AI Governance Use Cases: Practical Examples for Teams

AI governance use cases are the specific situations where governance—approved rules, review steps, ownership, and evidence—is needed for AI work. Governance is not a single policy that covers everything; it is a set of decisions made for each type of AI use, matched to the risks that use carries. fenbs keeps the approved boundaries and lessons for each workflow in one place, so the rules are visible to every person and AI assistant working in that context.

The same team may need different governance levels for different tasks. Using AI to summarize internal meeting notes carries different risk from using it to screen job applicants or generate clinical documentation. Understanding the use case determines what governance is proportionate.

Research and literature review

Using AI to identify relevant sources, summarize papers, or draft a literature review section is one of the most common AI use cases in knowledge work. The governance question is: who verifies the citations, and what happens when the AI attributes something incorrectly?

Good governance for research AI use includes: a rule that all citations must be checked against primary sources before the work is finalized; a named reviewer who is responsible for that check; and a record of what was verified. This is proportionate—low review burden, clear ownership, and the evidence is the verified citation list itself.

Writing and content production

AI-assisted writing for external content—proposals, reports, marketing, client communications—requires governance around accuracy, disclosure, and approval. The governance question is: who is accountable for the content before it goes out, and does it need to disclose AI involvement?

A useful governance rule for writing is to assign authorship responsibility explicitly: a named person reviews and approves every AI-assisted external document before it is sent. That person is not just checking for typos—they are taking accountability for the content. For professional services (legal, medical, financial), disclosure requirements may be external rather than just internal policy.

Hiring support and candidate review

Using AI to screen resumes, generate interview questions, or summarize candidate assessments is a high-risk use case. The EEOC has guidance on AI in hiring, and several states have specific requirements for automated employment decision tools. The governance question here is not just "did a person review the output"—it is "did a qualified person who understands the legal constraints make the final decision?"

Governance for hiring AI use typically requires: a rule that AI cannot make or determine a final hiring decision; a named person responsible for reviewing AI recommendations; documentation of the review for each candidate; and a periodic audit of whether the AI's recommendations correlate with protected characteristics in ways that could indicate disparate impact.

AI assistant actions in work systems

When an AI assistant connects to work systems—project management, CRM, communications, or documents—and takes actions within them, governance becomes about access controls and approval rules. The governance question is: what can the assistant do without asking a person first?

Proportionate governance for AI assistant actions includes: a clear list of permitted actions (read, comment, update status) and actions that require human approval before execution (send email, delete record, approve a decision); a named person responsible for the assistant's access configuration; and a review of the assistant's actions history at the set interval. For a broader view of AI agents in work settings, the guide to AI agent use cases covers the operating model.

Customer-facing AI use

AI tools that interact with customers—chatbots, automated responses, product recommendations—carry regulatory exposure in many sectors. The governance question is: does the customer know they are interacting with AI, and does the output meet applicable standards?

FTC guidance on AI-generated content and endorsements, sector-specific requirements in financial services and healthcare, and state consumer protection laws may all apply. Document the governance decision: what does the customer-facing AI do, what human oversight exists, and what disclosures are made? Treat those as live decisions that need updating when the tool or the regulation changes.

What proportionate governance looks like

Proportionate AI governance matches the rule to the risk. Low-risk AI use (drafting internal notes, summarizing a meeting, reformatting a document) needs lighter governance: a named reviewer and a brief record. High-risk AI use (hiring decisions, patient-facing outputs, financial recommendations) needs more: specific approval steps, a documented audit trail, and periodic review by qualified people.

The common failure is treating all AI use the same: either applying enterprise-grade compliance to every AI-assisted task (slowing the organization unnecessarily), or applying no governance to any task (creating accountability gaps where they matter most). Map each use case, assess the risk, and set the governance level accordingly.

How to do this with fenbs

Create a separate governance record for each significant AI use case in your organization.

1. Create a free account and set up a governance project. Add a brief description of the AI use cases it covers.
2. For each use case, write a Decision: what the AI does, what it cannot do, who reviews the output, and what counts as a valid review.
3. Create a task for each review step that requires documentation: a named reviewer, a due date, and a checklist for what the review covers.
4. When an incident or near-miss occurs, add a lesson: what happened, what the gap was, and what changed. Future AI sessions in that context read the lesson before starting.
5. Review the governance decisions for each use case quarterly. Check whether the use case has expanded beyond the original scope.

fenbs does not monitor AI outputs, automate compliance, or provide legal advice. It holds the governance layer: the decisions behind each use case and the evidence that they are being followed. For how governance scales across a small team, see AI agent governance for small teams.