AI is no longer a tool, it’s a teammate. Every high-performing company starts in chaos and scales toward predictable revenue. This is the GTM maturity curve. The 5 stages every teams go through, the pain points at each, and how AI agents and humans work together to drive growth.
Stage 1: Ad Hoc (The Wild West) #
We don’t really have a process. We just sell.
Pains: #
- No clear ICP definition
- No primary channel
- Random leads, inconsistent follow-ups
- Founders do everything manually
Humans: talk to users #
- Founders sell, guess ICP, test scripts manually
AI Agents: basic assist #
- Draft outreach copy
- Enrich leads
- Speed up ICP testing
AI Contribution: #
- ~10%, Humans run the show, AI supports
Stack #
- Google Sheets to track conversations with prospects
- ChatGPT to write and iterate on the messaging
- Clay for list building and data enrichment
Stage 2: Undefined (The GTM Fog) #
We’re active, but it’s messy.
Pains: #
- CRM exists but isn’t used properly
- No clarity on what works
- Sales and marketing not aligned
Humans: Validate what’s working #
- Early GTM hire qualifies and closes deals
- Founders still drive GTM but focus on ops and basic reporting
AI Agents: Automation + feedback loop #
- Take notes on calls, summarize meetings
- Identify what channels or personas work
- Automate follow-ups
AI Contribution: #
- ~25%, Humans experiment, AI makes them faster
Stack #
- Hubspot for GTM tracking and sequences
- Instantly for email automation
- Clay for list building and data enrichment
Stage 3: Progressive (Repeatable Motion Begins) #
We’re starting to see what works and double down.
Pains: #
- Manual processes slow things down
- Friction in handoffs
- Marketing, Sales, CS not fully in sync
Humans: Scale what works #
- Full-stack AEs own enterprise deals
- Leadership begins to focus on metrics
- GTM Engineer starts building custom AI agent
AI Agents: GTM Co-pilot #
- Surface intent signals (website visits, product usage, job changes)
- Enrich and prioritize leads automatically
- Route opportunities based on territories, rep capacity, or deal size
- Suggest next-best action and sequences
- Sync golden records across CRM, enrichment, and engagement tools
AI Contribution: #
- ~50%, AI is now a co-pilot. Humans drive outcomes.
Stack #
- Hubspot/Salesforce is fully adopted and becomes the source of truth
- Cargo to build custom AI agents for scoring, enrichment, lead routing
- Outreach to pilot the work of the fullstack AEs
- Gong to analyze talk tracks, coach reps
Stage 4: Mature (The GTM Engine) #
We’re aligned across functions and predictably growing.
Pains: #
- Cross-team orchestration becomes complex
- Scaling personalization is hard
- Holistic understanding of the engine
- Clear attribution model
Humans: Upsell & strategic deals #
- Leadership drives strategy
- Full-stack AEs own expansion and entreprise deals
- GTM Engineers manage AI workforce
AI Agents: Multi-agent orchestration #
- Specialized AI agents collaborate across the all funnel: qualification, routing, upsell, reactivation
- AI defines when they need to handle the lead vs an AEs
- Coordinate campaigns, track pipeline health, enforce SLA handoffs
- Suggest coaching points from calls
AI Contribution: #
- ~75%, AI runs the GTM engine. Humans supervise and improve it.
Stack #
- Cargo to manage AI workforce and human workforce
- Hubspot as the unified system of record
- Outreach for multi-channel account engagement
- Gong for revenue intelligence
- Hex for GTM analytics and dashboards
Stage 5: Self-Optimizing (Compounding Growth) #
Our GTM system evolves with the market.
Pains: #
- Staying agile while scaling
- Balancing fast growth with experimentation
- Hiring fast enough
Humans: High-level strategy + market bets #
- Leadership sets high-level priorities
- GTM Engineers maintain the AI architecture
- Full-stack AEs close high-touch strategic deals
AI Agents: Continuous self‑optimization #
- Run autonomous A/B tests
- Optimize playbooks continuously
- Forecast revenue, recommend hiring
- Trigger next best actions from customer behavior
AI Contribution: #
- ~90%+, AI drives growth, humans steer direction.
Stack: #
- Same stack than for stage 4
Key Takeaways #
- 5-stage GTM maturity curve with AI contribution increasing from 10% → 90%+: Stage 1 Ad Hoc (founders + ChatGPT, 10%), Stage 2 Undefined (GTM hire + automation, 25%), Stage 3 Progressive (Full-Cycle AEs + AI co-pilot, 50%), Stage 4 Mature (multi-agent orchestration, 75%), Stage 5 Self-Optimizing (autonomous AI, 90%+)—humans shift from “doing everything” to “driving strategy”
- Stage 1-2 (Ad Hoc → Undefined): Founder-led chaos → early GTM structure: No ICP/process → CRM exists but messy, sales/marketing not aligned. AI = basic assist (draft copy, enrich leads, ChatGPT + Clay). Humans validate what works, AI makes them faster
- Stage 3 (Progressive): Repeatable motion begins, AI becomes co-pilot (50%): Full-Cycle AEs own enterprise deals, GTM Engineer builds custom AI agents (surface intent signals, enrich/prioritize leads, route by territory/capacity, sync golden records across tools). Pain: Manual processes slow things, handoffs friction. Stack: Salesforce/HubSpot + Cargo (AI agents) + Outreach + Gong
- Stage 4 (Mature): GTM engine with multi-agent orchestration (75%): Specialized AI agents collaborate across funnel (qualification, routing, upsell, reactivation), AI decides when to handle vs. AE escalation, coordinates campaigns/pipeline health/SLA enforcement. Humans = leadership strategy, Full-Cycle AEs on expansion/strategic deals, GTM Engineers manage AI workforce. Pain: Cross-team orchestration complexity, scaling personalization
- Stage 5 (Self-Optimizing): Compounding growth with autonomous AI (90%+): AI runs autonomous A/B tests, optimizes playbooks continuously, forecasts revenue/hiring needs, triggers next-best actions from customer behavior. Humans = high-level priorities, GTM Engineers maintain architecture, AEs close high-touch strategic deals. Pain: Staying agile while scaling
Frequently Asked Questions #
AurelienJun 20, 2025
