Keelson · Blog
AI Operations Governance: The Practical Controls Mid-Market Teams Can Run · Keelson
01 · Inventory and ownership
Govern what you can name, not what the policy assumes.
The first control is an AI inventory that names every workflow, tool, vendor, data class, business owner, technical owner, and downstream metric. Do not start with a questionnaire asking whether the company uses AI. Start with the queue, the browser extensions, the vendor invoices, the automations, and the places an operator pastes a customer record into a prompt. The inventory is complete when every workflow has one named human who can answer what the system does, what it reads, what it writes, and what happens when it is wrong.
Write claims triage, settlement matching, or contract review. “AI transformation” is not a workflow an operator can pause or measure.
The owner is accountable for the metric, the runbook, the exceptions queue, and the next review. A shared inbox is not an owner.
Claims cycle time, exceptions per shift, or dollars reconciled tells the review what outcome the workflow is supposed to move.
02 · Risk tiers and approval gates
A tier without a gate is just a label.
Give each tier a decision the operator can make. Low-risk drafting and internal search can use a lightweight owner approval. Medium-risk workflows that touch operational records need a documented data boundary, test set, and business-owner sign-off. High-risk decisions that affect a customer, employee, payment, safety outcome, or regulatory obligation need a named approver, mandatory human review, and an escalation path before production access.
Internal summaries, drafts, and search stay behind the owner’s approval. The gate is a recorded owner, an approved tool, and no external business action.
A workflow touching operational records needs a test set, a written data boundary, a named business owner, and evidence that the output can be overridden.
Customer, employee, payment, safety, and regulatory outcomes require an approver, a review rule, and a stop condition before production access.
03 · Data and vendor controls
The tool earns access; it does not receive it by default.
Record the minimum data the workflow needs, the retention period, where it is processed, which subprocessors can access it, how deletion requests are handled, and which vendor contract covers the use. Sensitive data does not enter a tool because a demo made the workflow look faster. The second person verifies the vendor terms after the owner writes the data boundary and before production credentials are issued.
- What is the minimum input the workflow needs?
- Which data classes are explicitly excluded?
- Where is the data processed and retained?
- Who can retrieve or delete it?
- What contract and subprocessor list covers the use?
The operator should be able to show the exact approved fields, retention rule, and vendor review date without opening a procurement ticket. If the boundary is not on the runbook page, it will be improvised in the queue.
The build method is useful here: score the tool against the workflow and the handoff, not against the quality of its demo.
04 · Human review and escalation
Human in the loop is a queue design, not a checkbox.
Every production workflow needs a clear handoff between model output and business action: what a reviewer checks, which confidence or rule threshold sends a case to review, how an override is recorded, and who receives an exception after the queue crosses its limit. The control is real only when it has a response time, an owner, and a rule for stopping automation when the failure pattern changes.
Write the few facts that make the output safe to act on. A reviewer should not have to infer the standard from the model’s confidence score.
Capture the reason, not just the final answer. Overrides are the training set for the next review and the earliest signal that the workflow is drifting.
Set the exception rate, queue age, or failure pattern that moves the workflow to a named escalation contact and pauses automated action.
05 · Logging and incident response
The log should tell the operator what changed.
Log the input class, model or vendor version, prompt or policy version, output status, reviewer decision, override reason, and downstream result without storing more sensitive content than the team needs. When a workflow fails, the incident record should answer what changed, when the first bad case appeared, how many cases were exposed, who paused the workflow, and what test proves it is safe to restart.
Version the workflow inputs that can change without a deployment: vendor model, prompt, policy, routing rule, reviewer decision, and downstream result. Keep the content boundary narrow so an incident log does not become a second data lake.
The runbook names the person who pauses the workflow, the queue that takes over, the affected volume, and the test that gives production access back.
06 · Monthly and quarterly review
Governance is the decision the review makes, not the meeting it fills.
The owner runs a 20-minute monthly review of volume, exception rate, overrides, latency, cost, and the locked business metric. The governance group runs a quarterly review of access, vendor terms, risk tiers, incidents, model changes, and whether the workflow still belongs in production. Each review ends in one decision: keep, retrain, narrow, pause, or retire.
Volume, exceptions, overrides, latency, cost, and the business metric. Twenty minutes, one owner, one action for the next month.
Recheck vendors, permissions, tiers, incidents, model changes, and the data boundary before the workflow inherits another quarter of access.
A review that ends with “noted” leaves the workflow unchanged. Name the operating decision and the owner who carries it into the next queue.
The practical standard is simple: if an operator cannot point to the owner, the gate, the reviewer, the log, and the next review date, the workflow is not governed. Put those five answers on one page and the team has a control system it can run before the next Monday morning, not a policy it hopes someone remembers after an incident.
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A 90-day build is the right answer sometimes and AI Ops is the right answer other times, but both answers cost you three years of your Monday morning if you pick the wrong one. A short working note on the four signals an operator reads to pick, the cost of getting it backwards, and the Monday test you can run before the invoice goes out.
The practical AI ops digest
Working notes on outcome-locked pricing, the 90-day build, and what AI Ops actually does after handoff. No marketing — just the operator notes we send to the people who already read this blog.
Bring us the workflow
Put the controls on the workflow that matters.
The self-serve audit turns the inventory, risk tier, data boundary, exception route, and metric into one page your operator can run. Or talk to a build operator directly if the workflow already has a queue and a number that is drifting.
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