PLG AI Funnel: Product-Led Growth in the Agent Era

The Paradigm Shift

Old PLG Funnel:

Landing Page → Free Trial → Activation → Conversion

New PLG Funnel:

Agent Query → Documentation Scan → Feature Match → Recommendation

The buyer's first interaction is no longer your landing page—it's an AI agent scanning your documentation to answer their question.

The Four Stages

Stage 1: Agent Query

What happens: User asks AI "What tool can help me [problem]?"

Optimization goals:

• Brand appears in AI's consideration set

• Correct category association

• Problem-solution mapping exists in AI's knowledge

Tactics:

Action Why It Works

Entity building AI must know your brand exists and what category it's in

Third-party mentions Reviews, comparisons, listicles feed AI training data

Clear positioning "X is a [category] that [primary benefit]" statements

Audit questions:

• Does AI know your brand when asked directly?

• Does AI associate your brand with your category?

• Do competitors appear but you don't?

Tool: entity-builder agent for authority building

Stage 2: Documentation Scan

What happens: AI scans your docs, help center, marketing pages to understand capabilities.

Optimization goals:

• Content is AI-extractable (chunked, structured)

• Answers are front-loaded (not buried)

• Each page passes the "Taco Bell Test" (stands alone)

Tactics:

Action Why It Works

Answer-first structure AI extracts the first sentence as the answer

FAQ sections Pre-formatted Q&A is ideal for extraction

Structured data Tables, bullets, headers signal discrete facts

Standalone sections AI may only see one chunk, not the full page

The Extractability Checklist:

☐ First sentence directly answers the page's implied question ☐ H2/H3 headers are questions or clear topic labels ☐ Tables used for comparisons and feature lists ☐ Each section makes sense without surrounding context ☐ No "as mentioned above" or "see below" dependencies

Tool: llm-optimizer agent for content optimization

Stage 3: Feature Match

What happens: AI matches user's specific needs to your product's capabilities.

Optimization goals:

• Features described in user-problem terms

• Use cases explicitly mapped to capabilities

• Limitations clearly stated (builds trust)

Tactics:

Action Why It Works

Problem → Feature mapping "If you need X, [Product] does Y"

Use-case pages Dedicated pages per job-to-be-done

Integration lists AI checks compatibility requirements

Pricing clarity AI needs to match budget constraints

Feature Documentation Template:

[Feature Name]

Problem it solves: [User problem in their words]

How it works: [1-2 sentence explanation]

Best for: [Specific use cases]

Limitations: [What it doesn't do]

Example: [Concrete scenario]

Anti-pattern: Feature pages that describe functionality without connecting to user problems.

Stage 4: Recommendation

What happens: AI decides whether to recommend your product and how to position it.

Optimization goals:

• Clear differentiation from alternatives

• Social proof AI can cite

• Product tie-backs throughout content

Tactics:

Action Why It Works

Comparison content "X vs Y" pages AI directly references

Quantified outcomes "Reduces time by 40%" > "saves time"

Review presence G2, Capterra reviews influence AI recommendations

Product mentions in answers Every content piece connects back to product

The Product Tie-Back Rule: Every 1-2 paragraphs of educational content should include how your product relates.

• ❌ "Lead scoring helps prioritize prospects"

• ✅ "Lead scoring helps prioritize prospects—[Product] automates this with AI-powered scoring"

Tool: aeo-scorecard skill for measuring recommendation success

PLG × AEO Integration

PLG Stage AEO Concept Metric

Agent Query Entity/Authority AI Visibility %

Documentation Scan Extractability Citation Rate

Feature Match Fact-Density Feature mention accuracy

Recommendation Product Tie-Back AI Share of Voice

Quick Audit Workflow

1. Test 10 queries your buyers ask → Does your brand appear? (Stage 1)

2. Check if AI cites YOUR content → Or competitor/third-party? (Stage 2)

3. Ask AI about specific features → Does it know your capabilities? (Stage 3)

4. Ask "Should I use [Product] for [use case]?" → What's the recommendation? (Stage 4)

Common PLG AI Gaps

Symptom Stage Broken Fix

Brand unknown to AI Query Entity building, third-party mentions

AI cites competitors' content Documentation Improve extractability, answer-first

AI misunderstands features Feature Match Rewrite feature docs with problem framing

AI recommends competitor Recommendation Strengthen differentiation, add social proof

Related Tools

• llm-optimizer

• Deep content optimization for Stage 2

• entity-builder

• Authority building for Stage 1

• aeo-scorecard

• Metrics framework for all stages

• /aeo-workflow

• Full implementation workflow

• query-expansion-strategy

• Understanding query fan-out