An AI use case intake process is how an organization records, reviews and approves each proposed AI project before anyone builds or buys it. Every submission goes through the same questions and scoring criteria, so similar requests get similar decisions. Trustible’s Automated Workflows run this process with role-based routing and an audit log of every decision.
Article Overview
- An intake process has six stages: submission, triage, evaluation, decision, onboarding and ongoing monitoring.
- A good intake form covers seven context areas. Keep the answers structured, because they drive risk scoring.
- Leidos cut its initial AI intake from weeks to hours after moving from spreadsheets to a purpose-built platform.
What Is an AI Use Case Intake Process?
When AI requests arrive through email and chat, governance teams lose track of them. Teams start pilots without review, departments buy overlapping tools, and risk reviews depend on who happened to see the request. The Cloud Security Alliance reports that more than half of organizations lack a systematic AI inventory, so many teams can’t produce one when an auditor asks.
A structured intake process gives every proposed use case one entry point and one set of review criteria. It also creates a dated record of each decision, which regulators expect under the EU AI Act and several U.S. state laws. Most teams find that a clear process speeds up approvals, because submitters know what reviewers need.
An AI use case intake process is a structured method for capturing, evaluating, and routing proposed AI projects before development begins. Rather than letting teams spin up pilots ad hoc, an intake process creates a defined entry point: every AI idea goes through the same set of questions, gets assessed against consistent criteria, and gets directed to the right stakeholders before a single line of code is written.
Without this structure, organizations face a familiar set of problems. AI adoption accelerates faster than oversight can keep pace. Duplicate tools proliferate. Risk assessments happen inconsistently — or not at all. Teams invest in projects that conflict with regulatory requirements or internal policy.
A well-designed intake process solves for all of this. It gives organizations consistent risk assessment across every proposed use case, cross-functional visibility into what is being built and by whom, and a defensible record of how decisions were made — a requirement under frameworks like the EU AI Act and emerging U.S. state regulations.
The goal is not to slow AI down. It is to make sure the AI your organization deploys is the right AI, built the right way, with the right oversight in place from the start. Organizations looking to accelerate AI intake are finding that a well-structured process actually speeds up responsible deployment rather than hindering it.
The Six Tradeoffs Every Intake Process Must Navigate
Building an effective AI use case framework means making deliberate choices about how it will operate. There is no universal template. As Trustible advisor Gamino-Cheong put it: “There’s no ‘perfect’ intake process. Only the one that’s right-sized for your organization’s size, role, and risk profile.”
Every organization designing an intake process will encounter six core tradeoffs:
- Granularity. How detailed should submissions be? More detail gives reviewers better signal, but heavy forms create friction and discourage participation. Start with the minimum information needed to make a triage decision, and add depth only where risk warrants it.
- Heaviness. How rigorous should the review process be? A process that is too light misses real risks; one that is too heavy stalls legitimate work. Calibrate the review depth to the risk level of the use case, not to a single standard applied universally.
- Outcomes. What does a successful intake look like — approval, rejection, or conditional advancement? Define the possible outcomes upfront and make sure submitters understand what each one means for their project timeline.
- Participation. Who submits use cases, and who reviews them? Broad participation generates more coverage; narrow participation enables tighter control. The right answer depends on your organizational structure and how AI decision-making authority is distributed.
- Implementation. Will the intake process live in a spreadsheet, a shared form, or a dedicated platform? Early-stage programs often start with lightweight tools. As volume grows, the manual burden compounds: inconsistent reviews, no audit trail, no way to route by risk level automatically. Trustible’s Automated Workflows replace that patchwork with structured intake, role-based routing, and a defensible record of every decision, without requiring a lengthy implementation project to get started.
- Timeliness. How fast should the process move? Speed matters to business teams. Set clear SLAs for each review stage so submitters know what to expect and reviewers have accountability.
Getting these tradeoffs right requires honest self-assessment about where your organization is today — not where you plan to be in two years. Build for your current reality, then iterate.
The Core Stages of an AI Use Case Intake Workflow
Each stage has an owner and a target time. A low-risk use case should get through stages 1 to 4 in a few days.
| Stage | What happens | Owner | Target time |
|---|---|---|---|
| 1. Submission | The requester describes the use case in a standard form. | Contributor | Same day |
| 2. Initial triage | A reviewer checks the submission is complete and assigns a preliminary risk tier. | Governance team | 1 to 3 business days |
| 3. Risk and value evaluation | Reviewers assess value, risk, feasibility and governance readiness. Legal, privacy and security join based on the risk tier. | Cross-functional reviewers | 3 to 10 business days |
| 4. Decision | The approver approves, approves with conditions, requests a deeper assessment, or declines with guidance, and records the reason. | Approver | 1 to 2 business days |
| 5. Onboarding | The approved project starts with its conditions attached: data handling rules, documentation, monitoring thresholds and a reassessment date. | Business owner and technical lead | Varies by project |
| 6. Ongoing monitoring | The governance team checks production use cases against their approval conditions and re-reviews them after material changes. | Governance team | On the schedule set at approval |
In Trustible, stages 1 to 4 run as a three-section intake workflow. The contributor completes Section I (Business Case and Risk Scoping). The reviewer handles triage and evaluation in Section II (Initial Risk and Benefits Review). The approver records the decision in Section III (Proposal Decision and Next Steps). Stages 5 and 6 run as follow-on tasks, such as vendor profiles, impact assessments and periodic reviews. For what happens after the decision, see What is an AI use case workflow?
