What is an AI impact assessment?
An AI impact assessment reviews who may be affected by an AI use case, what decisions or records may change, which data is involved, what harms could occur, and what controls are needed before approval.
AI automation resource
AI Impact Assessment Template
AI impact assessment template for affected users, data sensitivity, customer impact, fairness, human review, vendor risk, mitigations, and approval evidence.
Search intent
Business owners, compliance reviewers, security teams, and operations leaders deciding whether an AI use case could affect customers, employees, records, pricing, compliance, or trust.
An AI impact assessment helps a business decide whether an AI use case should proceed, pause, or move into stronger governance. The assessment should identify affected people, affected records, decision impact, data sensitivity, human review, vendor involvement, fairness concerns, reversibility, mitigation evidence, and the owner who accepts the decision.
Guide sections
A practical framework for the workflow decision.
These resources support buyers who are still comparing examples, controls, ROI, and implementation readiness.
Use case record
Start from the inventory: name the use case, owner, department, users, tool, workflow, source systems, status, and decision needed.
Model record
Document the model, agent, vendor, version, environment, owner, data access, lifecycle status, and evaluation evidence.
Affected people
Identify customers, employees, applicants, patients, vendors, partners, managers, reviewers, and operational teams affected by the AI use.
Decision impact
Classify whether AI only drafts or whether it influences eligibility, pricing, prioritization, advice, customer treatment, or records.
Data sensitivity
Review customer, employee, health, financial, legal, credential, pricing, proprietary, regulated, and private-note data exposure.
Human oversight
Define reviewer duties, source evidence, approval triggers, escalation paths, override rules, and blocked-release conditions.
Fairness review
Check whether outputs could treat people inconsistently, amplify biased source data, hide uncertainty, or create unfair escalation paths.
Vendor and tool risk
Document vendors, public tools, subprocessors, data use, model training terms, support access, retention, and procurement status.
Mitigations
List controls, approval rules, data minimization, testing evidence, monitoring metrics, incident response, and rollback steps.
Approval decision
Approve, approve with controls, keep read-only, require redesign, send to risk register, or reject before implementation expands.
Checklist
What to confirm before moving from research to implementation.
A useful resource page should help the buyer make a better decision before they contact anyone.
- Identify the AI use case, owner, department, tool or vendor, source systems, users, workflow stage, and approval decision needed.
- List affected customers, employees, applicants, vendors, reviewers, managers, records, and downstream teams.
- Classify decision impact: draft-only, recommendation, prioritization, customer-facing output, system update, eligibility, pricing, or record change.
- Review data sensitivity, data minimization, recipient boundaries, retention, vendor handling, and whether public AI tools are involved.
- Define human review, escalation triggers, blocked actions, override rules, source evidence, and reviewer accountability.
- Evaluate fairness, consistency, explainability, uncertainty handling, appeal paths, and monitoring signals before launch.
- Record mitigations, evidence, residual concerns, approval owner, review date, and whether the use case must enter the risk register.
FAQ
Common impact assessment questions.
Short answers for teams researching AI workflow automation before choosing a pilot.
When should a business use an AI impact assessment template?
Use it before launching AI that affects customers, employees, applicants, pricing, eligibility, compliance claims, sensitive records, public content, or permanent system updates.
How is an AI impact assessment different from a risk assessment?
An impact assessment focuses on affected people, decisions, data, fairness, transparency, and business consequences. A risk assessment scores the workflow controls, permissions, failures, and launch readiness.
Next step
Turn the guide into a scoped workflow review.
We will help identify the workflow, approval boundary, data sources, and ROI model that make sense for a first pilot.