AI governance inside the operation
AI should have a job description.
When AI influences schedules, quality decisions or customer commitments, it is part of the operation. OS gives each capability a purpose, boundary, owner and reviewable record.
The operational question
Not only “can the model do it?”
The useful question is whether the action is appropriate in this workflow: with these inputs, at this level of authority, under this person's responsibility and with a safe path when confidence is low.
A suggestion becomes operational when it changes:
The capability envelope
Six controls around every AI-assisted step.
Named capability
The model, version and operational purpose are known.
Permitted scope
Allowed records, tools and actions are explicitly bounded.
Human owner
A role remains accountable for use and outcome.
Decision threshold
Confidence and impact determine when review is required.
Visible reasoning context
Users see the inputs and operational signals behind a suggestion.
Action record
Inputs, output, approval and resulting changes remain reviewable.
Graduated authority
Autonomy is a setting, not a leap.
A capability can begin by assisting, earn trust as a recommender and only execute when the process, data and fallback are mature enough.
Assist
Summarise, search, explain or draft. A person decides and acts.
Low-impact knowledge work
Recommend
Rank options or flag risk. A person reviews the recommendation before execution.
Planning and exception decisions
Prepare
Build a schedule, record or transaction for explicit approval.
Repeatable, reversible work
Execute
Perform a bounded action when pre-approved conditions are met.
Stable rules with monitoring and fallback
Practical manufacturing use
Start where context improves a decision.
Delivery-risk detection
Combine current progress, material readiness and remaining capacity to flag orders before a deadline is missed.
Exception triage
Classify shop-floor issues and route them with the relevant order, product and history attached.
Planning scenarios
Prepare feasible alternatives and show the trade-offs; the planner keeps authority over the schedule.
Quality pattern support
Surface recurring combinations of product, machine, material and deviation for expert review.
Work-instruction assistance
Present the approved instruction and answer questions using only controlled operational sources.
Operational summaries
Create shift and management summaries from the live record without manual report assembly.
Start with the work
Put one AI use case inside a safe operating boundary.
We will define the decision, data, authority, human owner and evidence required before choosing a model or automation pattern.
Map a workflow