The AI GTM Engine: Problem-Space Intelligence for Answer Engines
A systematic approach to building answer engine visibility. Seven phases that take you from problem-space discovery to measurable citation ownership.
The GTM Engine isn't a one-time project. It's an operating system for continuously earning visibility in AI answer engines.
The Seven Phases
Problem-Space Discovery
Map the buyer questions and problem territories that matter to your market. This becomes the foundation for all targeting and content decisions.
Outputs
- Question library (30-50 core questions)
- Problem-space clusters
- ICP-to-question mapping
Competitive Citation Analysis
Understand who currently owns answers in your problem-spaces. Identify gaps where you can establish presence and territories where you need to compete.
Outputs
- Citation share by topic
- Competitor content audit
- Opportunity prioritization
Content Architecture
Design a content structure optimized for AI citation. Answer-first formats, FAQ clusters, and systematic topic coverage.
Outputs
- Content taxonomy
- Page templates
- Internal linking strategy
Asset Development
Build the content assets that will earn citations: pillar pages, answer blocks, proof points, and sponsored-answer-ready creative.
Outputs
- Pillar content
- FAQ content
- Proof point library
Distribution & Amplification
Get content indexed, build third-party validation, and prepare for paid answer engine placements when available.
Outputs
- Publishing calendar
- PR/backlink plan
- Paid readiness assets
Measurement Infrastructure
Set up tracking for citation frequency, visibility share, and pipeline impact. Build the reporting cadence before data exists.
Outputs
- Tracking setup
- Reporting templates
- Attribution assumptions
Optimization Loop
Continuously monitor citation performance, identify winning patterns, and iterate on content and targeting.
Outputs
- Weekly citation tracking
- Content updates
- Strategy refinements
Why “problem-spaces,” not keywords?
Keywords are an artifact of how search engines work. Answer engines respond to questions, contexts, and problem framing. The shift requires thinking in terms of:
Buyer questions
What are people actually asking when they have the problem you solve?
Problem contexts
What situations trigger these questions? What's the surrounding context?
Evaluation criteria
What are buyers comparing when they ask these questions?
Decision triggers
What moves someone from asking questions to taking action?
[GTM Engine Diagram]
Visual representation of the seven-phase cycle