Go-to-market in the AI era
A revenue operating system for shorter learning loops
Design an end-to-end AI go-to-market operating system for B2B SaaS, from ICP and positioning to AI search discovery, hybrid PLG/SLG, pricing, metrics, and a 90-day roadmap.
Source · Handbook · Go-to-market in the AI era · Source reviewed 2026-06-20

1. AI GTM principles — four shifts
AI is an operating system for shortening market-learning loops, not simply reducing headcount.
2. Market, ICP, positioning & messaging
Define whom to sell to and what language to use through fields, scores, and statements.
- A four-layer ICP and four separate scoresSeparate firmographic, technographic, trigger, and pain data from fit, intent, readiness, and expansion
- Three positioning statements & stage-specific messagesFix the audience, problem, and outcome, then change each message’s role by buying stage
- Practice: prioritize accounts and choose positioningSeparate fit and intent, shape three positioning statements, and judge stage-specific messages
- ICP & messaging checkCheck score separation, positioning statements, and stage-specific messaging
3. AI-search discovery & the revenue engine
Design discovery surfaces as trust assets and connect product signals, sales, and pricing into one revenue engine.
- AI-search and content discoveryFive discovery surfaces, citation criteria, and five content portfolio types
- Hybrid PLG+SLG and pricing & packagingUse product signals as sales triggers and price by access, consumption, or value
- Discovery & revenue-engine checkCheck citability, content production, hybrid motions, and pricing models
4. Metrics, operating cadence & a 90-day roadmap
Manage revenue-learning speed with metrics and meeting cadence, then turn it into a first 90-day plan.
5. 신호·PoC·확장 실행
AI GTM을 승인 가능한 수익 운영 시스템으로 완성합니다.
Final challenge
Prove your mastery with a comprehensive course quiz.