20 Sprints to Full GTM Autonomy — from first signal to autonomous Revenue OS.
Each sprint is 2 weeks. Start date: February 12, 2026. The GAI (GTM Automation Index) is tracked separately for each ICP cluster because automation potential varies by company maturity.
GTM Automation Index (0-100%) tracked per cluster. Higher automation potential for simpler GTM motions (Founder-Led), lower for complex enterprise orgs (Revenue Machine).
GAI Calculation
GAI = (Σ (Weightc × Autonomyc) / Σ Weightc) × 100Categories: Signal (20%) · Content (25%) · Orchestration (25%) · Conversion (15%) · Intelligence (15%)
Real-time automation index for each ICP cluster based on shipped capabilities (Sprint 3).
Current GAI · Sprint 3
Target: 92% at Sprint 20 (5% reached)
1-3 people GTM org. Highest automation potential.
Current GAI · Sprint 3
Target: 88% at Sprint 20 (3% reached)
3-10 people. Building first scalable processes.
Current GAI · Sprint 3
Target: 80% at Sprint 20 (1% reached)
10-30 people. Multi-team orchestration.
Current GAI · Sprint 3
Target: 65% at Sprint 20 (0% reached)
30-100+ people. Enterprise efficiency at scale.
Learning, prototyping, first user value through signal ingestion & identity resolution, and the first GAI dashboard.
Market research, technology evaluation, and conceptual design. Define the product vision, ICP clusters, GAI tracking framework, and overall system architecture.
GAI Target after Sprint 1
Build the first functional prototype — application scaffold, design system, website, demo environment, and deployment pipeline. Validate the concept with a working product shell.
GAI Target after Sprint 2
Deliver the first tangible user value. Build passive signal connectors (website visitor identification, LinkedIn tracking) and resolve anonymous signals to known contacts and companies.
GAI Target after Sprint 3
Build the first operational dashboard showing signal volume, identity resolution rates, and the GAI index tracking per ICP cluster. This becomes the heartbeat screen.
GAI Target after Sprint 4
Intent scoring, dynamic ICP, LLM-powered messaging, and automated personalization.
Separate revenue intent from noise. Build a rule-based scoring engine that classifies signals by intent strength and maps them to ICP clusters.
GAI Target after Sprint 5
Auto-generate and refine ICP profiles based on closed-won deal patterns. The system learns which company profiles convert best and adjusts targeting.
GAI Target after Sprint 6
LLM-driven generation of multi-channel outreach templates. Each message is tailored to the persona, ICP cluster, and signal context.
GAI Target after Sprint 7
Context injection from real-time company events: funding rounds, job postings, product launches, executive changes. Every outreach feels hand-crafted.
GAI Target after Sprint 8
Email sequences, LinkedIn outreach, omni-channel orchestration, and meeting booking.
High-volume email outreach with automated A/B testing. Full sequence builder with branching logic based on recipient behavior.
GAI Target after Sprint 9
Add LinkedIn as a second outreach channel. Build the orchestration layer that coordinates email and LinkedIn touches in a unified sequence.
GAI Target after Sprint 10
Intelligent channel switching based on engagement data. If email isn't working, automatically escalate to LinkedIn or direct mail. Build the autonomy decision engine.
GAI Target after Sprint 11
Close the loop: from signal to booked meeting. Cal.com integration for direct meeting booking. AI handles scheduling negotiation via email.
GAI Target after Sprint 12
Interactive deal rooms, buyer analytics, e-signatures, and CRM bidirectional sync.
Auto-generate personalized micro-sites for each prospect. A digital deal room with relevant content, pricing, case studies, and next steps - all tracked.
GAI Target after Sprint 13
Deep analytics on how prospects interact with deal rooms. Which pages they visit, how long they spend, what they share internally. Intent signals from buying behavior.
GAI Target after Sprint 14
Autonomous proposal generation and e-signature integration. AI drafts proposals based on deal context, human approves, prospect signs digitally.
GAI Target after Sprint 15
Bidirectional CRM sync and collaborative features within deal rooms. Buyer and seller can chat, comment, and align on next steps within Gekko.
GAI Target after Sprint 16
ML-based scoring, competitive intel, customer success, and full Revenue OS autopilot.
ML-based scoring that predicts conversion probability. Win-pattern analysis identifies which signals, behaviors, and ICP attributes predict success.
GAI Target after Sprint 17
Automated competitive intelligence. Tech-stack changes as triggers, auto-generated battlecards from win/loss data, and real-time competitor alerts.
GAI Target after Sprint 18
Extend autonomous execution into post-sale. Churn risk detection, renewal automation, expansion signal identification, and onboarding acceleration.
GAI Target after Sprint 19
All modules live. Gekko operates as a full Revenue OS with autopilot mode for mature motions. Revenue-as-Code: GTM playbooks defined in YAML and versioned in Git.
GAI Target after Sprint 20
Monday Week 1 — Goal setting, GAI target definition, and sprint scope alignment.
Friday Week 2 — Functional demo of shipped features to stakeholders and design partners.
Continuous deployment via CI/CD. Every merged PR ships to production.