Building an AI Use Case Library That Drives Adoption

“How could AI help with my work?”

This question kills more AI adoption than any technical limitation. People hear about AI’s potential but can’t connect it to their specific tasks. Without that connection, AI remains abstract and unused.

The solution is a use case library—a curated collection of examples showing how AI applies to real work in your organisation. Done well, a use case library transforms AI from theoretical to practical, from abstract to applicable.

Let me share how to build one that actually drives adoption.

Why Use Case Libraries Matter

Use case libraries serve multiple purposes:

Bridging the Imagination Gap

Most people can’t extrapolate from general AI capabilities to their specific work. They need concrete examples:

  • “AI can help with content creation” is abstract
  • “AI can draft the quarterly stakeholder update from bullet points” is actionable

Use cases bridge this gap.

Normalising AI Use

When people see use cases from their function and level, AI use feels normal rather than exotic. If “people like me” use AI for “tasks like mine,” resistance drops.

Providing Starting Points

Use cases give people somewhere to start. Rather than staring at a blank prompt, they can adapt an existing approach.

Demonstrating Value

Concrete use cases with quantified outcomes make AI value visible:

  • “Reduced report drafting time from 4 hours to 45 minutes”
  • “Improved first-draft quality significantly based on manager feedback”

These outcomes motivate adoption.

Ensuring Appropriate Use

Use cases clarify not just what you can do but what you should do. They embed appropriate use in practical examples.

Use Case Anatomy

Effective use cases include specific elements:

Task Description

What work task does this address?

  • Be specific: “Drafting weekly project status updates” not “Writing”
  • Connect to recognisable work: Use task names people actually use

Before State

How was this task done before AI?

  • Time required
  • Steps involved
  • Pain points
  • Quality challenges

This establishes the baseline AI improves.

AI Approach

How does AI assist with this task?

  • Which tool(s) used
  • Prompt or approach (specific enough to replicate)
  • Human role in the process
  • Verification steps

Provide enough detail to enable replication.

After State

What’s the result of using AI?

  • Time savings (quantified)
  • Quality improvements (specific)
  • Other benefits
  • Remaining human work

Outcomes motivate adoption.

Tips and Variations

What else should users know?

  • Common mistakes to avoid
  • Variations for different situations
  • Advanced techniques for experienced users

Tips help people succeed with the use case.

Attribution

Who contributed this use case?

  • Adds credibility
  • Creates models people can reach out to
  • Recognises contribution

Optional but valuable if people are willing.

Sourcing Use Cases

Where do use cases come from?

Internal Discovery

Find people already using AI effectively:

  • Survey workforce about AI use
  • Ask managers to identify team AI users
  • Monitor AI tool usage data
  • Listen for AI mentions in conversations

People are often already experimenting—capture what’s working.

Facilitated Development

Create structured opportunities to develop use cases:

  • Workshops where teams identify AI opportunities
  • Pilot programs that generate documented use cases
  • Innovation challenges focused on AI applications
  • Hackathon-style events

Facilitation accelerates use case development.

External Adaptation

Start with external examples and localise:

  • Industry use cases adapted to your context
  • Vendor examples modified for your processes
  • Consultant-provided frameworks filled with your specifics

External sources provide starting points; localisation makes them relevant.

Use Case Sprints

Dedicated efforts to develop use case libraries:

  • Select a function or process area
  • Gather relevant stakeholders
  • Systematically identify opportunities
  • Document and test use cases
  • Publish and promote

Sprints create concentrated progress.

Organising the Library

How you organise use cases affects usability:

By Function

Organise by business function:

  • Marketing use cases
  • Finance use cases
  • HR use cases
  • Operations use cases

People can quickly find use cases relevant to their area.

By Task Type

Organise by the type of work:

  • Content creation use cases
  • Data analysis use cases
  • Communication use cases
  • Research use cases

Useful when functions share similar task types.

By Tool

Organise by AI tool used:

  • ChatGPT use cases
  • Microsoft Copilot use cases
  • Specialised tool use cases

Helpful when people have access to specific tools and want to explore capabilities.

By Complexity

Organise by difficulty:

  • Beginner use cases
  • Intermediate use cases
  • Advanced use cases

Helps people find appropriate starting points.

Multiple Views

Best approach: enable multiple navigation paths. Same use cases, different ways to find them.

Making Use Cases Findable

A library no one can find is useless:

Central Location

House the library in a known, accessible place:

  • Intranet site
  • Learning management system
  • Shared collaboration space
  • Dedicated microsite

Make the location obvious and memorable.

Search Capability

Enable searching by:

  • Keywords
  • Function
  • Task type
  • Tool
  • Benefit type

Good search accommodates different ways people look for content.

Integration With Work

Put use cases where people are working:

  • Links in relevant tool interfaces
  • Integration with help systems
  • Connection to training content
  • Manager distribution to teams

Don’t make people go somewhere separate.

Promotion

Actively promote the library:

  • Regular communications about new use cases
  • Featured use cases in newsletters
  • Manager encouragement to explore
  • Training references to library

Awareness drives usage.

Maintaining the Library

Use case libraries need ongoing maintenance:

Adding New Use Cases

Establish regular cadence for additions:

  • Continuous submission process
  • Periodic use case development efforts
  • Review and publication process
  • Announcement of new additions

Fresh content maintains interest.

Updating Existing Use Cases

AI tools change; use cases need updates:

  • Regular review of existing use cases
  • User feedback on accuracy
  • Tool update impact assessment
  • Retirement of obsolete use cases

Outdated use cases damage credibility.

Quality Control

Ensure use cases meet standards:

  • Accuracy verification
  • Appropriate use confirmation
  • Format consistency
  • Outcome validation

Quality protects the library’s value.

User Feedback

Enable users to contribute:

  • Rating or voting on use cases
  • Comments with tips or variations
  • Reporting of issues
  • Requests for new use cases

Feedback improves quality and fills gaps.

Measuring Library Impact

How do you know if the library is working?

Usage Metrics

  • Library visits
  • Use case views
  • Search patterns
  • Download/save rates

Usage indicates relevance.

Adoption Correlation

  • Do library users adopt AI more?
  • Do people who use a use case actually implement it?
  • Which use cases drive most adoption?

Correlation reveals library effectiveness.

Qualitative Feedback

  • What do users say about the library?
  • What’s missing?
  • What’s most valuable?
  • What’s confusing?

Feedback guides improvement.

Business Impact

  • Do use cases deliver promised benefits?
  • What’s the cumulative impact of library-driven adoption?
  • What’s the ROI of library investment?

Impact justifies continued investment.

Common Mistakes

Avoid these pitfalls:

Too generic: Use cases that don’t connect to actual work don’t drive adoption.

Too complex: Starting points that are intimidating don’t get used.

Outdated quickly: Without maintenance, libraries become misleading.

Hidden: Libraries no one knows about can’t help.

Missing context: Use cases without enough detail can’t be replicated.

No quality control: Poor use cases damage credibility of good ones.

Getting Started

If you don’t have a use case library, start building one:

  1. Identify 10-15 existing AI uses across the organisation
  2. Document them in consistent use case format
  3. Organise by function or task type
  4. Publish in accessible location
  5. Promote through available channels
  6. Establish process for additions and maintenance
  7. Measure usage and iterate

Start small and grow. A library of 15 solid use cases beats 100 superficial ones.

People need to see AI relevance to adopt it. A well-designed use case library makes that relevance visible.

Build the library. Watch adoption follow.