AI Use-Case Intake Process — From Idea to Implementation

Why You Need an AI Use-Case Intake Process

As AI adoption grows in your organisation, the number of AI use-case ideas will multiply rapidly. Without a structured intake process, your company faces two problems:

1. Good ideas get lost — Employees suggest AI applications informally, but there is no system to capture, evaluate, and act on them
2. Bad ideas consume resources — Without evaluation criteria, the loudest voice or most senior requester wins, rather than the highest-value use case

An AI use-case intake process creates a fair, transparent system for capturing AI ideas from anywhere in the organisation and routing them through evaluation, prioritisation, and (if approved) implementation.

The Intake Process Overview

Stage 1: Submission

Any employee can submit an AI use-case idea through a standard intake form.

Stage 2: Initial Triage

The AI governance committee or designated reviewer conducts a quick assessment to filter out duplicates, out-of-scope requests, and clearly infeasible ideas.

Stage 3: Detailed Evaluation

Promising use cases are scored against standardised criteria covering business value, feasibility, risk, and alignment.

Stage 4: Prioritisation

Scored use cases are ranked and placed on the AI project backlog.

Stage 5: Approval and Assignment

The top-priority use cases are approved for implementation and assigned to an AI champion or project team.

Stage 6: Implementation and Review

The use case is implemented, measured, and reviewed. Learnings feed back into the process.

AI Use-Case Intake Form Template

Section 1: Submitter Information

Section 2: Use Case Description

What is the current process or task? [Describe the current way this work is done, without AI]

What problem does this solve or what opportunity does it create? [Describe the pain point, inefficiency, or missed opportunity]

How would AI improve this process? [Describe specifically how AI would be used — which tool, what inputs, what outputs]

Who would benefit? [List the team, department, or stakeholders who would benefit]

How often is this task performed?

• Daily
• Weekly
• Monthly
• Ad hoc / As needed

Estimated time currently spent on this task: [Hours per week or per occurrence]

Section 3: Data and Risk

What data would be used as input to the AI tool? [Describe the data types involved]

Does this data include personal data?

• Yes
• No
• Unsure

Does this data include confidential or client data?

• Yes
• No
• Unsure

What is the impact if the AI output is incorrect?

• Low — minor inconvenience, easily corrected
• Medium — requires rework, could cause delays
• High — could cause financial loss, reputational damage, or compliance issues
• Critical — could cause harm to individuals or severe business impact

Section 4: Expected Benefits

Estimated time saved per week/month: [Hours]

Other expected benefits: [E.g. improved quality, faster turnaround, better customer experience, reduced errors]

Is this a quick win (can be implemented in 1-2 weeks) or a strategic initiative (requires 1-3 months)?

• Quick win
• Strategic initiative

Evaluation Scoring Criteria

Use these criteria to score each submitted use case on a 1-5 scale:

Business Value (Weight: 30%)

Feasibility (Weight: 25%)

Risk Level (Weight: 25%) — Inverse scoring

Strategic Alignment (Weight: 20%)

Composite Score Calculation

Composite Score = (Business Value × 0.30) + (Feasibility × 0.25) + (Risk Level × 0.25) + (Alignment × 0.20)

Score ranges:

• 4.0 - 5.0: High priority — fast-track for implementation
• 3.0 - 3.9: Medium priority — add to backlog, implement when capacity allows
• 2.0 - 2.9: Low priority — reconsider in 3-6 months or when conditions change
• Below 2.0: Not recommended — provide feedback to submitter

Governance Workflow

Triage (Within 5 business days of submission)

The AI governance committee or designated reviewer:

1. Checks for duplicate or similar submissions
2. Confirms the use case is within scope (not already addressed by an existing tool)
3. Assigns an initial priority estimate
4. Communicates receipt to the submitter

Evaluation (Within 10 business days)

For use cases that pass triage:

1. Score against the evaluation criteria
2. Identify any governance or compliance concerns
3. Estimate implementation effort and timeline
4. Prepare recommendation for the governance committee

Decision (Monthly governance meeting)

The AI governance committee:

1. Reviews all scored use cases
2. Decides: Approve, Defer, or Reject
3. Assigns approved use cases to an AI champion or project team
4. Communicates decisions to all submitters

Implementation Tracking

Encouraging Submissions

The intake process only works if employees actually use it. To encourage submissions:

1. Make it easy — Use a simple form (the template above), not a bureaucratic process
2. Respond quickly — Acknowledge every submission within 2 business days
3. Celebrate successes — Share implemented use cases and their results publicly
4. Provide feedback — Even rejected ideas deserve an explanation
5. Remove barriers — Employees should not need manager approval to submit an idea

Related Reading

• AI Evaluation Framework — Evaluate use cases with a structured quality and risk framework
• AI Vendor Approval Checklist — Approve the tools needed for each use case
• ChatGPT Approved Use Cases — Examples of approved ChatGPT use cases by department

Why Most AI Use Case Intake Processes Fail

The most common failure mode for AI use case intake is excessive bureaucracy that discourages submissions. When employees must complete ten-page business case documents before an AI idea receives initial review, only the most persistent champions submit proposals while valuable grassroots ideas from frontline workers never reach evaluation. Effective intake processes use lightweight initial submissions — a one-page form capturing the business problem, estimated impact, and data availability — with detailed business case development reserved for ideas that pass initial screening.

Comparing Centralized vs. Distributed Intake Models

Centralized intake models funnel all AI proposals through a single governance committee. This ensures consistent evaluation criteria and prevents duplicate investments but creates bottlenecks when submission volume exceeds committee capacity. Distributed models delegate initial screening to departmental technology leads who forward vetted proposals to a central committee for cross-organizational prioritization. Hybrid models increasingly represent best practice: departmental leads conduct feasibility triage using standardized criteria, while the central committee handles strategic prioritization, resource allocation, and governance approval for proposals that pass departmental screening.

How Mature Organizations Evolve Their Intake Processes

Organizations progress through three maturity stages in their AI use case intake processes. Stage one (reactive): individual departments purchase AI tools independently without centralized awareness, creating shadow AI risks and duplicate investments. Stage two (controlled): a centralized intake process captures proposals, applies consistent evaluation criteria, and coordinates resource allocation across competing priorities. Stage three (strategic): the intake process evolves into a continuous innovation pipeline where proactive scanning identifies high-value AI opportunities before departmental submissions, the AI center of excellence mentors submitters to strengthen proposals before formal review, and portfolio-level optimization balances quick wins against transformational investments based on organizational capacity.

Organizations should publish an internal AI use case catalog documenting approved and deployed use cases across all departments. This catalog serves dual purposes: demonstrating organizational AI maturity to incoming proposals and inspiring employees in departments that have not yet identified AI opportunities by showcasing successful implementations in peer departments. Catalogs should include implementation timelines, resource requirements, and quantified outcomes for each documented use case.

Mature intake processes should incorporate a technical feasibility pre-screening stage using standardized checklists before proposals reach the evaluation committee. Pre-screening criteria include: data availability verification through warehouse inventory audits, API compatibility confirmation with existing middleware orchestration layers like MuleSoft or Workato, estimated compute requirements mapped against provisioned cloud GPU quotas, and preliminary vendor shortlisting comparing SaaS options against open-source alternatives hosted on internal Kubernetes clusters.