01
What is changing or failing?
Refinery teams spend time extracting, reconciling and reformatting information across logs, reports, action registers and audit packs. The work is repetitive, but its output may influence operating, assurance or management decisions.
A generic AI initiative creates immediate questions about data access, source reliability and accountability. A bounded reporting workflow allows the organisation to test value while keeping the technical and control boundary explicit.
NIST’s AI Risk Management Framework asks organisations to specify the task and application scope, define human oversight and test the system against its intended context. Its Generative AI Profile adds explicit attention to provenance and review. For reporting, that translates into a simple rule: the system should never make it harder to identify the source, assumption and person accountable for release.
02
Why does it matter commercially?
Manual compilation consumes specialist time and slows the point at which management can see exceptions or act. Yet automation that obscures sources can increase review effort and reduce trust rather than creating useful capacity.
The commercial case depends on measurable time removed, faster exception visibility and maintained or improved quality—not the number of AI features introduced.
03
What must management decide?
Management must choose a workflow where AI can assist with search, extraction, comparison or drafting while a named person remains responsible for review and release.
The first decision is therefore about workflow design and evidence control. Model selection comes later and should remain subordinate to data classification, accuracy requirements and operating authority.
Measure the review burden, not only generation speed
A credible pilot separates time spent collecting, drafting, checking sources, resolving exceptions and approving release. Faster drafting creates no value if reviewers spend the saved time reconstructing provenance or correcting plausible errors. Measure total elapsed time, specialist touch time, exception-detection time and material correction rate.
04
What evidence is required?
- Current effort, delay, rework and error pattern for the selected report
- Authoritative sources and permitted data boundary
- Required traceability from output back to source
- Human review, exception and approval responsibilities
- Acceptance measures for time, quality and adoption
- Test set containing normal, incomplete, contradictory and late-changing source records
- Record of material corrections and the failure mode that produced each one
05
What should happen next?
- Select one read-only reporting workflow with a visible owner and baseline.
- Design source references, review steps and exception handling before automation.
- Test on representative historical cases and record failure modes.
- Scale only after users can verify outputs and the measured benefit survives normal operating conditions.