Your Team Shipped AI. Nobody Owns What Happens Next.
Fractional AI governance and assurance — built into the tools your team already uses.
The Problem
Most organizations deploying AI have the same gap. Someone signed off on the model. Someone owns the budget. But nobody owns the question of whether the output is right, who reviews it, what happens when it’s wrong, and how any of that gets evidenced to a customer, a regulator, or a board.
The failures that have made headlines over the past two years almost never trace back to the model. They trace back to process: no review step, no defined accountability, no record of what was checked, no way to tell a confident wrong answer from a correct one before it reached a customer. That’s not a data science problem. It’s a quality and delivery problem — and it’s the one I’ve spent 23 years solving.
Why Most AI Governance Work Doesn’t Stick
NIST AI RMF and ISO 42001 are sound frameworks. The trouble is that they mostly get delivered as strategy: a maturity assessment, a policy document, a set of principles, an invoice. Six months later the policy is in a folder nobody opens and the team is shipping exactly the way it was before.
Governance only works when it’s a step someone has to take. A workflow transition that won’t advance without a review. A ticket type that requires a named approver. A page template that captures what was tested and by whom. A report that surfaces what shipped without oversight.
What I Actually Build
Governance workflows in Jira — review gates, approval steps, and issue types mapped to NIST AI RMF functions, built into the delivery process your team already follows
Evidence and documentation structure in Confluence — model inventories, use-case registers, review records, and incident logs that produce an audit trail as a byproduct of the work rather than a separate exercise
AI output review practice — who reviews what, against which criteria, at what frequency, with what escalation path when the output is wrong
Failure-mode assessment — a structured read of where your specific AI deployments are most likely to fail, based on how comparable deployments have failed elsewhere
Reporting and accountability — dashboards that show leadership what’s governed, what isn’t, and where the exposure sits
Why Me
Twelve years administering Jira and Confluence at the SuperAdmin level, and an Atlassian Certified Professional in Jira Administration for Cloud (ACP-120). Two decades of building quality organizations for regulated and high-security brands — Verizon, Bristol-Myers Squibb, Alaska Airlines, L’Oreal — where an unreviewed release was never an option.
AI governance is a quality assurance discipline pointed at a new category of system. The frameworks are new. Building a review discipline into how a team ships is not.
Engagement Models
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A fixed-scope review of your current AI deployments, governance gaps, and highest-exposure use cases, with a prioritized remediation plan.
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6 - 12 weeks to stand up governance workflows, documentation structure, and review practice in your existing tooling
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Ongoing, 1-3 days per week, owning the practice as it scales with your AI footprint