How to Create an AI Use Case Intake Form and Risk Scoring Model That Actually Works

Why an AI Intake Process Matters

As AI adoption accelerates across technology organizations, the biggest challenge is often not finding ideas—it’s deciding which ideas are worth pursuing. Without a structured intake process, teams can end up with scattered requests, duplicated efforts, unclear ownership, and risky use cases that move forward without proper review.

An AI use case intake form and risk scoring model solve this by creating a repeatable way to evaluate incoming ideas. Together, they help your organization:

• Capture the right information up front
• Compare use cases consistently
• Prioritize high-impact opportunities
• Identify legal, security, privacy, and ethical concerns early
• Route ideas to the right stakeholders for review

If you work in product, engineering, data, operations, or innovation leadership, this framework gives you a practical way to turn AI demand into a manageable pipeline.

What an AI Use Case Intake Form Is

An AI use case intake form is a standardized document or workflow used to gather the essential details about a proposed AI initiative. It acts as the front door for AI ideas and ensures every request is evaluated using the same criteria.

A strong intake form should answer:

• What problem are we trying to solve?
• Who will use the solution?
• What data will it require?
• What business value could it create?
• What risks or constraints exist?
• Does AI actually make sense for this problem?

Think of it as a filter that separates vague enthusiasm from actionable, well-scoped opportunities.

What a Risk Scoring Model Is

A risk scoring model is a structured method for assigning a numerical or categorical risk level to each AI use case. It helps teams determine how much governance, review, and oversight a proposal needs before moving forward.

A good risk model typically assesses factors such as:

• Data sensitivity
• User impact
• Regulatory exposure
• Model explainability needs
• Potential for bias or harm
• Security and privacy concerns
• Human oversight requirements

The goal is not to block innovation. The goal is to match the level of scrutiny to the level of risk.

Step 1: Define the Purpose of Your Intake Process

Before building the form, clarify what decisions it should support. For example:

• Should it help teams decide whether to approve a pilot?
• Should it route ideas to legal, security, or data governance?
• Should it prioritize use cases for an AI roadmap?
• Should it determine whether a use case needs human review or executive approval?

Your purpose will shape the form fields, scoring criteria, and workflow.

Best practice

Keep the intake process aligned with business outcomes. If the process is too heavy, people will avoid it. If it’s too light, it won’t catch important risks.

Step 2: Design the Intake Form Fields

Your intake form should collect enough information to evaluate both value and risk without overwhelming the requester.

Core sections to include

1. Use Case Overview

Capture the basics:

• Use case title
• Requestor name and team
• Business unit
• Date submitted
• Short description of the idea

2. Problem Statement

Ask the requester to explain:

• What problem is being solved?
• Who experiences the problem?
• How is the problem handled today?
• Why is AI being considered?

3. Expected Business Value

Include questions such as:

• What outcome will improve?
• Is the goal cost reduction, revenue growth, efficiency, customer experience, or risk reduction?
• What metrics will define success?
• What is the estimated business impact?

4. Users and Stakeholders

Capture:

• Primary users
• Internal or external customer impact
• Departments involved
• Decision-makers and approvers

5. Data and System Requirements

Ask:

• What data sources are needed?
• Is the data structured, unstructured, or both?
• Does the data include personal, confidential, or regulated information?
• Are there existing systems or APIs involved?
• Is the data high quality and accessible?

6. AI Approach

Request details like:

• What type of AI is being proposed?
• Is this a predictive model, generative AI, recommendation engine, classification system, or chatbot?
• Is automation involved?
• Will the AI make decisions or only assist humans?

7. Risk and Compliance Considerations

Include prompts for:

• Privacy concerns
• Security risks
• Regulatory or legal exposure
• Bias or fairness issues
• Explainability requirements
• Human oversight needs

8. Implementation Readiness

Ask:

• Is there executive sponsorship?
• Is funding available?
• Does the team have the necessary technical skills?
• What dependencies or blockers exist?
• What is the desired timeline?

Step 3: Keep the Form Structured and Easy to Score

To make the intake process scalable, use a mix of:

• Multiple-choice questions for consistent scoring
• Short-answer fields for context
• Required fields for critical information
• Conditional logic to reveal extra questions only when needed

Example

If a requester selects “uses customer personal data,” the form can automatically reveal questions about consent, retention, and compliance review.

This keeps the form manageable while still capturing important detail.

Step 4: Build a Risk Scoring Model

A risk scoring model should translate answers from the intake form into a clear, repeatable assessment.

A simple scoring approach

Use a scale of 1 to 5 for each category, where 1 = low risk and 5 = high risk.

Suggested categories:

• Data sensitivity
• User impact
• Regulatory exposure
• Model autonomy
• Explainability need
• Bias/fairness risk
• Security risk
• Operational complexity

You can then calculate a total score or weighted score.

Example weighting

Not every category should carry the same weight. For example:

• Data sensitivity: 20%
• Regulatory exposure: 20%
• User impact: 15%
• Model autonomy: 15%
• Bias/fairness risk: 10%
• Explainability need: 10%
• Security risk: 5%
• Operational complexity: 5%

Adjust weights based on your organization’s priorities and risk appetite.

Step 5: Define Risk Tiers and Actions

Scores are only useful if they lead to action. Create clear tiers that determine the next step in the workflow.

