Use Case Documentation Strategy: How AI Agents Match Your Product to User Needs

Elena, head of marketing at a CRM platform, noticed something odd. When she asked ChatGPT "What CRM should a real estate agency use?", it recommended three competitors. When she asked "What CRM should a SaaS company use?", different competitors. Her product never appeared in either list.

Her CRM supported both industries perfectly. They had customers in real estate and SaaS. But their website said "CRM for modern businesses"—generic positioning that AI agents couldn't map to specific use cases.

She spent two weeks documenting explicit use cases. Within a month, ChatGPT started recommending them for real estate agencies, property management companies, and mortgage brokers. All because AI agents could now match their product to specific scenarios.

Why Use Case Documentation Matters for AI

When someone asks "What's the best [category] for [specific situation]?", AI agents scan for explicit use case matches. They're looking for content that says "This product works for this exact scenario."

Generic positioning like "flexible CRM for growing companies" tells AI agents nothing actionable. Specific use case documentation like "CRM for real estate agencies managing 500+ property listings with automated follow-up" tells AI exactly when to recommend you.

AI agents can't infer your use cases from feature lists. You must document them explicitly.

The Three-Tier Use Case Framework

Elena built a framework that made use cases discoverable and parseable by AI agents.

Tier 1: Primary Use Cases

These are the 3-5 core scenarios where your product excels. Each gets dedicated landing page with depth.

Elena identified: Real estate agencies (10-50 agents), mortgage brokers, property management companies, commercial real estate firms, and title companies.

Each became a standalone page: /use-cases/crm-for-real-estate-agencies/

Why dedicated pages? AI agents parse site structure. A dedicated URL signals importance and specificity.

Tier 2: Secondary Use Cases

These are variations or adjacent scenarios. They get section-level documentation, often on a main use cases page.

Elena's secondary use cases: Solo real estate agents, real estate teams (3-10 people), luxury real estate, new construction sales, and real estate investors.

These lived as sections on her main use cases page with anchor links AI agents could reference.

Tier 3: Tertiary Use Cases

Edge cases and emerging scenarios. These get FAQ-level documentation.

Elena added: International real estate, vacation rental management, and land development.

These appeared in FAQ format: "Does this CRM work for vacation rental management? Yes, our calendar sync and automated messaging work perfectly for vacation rental hosts managing multiple properties."

AI agents pulled from these FAQs when users asked about specific scenarios.

The Use Case Page Template

Elena created a standard template for each primary use case page. Consistency helped AI agents parse information reliably.

Element 1: Clear H1 Title

Format: [Product Category] for [Specific Use Case]

Elena's example: "CRM for Real Estate Agencies"

Not: "Real Estate Solutions" or "Empowering Real Estate Professionals"

AI agents parse H1 tags heavily. Make them explicit and keyword-rich.

Element 2: One-Sentence Value Proposition

First paragraph, first sentence: What this product does for this specific use case.

Elena wrote: "RealtyHub is a CRM built specifically for real estate agencies managing multiple agents, property listings, and client relationships with automated follow-up and MLS integration."

This gave AI agents everything needed to recommend her product for this scenario.

Element 3: Specific Pain Points

A bulleted list of 4-6 problems this use case faces.

Elena documented:

• Managing hundreds of property listings across multiple agents
• Tracking which clients have seen which properties
• Automating follow-up after showings
• Syncing with MLS data feeds
• Coordinating schedules across multiple agents
• Tracking commission splits and referrals

When prospects asked ChatGPT "What are common problems for real estate CRMs?", AI agents could reference these specific pain points.

Element 4: Solution Mapping

How your product specifically solves each pain point.

Elena created a clear mapping:

• "Unlimited property listings organized by agent and status"
• "Client-property viewing history tracked automatically"
• "Automated follow-up sequences triggered after showing appointments"
• "Direct MLS integration with automatic sync"

AI agents used this to explain how her product solved real estate-specific problems.

Element 5: Customer Example

One concrete customer story with metrics.

Elena's template: "[Company name], a [size] [industry] company in [location], used RealtyHub to [outcome] resulting in [metric]."

Real example: "Sunset Realty, a 25-agent real estate firm in Austin, used RealtyHub to automate post-showing follow-up, resulting in 34% faster time-to-offer and 18% higher close rates."

AI agents cite specific customer examples when explaining why a product fits a use case.

Element 6: Common Questions

5-10 frequently asked questions specific to this use case.