What Should an AI Use Case Intake Form Include?
An AI use case intake form should cover seven context areas, because reviewers need those answers to score risk. Grouping them under four headings keeps the form short.
| Section | Fields | Why reviewers need it |
|---|---|---|
| Use case basics | Business purpose; AI description (system, model or vendor, if known) | Defines what the AI does and for whom |
| Risk signals | Data types used; affected populations; deployment context (internal or customer-facing, jurisdictions); human oversight level | Shows privacy, fairness and regulatory exposure, and how much the system acts on its own |
| Third parties | Vendor, model provider or hosted service | Starts a vendor and model review |
| Ownership and value | Business owner; technical lead; expected outcome and success metric | Assigns accountability and supports prioritization |
Use multiple-choice or dropdown answers where you can, because an automated score can read a selected option but not a paragraph of free text. Remove any field that doesn’t affect a review decision.
Six design choices for an intake process
At Trustible’s IAPP panel, Andrew Gamino-Cheong said: “There’s no ‘perfect’ intake process. Only the one that’s right-sized for your organization’s size, role, and risk profile.” Each organization has to decide six things:
- How much detail to ask for. Ask for enough to assign a risk tier, and request more only when the tier calls for it.
- How deep each review goes. Set review depth by risk tier. One standard for every request either misses risk or delays low-risk work.
- What the possible outcomes are. Publish the decision options so submitters know how each one affects their timeline.
- Who submits and who reviews. Letting anyone submit improves coverage. A small reviewer group keeps decisions consistent.
- What tool you use. Spreadsheets and shared forms work for a handful of requests. They can’t route by risk tier or keep a change history as volume grows.
- How fast each stage should move. Publish a target time for each stage and track it.
Centralized vs. Distributed Intake Models
In a centralized model, all AI use case submissions flow through a single governance function — typically an AI governance team, a Center of Excellence, or a risk committee. This model delivers consistency, a unified audit trail, and clear ownership. It is easier to enforce standards and identify duplicates. The tradeoff is speed: centralized review can become a bottleneck as submission volume grows.
In a distributed model, intake and initial review responsibilities are delegated to business units or functional teams, with the central governance function setting standards and handling escalations. This model moves faster and tends to generate stronger buy-in from the teams doing the submitting. The tradeoff is consistency: without tight standards and tooling, quality varies across units.
Many organizations start centralized to establish standards, then move toward a hybrid model as governance matures and business units build internal capability. The right starting point depends on your current governance capacity and how broadly AI adoption is already spread across the organization.
How to Evaluate and Score AI Use Cases
Once a use case is submitted, evaluation should be consistent and defensible. Strong AI use case pScore every use case on four dimensions:
- Business value. Expected impact, how confident you are in the estimate, and time to value.
- Risk. Potential harm to individuals, the organization or third parties. Set a risk threshold; any use case above it gets a deeper review regardless of its value score.
- Feasibility. Whether you have the data, skills and budget to build and run it responsibly.
- Governance readiness. Whether the needed policies, controls and monitoring exist. If they don’t, make them a condition of approval.
Pick a scoring method and apply it the same way every time. Two reviewers looking at the same use case should reach the same decision, and you should be able to explain that decision to an auditor.
Trustible scores use cases automatically. Each intake answer maps to a risk attribute, such as “Sensitive PII,” “Third-party system” or “External users.” Rules attached to those attributes score risk in five categories: performance, data privacy, cybersecurity, ethical and legal. Reviewers see the score with the reasons behind it, then accept it or override it and record why.
Recap from Trustible’s Panel at IAPP AI Governance Global North America 2025
Last week at the IAPP AI Governance Global North America conference in Boston, Trustible brought together AI governance leaders from Leidos and Nuix to explore a deceptively tactical but mission-critical question: What does the “perfect” AI intake process look like?
The lively session, moderated by Andrew Gamino-Cheong (CTO & Co-Founder of Trustible), unpacked the front door to AI governance—how organizations capture and review every AI use case, tool, or feature under consideration. Without a reliable intake process, organizations risk missing critical visibility into their AI landscape, undermining governance before it even begins.
Sophia Toomey, Program Manager, Leidos
Leidos has more than 50,000 employees, and AI tool requests arrived faster than the team could track them. Toomey’s team began with spreadsheets and PowerPoint before building a company-wide intake process. She recommended simple questions, meeting contributors in the tools they already use, and describing governance as risk reduction rather than auditing. “Give yourself grace if you’re still in Excel and PowerPoint,” she said. Leidos later cut its initial AI governance intake from weeks to hours on a purpose-built platform.
