AI use case approval process flowchart
AI use case approval template for single-use-case intake, value, data, feasibility and risk review, conditional approval, recorded decisions and delivery handoff.
What the ai use case approval process is
An AI idea becomes reviewable only when it names one use case, who will use it, which decision or task it supports and what better outcome can be measured. This template starts with that focused intake and sends vague proposals back for clarification before discovery time is spent. A portfolio analyst compares expected value with a baseline and adoption measure. A data steward then checks candidate sources, rights, provenance and quality, while a technical lead outlines the approach, dependencies, human fallback, delivery effort and operating ownership. Risk and governance review follows feasibility rather than replacing it, identifying potential harms, misuse, security and privacy impacts and selecting testing, oversight and monitoring controls. The final decision can approve, approve with explicit conditions or decline, and every approved path ends with a delivery lead accepting scope, measures, checkpoints and unresolved obligations.
This chart is the portfolio gate for one proposed use case, not the full lifecycle governance of a deployed AI system. Approved work should enter /templates/ai-governance-process when it needs a versioned inventory record, tier-based controls, independent review, monitoring, reassessment and retirement. Nor is this a model-development methodology: experiments, evaluation design, software delivery and production operations continue behind the handoff using the evidence and conditions recorded here. Keeping that boundary prevents an intake panel from approving a technology label such as 'use generative AI' without a specific user problem, while also preventing early discovery from pretending to settle every deployment risk. Adapt value thresholds, reviewer roles, conditional-approval rules and handoff evidence to your portfolio and governance model.
What this flowchart covers
In this template
- Seven role lanes across six phases for one use case, from sponsor intake through portfolio, data, technical and risk review to authority decision and delivery ownership
- An intake gate that requires specific users, a supported decision or task, a desired outcome and an accountable sponsor before value discovery begins
- Separate value, data and feasibility decisions, with explicit loops for access, provenance, quality, scope, dependency and operating-model gaps
- A risk proposal covering potential harms, misuse, security or privacy impacts plus testing, oversight and monitoring controls before the decision pack is compiled
- Three decision outcomes: approval, conditional approval with named owners and evidence due, or decline with reasons recorded, followed by a controlled delivery handoff
When to use this template
- A portfolio receives broad AI ideas with no specific user, baseline, measurable outcome or accountable sponsor
- Promising use cases reach technical discovery before anyone checks data rights, provenance, quality, delivery ownership or human fallback
- Risk review happens as a late veto because value, data, feasibility and control questions are not sequenced in one visible process
- Conditional approvals are recorded in meeting notes but their owners, evidence dates and delivery checkpoints do not follow the approved work
How it works
Require one use case per intake
Ask for one user group, one supported task or decision, one current baseline and one desired outcome. Split proposals that bundle unrelated departments or benefits, because each may have different data, feasibility and risk findings.
Make value measurable
Define the baseline, expected improvement, adoption signal, cost range and person accountable for realizing the benefit. Treat discovery as worthwhile only when evidence can change the investment decision, not as an automatic reward for a persuasive idea.
Screen the data path early
Identify candidate sources, provenance, access authority, quality limitations and sensitive fields before selecting an implementation approach. Record gaps as owned work rather than assuming production data will become available after approval.
Define conditional approval
Specify which conditions may remain open at handoff, who owns each one, what evidence closes it and which checkpoint prevents further progress if it remains unmet. Do not use conditional approval when an unresolved issue makes discovery itself inappropriate.
Build the delivery handoff
Transfer the approved scope, assumptions, value measures, data constraints, controls, conditions, decision record and review checkpoints into the delivery backlog. Require the delivery lead to accept ownership and return material scope changes for reassessment.
Frequently asked questions
What are the steps in an AI use case approval process?
Submit one use case; record its users, supported decision and desired outcome; confirm a specific scope and accountable sponsor; estimate value against a baseline; decide whether discovery effort is warranted; identify a usable data path; assess technical feasibility, dependencies, fallback and operating ownership; review potential harms and controls; compile evidence and assumptions; decide to approve, conditionally approve or decline; and hand approved scope, measures, conditions and checkpoints to delivery.
What should an AI use case proposal include?
Include the user and problem, the task or decision being supported, current baseline, measurable outcome, sponsor, candidate data, likely approach, dependencies, human fallback, delivery and operating owners, potential impacts, initial controls, assumptions and unresolved questions. The proposal does not need a finished design, but it should be specific enough that reviewers can compare value, feasibility and risk for the same use case.
What does conditional approval mean for an AI use case?
Conditional approval allows defined work to proceed while named, time-bound requirements remain open. Each condition needs an owner, required evidence and a checkpoint that blocks the next relevant stage if it is not closed. It should not be a softer name for unresolved fundamental risk, unavailable data or an absent owner. Record the authority and rationale so delivery understands both what may proceed and what remains prohibited.
How is use case approval different from AI governance?
Use case approval is an early portfolio decision about whether one sponsored idea merits delivery or discovery based on value, data, feasibility and an initial risk proposal. AI governance continues across the system lifecycle with inventory, risk tiering, control evidence, independent review, deployment authorization, monitoring, reassessment and retirement. Approval should hand into that lifecycle rather than being treated as permanent permission to deploy any later version or changed use.
Where this process fits
In most operations this process hands off to AI governance process flowchart (inventory to retirement).
Comes after
- AI governance process flowchart (inventory to retirement) — Policy-neutral AI governance process template for inventory, risk tiering, control selection, independent review, deployment decisions, monitoring, reassessment and retirement.
Part of
- Data management & governance
- AI governance