Example risk tiers

Low Risk

• Score: 0–20
• Action: Standard review and lightweight approval
• Examples: Internal productivity tools using non-sensitive data

Medium Risk

• Score: 21–35
• Action: Review by product, data, security, and governance stakeholders
• Examples: Customer-facing assistants using limited personal data

High Risk

• Score: 36+
• Action: Formal review, legal/compliance sign-off, executive approval, and documented controls
• Examples: AI used in hiring, credit decisions, healthcare, or other regulated contexts

These thresholds should be tailored to your environment.

Step 6: Add Value Scoring Alongside Risk Scoring

A risk score alone does not tell you whether a use case is worth pursuing. You also need a value score.

Common value dimensions

• Strategic alignment
• Revenue potential
• Cost savings
• Time savings
• Customer impact
• Competitive advantage
• Scalability

Why this matters

A use case with moderate risk may still be a top priority if it has very high business value. Conversely, a low-risk use case may not be worth the effort if the impact is minimal.

Simple prioritization formula

You can use a matrix like:

• High value / Low risk = Prioritize now
• High value / High risk = Review carefully, then decide
• Low value / Low risk = Consider later
• Low value / High risk = Defer or reject

This helps leadership focus on the most promising opportunities.

Step 7: Establish a Review Workflow

Your intake form and scoring model should feed into a clear workflow.

Example workflow

1. Request submitted
2. Initial triage by AI program owner or PMO
3. Automated scoring applied
4. Stakeholders notified based on risk tier
5. Review meeting or asynchronous approval
6. Decision recorded: approve, revise, defer, or reject
7. Use case added to roadmap or backlog

Important roles to include

• Business sponsor
• Product manager or use case owner
• Data lead
• Security representative
• Legal/compliance partner
• AI governance lead
• Architecture/engineering lead

Step 8: Make the Form Actionable for Requesters

A strong intake process does more than collect information. It helps requesters think more clearly about their idea.

Tips to improve quality

• Use plain language instead of technical jargon
• Provide examples for each question
• Explain why each field matters
• Offer guidance on what “good” answers look like
• Keep the form under 10 minutes if possible

When people understand the purpose, they provide better input.

Step 9: Automate What You Can

If your organization handles many AI requests, automate parts of the process.

Automation opportunities

• Auto-routing based on risk tier
• Slack or email notifications to reviewers
• Pre-filled fields from CRM or HR systems
• Automatic score calculation
• Dashboard reporting on pipeline volume and approval rates
• Audit trail creation for governance records

Automation reduces manual effort and improves consistency.

Step 10: Test and Improve the Model Over Time

Your first version will not be perfect. Treat it as a living framework.

Questions to review regularly

• Are low-risk use cases being over-escalated?
• Are high-risk use cases slipping through?
• Are the scoring categories aligned with actual issues encountered?
• Are requesters confused by any questions?
• Are reviewers seeing enough context to make decisions?

Metrics to track

• Number of submissions
• Approval rate by risk tier
• Time to decision
• Percentage of incomplete submissions
• Number of escalations after initial review
• Post-launch incidents or governance issues

Use this data to refine both the form and the scoring model.

Sample AI Use Case Intake Form Template

Here’s a simple starting point you can adapt:

Section 1: Request Details

• Use case title
• Requestor name
• Team or department
• Business sponsor
• Date submitted

Section 2: Use Case Summary

• Describe the problem
• Describe the proposed AI solution
• Who will use it?
• What business outcome is expected?

Section 3: Data and Technology

• What data will be used?
• Is sensitive or regulated data involved?
• What systems will integrate with the solution?
• Will the AI model be built, bought, or configured?

Section 4: Risk Assessment

• Could the use case affect customer decisions?
• Could it create legal or compliance exposure?
• Is human review required?
• What bias or fairness concerns exist?
• What security controls are needed?

Section 5: Readiness

• Is there funding?
• Is there an implementation owner?
• What is the desired timeline?
• What dependencies exist?

Sample Risk Scoring Model Template

Use a 1–5 scale for each category:

Category	1	3	5
Data Sensitivity	Public data only	Limited internal data	Highly sensitive or regulated data
User Impact	Internal convenience	Moderate business impact	High impact on customers or employees
Regulatory Exposure	None	Some review needed	Strong legal/regulatory scrutiny
Model Autonomy	Human always approves	AI suggests, human decides	AI makes decisions automatically
Explainability Need	Low	Moderate	High, must be fully explainable
Bias/Fairness Risk	Minimal	Possible concerns	Significant fairness risk
Security Risk	Low	Moderate	High security implications
Operational Complexity	Simple workflow	Moderate integration	Complex, multi-system deployment

Then define score ranges and required approvals.

Common Mistakes to Avoid

1. Making the form too long

If the intake process is burdensome, people will skip it or submit poor-quality responses.

2. Scoring risk without defining actions

A score is meaningless unless it triggers a clear next step.

3. Ignoring value

Not all low-risk ideas are worth pursuing. Prioritize based on both value and risk.

4. Using vague criteria

Terms like “high risk” or “sensitive” need clear definitions to ensure consistent scoring.

5. Failing to update the model

AI regulations, tools, and use cases evolve quickly. Your model should evolve too.

Final Thoughts

An AI use case intake form and risk scoring model are foundational tools for responsible AI delivery. They help technology teams move faster without losing control, ensuring that the right ideas get attention and the wrong ideas get the scrutiny they need.

Start simple, make the process easy to use, and refine it based on real submissions. Over time, your intake framework will become a powerful engine for AI governance, prioritization, and innovation.

Next Step

Once your intake and scoring process is in place, the natural next move is to design the governance workflow that determines who reviews each use case, what approvals are required, and how decisions are documented.