission, data, tool, stage, reviewer, credential storage, provider handling, audit expectations, and escalation. It is an operating practice, not a risk-reduction metric. Trade-offs: I separated proof-of-concept and production readiness while keeping client permission and data handling required at both stages. The model adds review work and accepts client-specific variation instead of pretending one universal rule fits every use case. Limits and failure modes: Blank owners, customer data in unapproved tools, unmanaged API keys, a proof of concept treated as production-ready, or human review assumed rather than named. What this proves: The model makes implicit assumptions visible before scale. It does not prove that every output was reviewed, and I do not claim a quantified change in adoption, speed, or risk.
The questions I needed answered
Before an AI use case moved beyond a proof of concept, I needed a practical list of answers. Had the client approved the tool? What data would enter it? Would the provider retain that data? Where was the API key stored? Who reviewed the output? What triggered escalation?
At Accedo, I defined a client-by-client governance model for product and delivery teams because those answers changed with the client and the use case. A general approval to use AI did not resolve them.
The use-case review
A proof of concept and a production workflow have separate readiness decisions. Customer data stays out of unapproved tools. The model names a human reviewer for AI output, approved storage for API keys, and an escalation owner when sensitive information is involved.
Client permission and data handling apply at both stages. Calling something a proof of concept is not a reason to leave credentials unmanaged or put customer data into an unapproved service.
The working check
For each use case, the model records:
client permission
data involved
approved tool
proof of concept or production
human reviewer
API-key storage
provider retention and data-residency position
audit expectations
escalation owner
Shared channels and meetups handle routine learning. The escalation path is for questions that need a specific owner or decision.
What changed
The practical change was visibility. Client permission, data handling, review ownership, and production readiness became named checks instead of implicit assumptions.
That does not prove every output was reviewed or every risk was removed. It gives the team a place to see what is decided, what is missing, and who needs to act.
The cost
The model adds review work. I kept experimentation and production readiness separate so a small test did not carry the same process as a live workflow.
Client-specific checks also make the model less tidy than one universal AI policy. That is deliberate. The rule has to reflect the client, data, and use case in front of the team.
Run the check before the next review
Take one current AI use case and fill in the list above. It should be a short preflight, not a new approval ceremony.
Any blank field identifies a decision or owner that is still missing before the use case scales.
Client-specific security decisions remain private, but the review questions are reusable.
