For a decade, B2B SaaS growth has been split between Sales-Led (scale headcount) and Product-Led (scale free tiers). Both are fundamentally linear. SLG scales with team size. PLG scales with product surface area.
AI-Led Growth removes the linear constraint. Pipeline generation becomes a compute problem. AI agents handle identification, enrichment, scoring, outreach, and qualification. Humans handle the high-judgment conversations that close deals.
The technical requirements are different from traditional sales tooling. Real-time enrichment waterfalls. Event-driven automation from intent signals. Confidence scoring on every contact record. Multi-provider data validation before any outreach fires.
Currently the viable model is hybrid: ALG + SLG. AI generates and qualifies. Humans close. The economics favor this heavily over pure SLG because compute costs decline while headcount costs increase.
Based on our audit data (127 B2B SaaS companies), 67% have adopted AI in GTM but only 23% have connected it to measurable pipeline workflows. The gap is almost entirely an infrastructure problem, not a tooling problem.
For a decade, B2B SaaS growth has been split between Sales-Led (scale headcount) and Product-Led (scale free tiers). Both are fundamentally linear. SLG scales with team size. PLG scales with product surface area.
AI-Led Growth removes the linear constraint. Pipeline generation becomes a compute problem. AI agents handle identification, enrichment, scoring, outreach, and qualification. Humans handle the high-judgment conversations that close deals.
The technical requirements are different from traditional sales tooling. Real-time enrichment waterfalls. Event-driven automation from intent signals. Confidence scoring on every contact record. Multi-provider data validation before any outreach fires.
Currently the viable model is hybrid: ALG + SLG. AI generates and qualifies. Humans close. The economics favor this heavily over pure SLG because compute costs decline while headcount costs increase.
Based on our audit data (127 B2B SaaS companies), 67% have adopted AI in GTM but only 23% have connected it to measurable pipeline workflows. The gap is almost entirely an infrastructure problem, not a tooling problem.