Find a useful opportunity—and understand what responsible delivery would require.
FICTIONAL USE CASES · PRACTICAL GOVERNANCE
Value and oversight belong in the same conversation.
Compare two original examples. Their labels describe the scenario, not measured returns, vendor ratings or an assessment of your business.
Public content drafts and Invoice data extraction differ across 4 of four comparison areas. Review the reasons and pilot boundaries below.
EXAMPLE 1
Public content drafts
Draft a first version of a service explanation using approved public material.
Potential value
Focused time saving
Data sensitivity
Public information
Human oversight
Editor reviews every draft
Delivery effort
Small bounded pilot
Why this level of oversight?
Incorrect claims can mislead readers even with public input. An editor verifies every statement and source before publication.
Data and scope
Published service descriptions and approved writing guidance.
Accountable owner
Communications owner
A bounded pilot
Compare editing time and factual corrections with the current drafting process.
Pause or stop when
Pause if drafts invent capabilities, citations or customer results.
Apply the four functions
Govern
Name the editor and define which content can be published.
Map
Limit inputs to approved public sources and document the intended audience.
Measure
Review factual accuracy and editing effort on a representative sample.
Manage
Keep publication manual, track corrections and withdraw misleading content.
EXAMPLE 2
Invoice data extraction
Suggest invoice fields for a finance reviewer before the existing approval process.
Potential value
Broader processing capacity
Data sensitivity
Confidential financial data
Human oversight
Finance validates before approval
Delivery effort
Moderate workflow effort
Why this level of oversight?
An incorrect account, amount or duplicate can affect a payment. Extraction stays separate from authorization and release of funds.
Data and scope
Supplier invoices with financial and contact details, handled within an approved environment.
Accountable owner
Finance process owner
A bounded pilot
Check field accuracy, duplicate detection and correction time across invoice formats.
Pause or stop when
Stop the pilot if unverified fields reach a payment instruction or an unauthorized data destination.
Apply the four functions
Govern
Assign finance accountability and preserve separation of duties.
Map
Map the invoice-to-payment workflow and approved data destinations.
Measure
Test totals, currencies, account fields, duplicates and unreadable documents.
Manage
Require field verification and existing approvals; keep payment execution outside the AI workflow.
A framework for judgment, not a readiness score.
The NIST AI Risk Management Framework is voluntary. Its four functions are Govern, Map, Measure and Manage. Governance runs across the other functions, and risk management continues throughout the system lifecycle; these are not four one-time approval steps.
Govern
Who owns the decision, rules and accountability?
Map
What is the intended context, data and possible impact?
Measure
What evidence will show performance and risk?
Manage
How will people act on findings, monitor and stop?
The examples and pilot boundaries above are KAISAN’s educational interpretation. They do not certify compliance or establish that a particular AI system is safe. Benefit does not cancel a data or oversight requirement.
Reviewed September 18, 2026 against AI RMF 1.0. NIST reports that a revision is in progress. Read the AI RMF Core and human–AI interaction guidance for the source context.