Elena added: "Does RealtyHub integrate with our MLS? Yes, we integrate with 150+ MLS systems." "Can we track commission splits? Yes, built-in commission tracking with customizable split rules." "Does it work for commercial real estate? Yes, with customizable property fields for commercial listings."

AI agents pulled from these when answering use-case-specific questions.

The Use Case Discovery Matrix

Elena created a matrix to ensure AI agents could find the right use case for any query.

Dimension 1: Industry Vertical

Explicit industry callouts. "Real estate," "mortgage," "property management."

Not industry-agnostic language. AI agents filter by industry when users specify one.

Dimension 2: Company Size

Specific size ranges. "Solo agents," "teams of 3-10," "agencies with 10-50 agents," "enterprise 50+ agents."

AI agents match company size when users specify it in queries.

Dimension 3: Workflow Type

The primary workflow this use case addresses. "Residential sales," "commercial leasing," "property management," "mortgage origination."

This helps AI agents understand which workflow variation matters.

Dimension 4: Integration Requirements

Key integrations this use case needs. "MLS integration," "DocuSign integration," "calendar sync with showing schedules."

When users ask "Does this integrate with [X]?", AI agents can match use case to integration.

Dimension 5: Compliance/Regulatory

Any regulatory requirements. "RESPA compliance for mortgage," "fair housing compliance," "data privacy for international clients."

AI agents reference compliance when relevant to the use case.

Making Use Cases Searchable

Elena optimized use case content for AI agent discovery.

Tactic 1: Keyword Frontloading

Put the most important keywords in the first 100 words of each use case page.

Elena's first paragraph always included: industry vertical, company size, primary pain point, and core solution.

AI agents prioritize early content when parsing pages.

Tactic 2: Comparison Positioning

Explicitly position against alternatives for each use case.

Elena added: "Unlike generic CRMs like Salesforce that require extensive customization for real estate, RealtyHub includes real estate workflows out of the box."

This helped AI agents understand when to recommend her product over competitors for specific use cases.

Tactic 3: Outcome Metrics

Quantify results for each use case with specific metrics.

Elena's outcomes: "Real estate agencies using RealtyHub close 18% more deals, reduce follow-up time by 12 hours per week per agent, and increase repeat client rate by 23%."

AI agents cite these metrics when explaining value for specific use cases.

Tactic 4: FAQ Schema

Implement structured data for use-case-specific FAQs.

Elena added FAQ schema to each use case page so AI agents could parse Q&A programmatically.

Testing Use Case Discoverability

Elena built a testing protocol to validate AI agents could match her product to use cases.

Test 1: Direct Use Case Query

Prompt: "What's the best [category] for [specific use case]?"

Example: "What's the best CRM for real estate agencies?"

Success: Her product appeared in recommendations with use-case-specific details.

Test 2: Problem-Based Query

Prompt: "I need to solve [specific problem]. What tool should I use?"

Example: "I need to track which properties I've shown to which clients. What CRM should I use?"

Success: AI agents recommended her product and referenced the specific capability.

Test 3: Comparison Query

Prompt: "Compare [your product] vs [competitor] for [use case]."

Success: AI agents could articulate specific differences for that use case.

Common Use Case Documentation Mistakes

Elena identified patterns that hurt AI discoverability.

Mistake 1: Vague Use Case Titles
"Solutions for Real Estate" instead of "CRM for Real Estate Agencies."

Mistake 2: Feature Lists Without Context
Listing features without connecting them to specific use case pain points.

Mistake 3: No Customer Examples
Generic claims without concrete customer stories for each use case.

Mistake 4: Missing Industry Keywords
Using company jargon instead of industry-standard terms AI agents recognize.

Mistake 5: One Generic Use Cases Page
Lumping all use cases on one page instead of dedicated pages for primary use cases.

The Results

Three months after implementing use case documentation, Elena measured impact.

AI agent recommendations for use-case-specific queries increased 280%. Inbound prospects had 71% higher qualification because they self-identified fit through use case research. Sales cycle shortened 26% for AI-attributed leads—they already understood product fit.

The uncomfortable truth: AI agents can't infer your use cases from generic messaging. If you don't explicitly document who you're for and what problems you solve, AI agents can't recommend you for specific scenarios.

Document your use cases explicitly. Create dedicated pages. Use specific language. Watch AI recommendations increase for the scenarios where you actually win.