AI tool approval form

When an approval workflow is needed

A workflow is needed as soon as employees use AI tools for company work and the business cannot answer which tools are allowed, who owns them, what data is permitted, and when the decision will be reviewed. The workflow does not need to be heavy, but it must create a durable record that managers, IT, security, HR, procurement, or compliance can inspect later.

AI tool approval process

A practical AI tool approval process starts before a team buys or enables the tool. The requester describes the business purpose, users, data categories, output use, integrations, and launch timing. The reviewer then decides whether the tool can be approved immediately, approved with restrictions, piloted, deferred for vendor evidence, or blocked. The final decision should be copied into the approved AI tools register so employees know what is allowed and managers can see when the decision needs review.

• Request: capture tool, owner, use case, users, data, output, integrations, and launch date.
• Review: check data sensitivity, vendor evidence, enterprise controls, output risk, and customer impact.
• Decision: record approved, restricted, pilot only, pending evidence, blocked, or retired.
• Operate: set review date, evidence link, restrictions, owner, and escalation path.

Form, register, questionnaire, and risk assessment

A request form captures the proposed tool and use case. An approved-tools register records the decision and review status. A vendor questionnaire collects evidence from the AI provider about data handling, training use, retention, deletion, subprocessors, and controls. A full risk assessment is deeper and should be reserved for sensitive data, customer-impacting workflows, regulated contexts, or automated decisions.

Required request fields

The request should be short enough that employees will complete it, but complete enough that reviewers can decide whether more evidence is needed.

• Tool name, vendor, requesting team, business owner, and technical owner.
• Business purpose, user group, launch date, and expected frequency of use.
• Data categories entered into prompts, uploaded files, integrations, or feedback fields.
• Output use: internal draft, customer-facing material, decision support, automation, or record creation.
• Vendor evidence links, contract status, enterprise controls, and known restrictions.

Decision statuses and review dates

Simple statuses make the workflow easier to run. Use approved for low-risk tools with enough evidence, approved with restrictions when data or output limits apply, pilot only when the business needs testing before approval, pending evidence when reviewers need more information, blocked when risk is unacceptable, and retired when the tool no longer has an owner or use case.

Restricted and blocked use cases

The approval workflow should tell employees when to stop and escalate. Examples include customer confidential data, credentials, unreleased financials, employee records, regulated data, source code, automated eligibility decisions, HR decisions, legal conclusions, security-sensitive workflows, and direct customer communications that have not been reviewed by a person.

Common implementation mistakes

The most common mistake is collecting requests without maintaining the register after approval. Other problems include approving a tool without a business owner, ignoring SaaS AI features inside existing tools, failing to distinguish pilot use from broad rollout, storing vendor evidence without a decision, and never reviewing tools after vendor terms or company use changes.