Most enterprises struggle to prove the value of AI because ideas live in silos and aren’t tied to clear business outcomes. The fastest way to fix this is to create an AI use case catalog, apply a consistent prioritization rubric, and require every initiative to have a quantified business case. That turns AI from scattered experiments into a repeatable value engine.

Why do enterprises need a structured way to show AI value?

Enterprises are under pressure to justify large AI investments with measurable returns, not just “innovation stories.”

👉 Workbook: Use Case Value

A structured Use Cases & Value framework gives teams:

• A repeatable way to define and deliver value

• Clear prioritization across AI opportunities

• Well-formed business cases

• Success criteria that link technical outcomes to business impact
  Workbook 3 Use Case Value

Without this structure, AI ends up as one-off pilots with unclear owners, fuzzy metrics, and no narrative for leadership.

What is an AI use case catalog—and what belongs in it?

An AI use case catalog is a central, business-facing repository for all AI initiatives—live, in-flight, and proposed.

It should answer:

• What AI projects exist or are being proposed?

• Who owns them?

• What problem do they solve?

• What value are they expected to deliver?

Core elements to include in each entry:

• ID – Auto-generated identifier for reporting and quick reference

• Title – A simple, human-readable name for the use case

• Overview – Short summary of the intent and idea

• Business Owner – Who is accountable on the business side

• Problem Statement – The specific business problem being addressed

• Risk Attributes & Score – Inputs from AI governance and risk teams

• Prioritization Attributes & Score – Based on your prioritization rubric

• Time to Approval – How long it takes from submission to signoff

• Value Metrics – KPIs that will be improved (e.g., NPS, cycle time, cost)

• Strategic Alignment – How this maps to company or AI strategy

• Solution Type – Internal build vs. vendor vs. TBD

Many teams start the catalog in Excel or SharePoint, then move to a more scalable, business-facing platform like AlignAI as the portfolio grows.

How should teams intake and prioritize AI use cases?

Use a structured prioritization rubric so every idea is evaluated consistently instead of by politics or gut feel.

A simple three-step approach:

1. Define your ideal project profile

   • What level of data readiness do you expect?

   • What implementation complexity fits your current maturity?

   • What time-to-value is acceptable?

2. Score each opportunity

   • Rate on value potential, technical feasibility, data readiness, and stakeholder support.

   • Use a 1–5 or low/medium/high scale to simplify discussions.

3. Rank and select

   • Focus on top-tier initiatives that can demonstrate value quickly.

   • Use these wins to build momentum and credibility for bigger, longer-horizon work.

The outcome is a prioritized roadmap of AI initiatives tied to measurable impact, not just a backlog of ideas.

What makes a strong AI business case?

A strong AI business case translates technical potential into clear, quantified business outcomes.

Your process should require every approved initiative to include:

• Problem & value hypothesis

  • What metric or process will improve?

• Baseline metrics & expected gains

  • Current performance vs. projected post-AI performance

• ROI estimate

  • Use scenarios if exact numbers are hard to pin down

• Functional requirements

  • What the solution must do for users

• Non-functional requirements

  • Performance, availability, security, regulatory constraints

• Cross-team alignment

  • Confirm business, data, and engineering are on the same page

This creates a shared understanding of why the project matters and how success will be measured, which is essential for stakeholder buy-in.

How do data flow diagrams and model cards fit into proving value?

Data flow diagrams and model cards help teams connect the dots between architecture, behavior, and business value.

• Data Flow / Solution Architecture

  • Shows how data moves through the system

  • Clarifies dependencies, ownership, and production pathways

  • Makes it easier to estimate effort, risk, and maintenance needs

• Model Cards

  • Document what each model does, what data it was trained on, and where it should or shouldn’t be used

  • Support governance, audit, and future enhancements

Together, they provide traceability: this model, with these inputs, supports this use case, which targets these metrics.

How can a team get started with a Use Cases & Value process?

Start small and make it real, not theoretical:

1. List your existing AI projects and pilots.

2. Build a simple catalog with owner, problem, value, and status.

3. Agree on a lightweight prioritization rubric and score your top 10–20 ideas.

4. Require a basic business case for anything that moves into delivery.

5. Layer on data flow diagrams and model cards as your process matures.

Over time, this becomes a repeatable engine for deciding what to build, why to build it, and how to prove it was worth it.

📘 Ready to operationalize AI value?

Download AlignAI’s Use Cases & Value Workbook to implement a full framework for use case catalogs, prioritization, business cases, and value tracking

👉 Workbook: Use Case Value

In Short: Use Cases & Value Explained

Q: Why do AI teams struggle to prove value?
Most teams lack a consistent way to describe, prioritize, and measure AI initiatives. Without a catalog, rubric, and business case template, AI stays in pilot mode and value is hard to quantify.

Q: Who should own the AI use case catalog?
Typically a central AI, data, or portfolio management team owns the catalog, with input from business owners, governance, and engineering.

Q: How often should AI use cases be re-scored?
Revisit scores regularly—at least quarterly or alongside planning cycles—so priorities reflect current strategy, capacity, and learnings.