Chris Stevenson, Head of AI Strategy & Operations, Nuix
Nuix supports regulators and investigators, so its AI reviews have to hold up to scrutiny. The team first tracked requests in shared Word documents, which stopped working as requests increased. Stevenson brought in legal and privacy early, then used Trustible to apply the same review steps every time. “AI governance demanded it. Partnering with legal was the game-changer,” he said. Read how Nuix made AI governance operational.
Common Pitfalls and Culture Shifts
- Skipping education. If teams don’t know why intake exists, they go around it. Stevenson’s warning: “Don’t underestimate how fast AI will creep in and overwhelm ad hoc systems.”
- Reviewing in silos. Legal, privacy, security and the business each spot different risks. Name a reviewer from each function and define when they’re pulled in.
- Presenting intake as enforcement. Toomey and Stevenson both saw more participation once they described intake as a way to reduce risk and get approvals faster.
- Stopping at approval. Without reassessment dates and change triggers, the inventory goes out of date within a year.
The practitioner insights in this article were gathered at the IAPP AI Governance Global North America conference, where Trustible hosted a panel on building effective AI intake processes. IAPP AI Governance Global is one of the leading gatherings for privacy, security, and AI governance professionals — a setting where practitioners share what is actually working, not what looks good in a framework document.
The panel surfaced a consistent theme: organizations that invest early in structured intake processes — even imperfect ones — are significantly better positioned to scale governance as AI adoption grows. The ones that wait for the perfect system rarely build one in time.
Key Takeaways for Organizations
If you are building or refining an AI use case intake process, keep these principles in mind:
- Start structured, not perfect. A basic intake form that gets used consistently beats a sophisticated process that exists only on paper. As Toomey put it, give yourself grace if you are starting in Excel.
- Right-size the process to your risk profile. Not every use case needs the same depth of review. Build tiered pathways so high-risk submissions get the scrutiny they require without slowing down low-risk ones.
- Treat risk as a gate. Use cases above a defined risk threshold should require additional review regardless of their business value score, as outlined in the evaluation framework above.
- Build cross-functional ownership early. Legal, privacy, and security should be embedded in the intake process — not consulted after decisions are made. Stevenson’s partnership with legal was the turning point at Nuix.
- Plan for monitoring, not just approval. Approving a use case is not the end of governance. Build ongoing monitoring into every approved project from day one, as described in the core workflow stages.
- Don’t wait for adoption to force your hand. As Stevenson warned: “Don’t underestimate how fast AI will creep in and overwhelm ad hoc systems.” The time to build intake infrastructure is before the volume of AI projects exceeds your capacity to manage them informally.
FAQ
It’s how an organization records, reviews and approves each proposed AI project before anyone builds or buys it. Every request goes through the same questions and scoring criteria, and each decision is documented.
It’s the set of stages, owners and target times an intake process follows. A common framework has six stages: submission, initial triage, risk and value evaluation, decision, onboarding, and ongoing monitoring.
Business purpose, AI description, data types used, affected populations, third-party dependencies, deployment context and human oversight level. Add the business owner, technical lead and expected outcome. Use structured answers so the form can feed automated risk scoring.
A low-risk use case should go from submission to decision in a few days. Higher-risk use cases need cross-functional review and usually take one to three weeks.
An AI use case is one application of AI to a defined business problem. It covers what the system does, what data it uses, who it affects and what outcome it should produce. One AI tool or vendor can support several use cases with different risk levels.
Weigh business value, risk and feasibility. Use cases above your risk threshold get a deeper review before they move forward, whatever their value score.
Intake covers submission, triage and the approval decision. The AI use case workflow also covers what follows: risk and impact assessments, vendor reviews, reassessments and reviews after material changes. Read more about AI use case workflows.
How Trustible Supports AI Use Case Intake
The intake challenges described in this post, inconsistent reviews, no audit trail, risk triage that depends on who happens to review a submission, are exactly what Trustible’s Automated Workflows are built to solve.
Intake in Trustible runs through three structured sections: contributors submit use case details and business context, reviewers assess risk and benefits with embedded intelligence to guide their judgment, and approvers make and document the final decision. Each section has defined ownership. Each step is logged. The result is a governance record that builds itself through normal operations, not one assembled before an audit.
Low-risk use cases are automatically identified for streamlined review. Higher-risk ones trigger deeper assessments, with Trustible’s Risk Management module generating specific risk recommendations and mitigation guidance from a continuously updated taxonomy, rather than leaving reviewers to assess from scratch. Every approved use case lands in AI Inventory as a structured record with ownership, status, and governance history tracked going forward.
Governance teams that replace manual intake with Trustible’s structured workflows don’t just move faster. They get a process that scales with AI adoption rather than falling behind it. If you want to see how it works in practice, Download the Slides from our IAPP panel or Get In Touch to talk through your specific situation.