AI startups must decide who owns marketing: in-house teams champion rapid iteration and product-sited data, or external agencies scale demand generation with specialized channels. The choice shapes product feedback loops, budget efficiency, and hiring risk.

This article provides concrete, action-ready strategies for both paths and a practical framework to pick the best approach for your stage, data constraints, and go-to-market goals...

Evaluate in three steps: goals, data, capabilities. Then map to in-house, agency, or hybrid.

• In-house: Build a product-led outbound and content engine. Roles: Growth Lead, Content Producer, Performance Marketer, Operations.
• Agency: Use a performance-focused partner for paid media and demand-gen.
• Hybrid: Core marketing in-house; partner for specialized experiments (ABM, paid social, SEO).

Concrete playbooks by stage:

• Seed: 3-month in-house content pilot; agency handles paid experiments within a strict budget.
• Series A: scale with a hybrid model; in-house analytics, agency for multi-channel demand gen.
• Series B+: formal demand-gen model; in-house full-stack marketing with agency for overflow.

Metrics to govern: CAC, LTV, time-to-first-win, NPS, data quality.

• Unclear objectives and missing SLAs; no agreed revenue targets.
• Overloading scope; trying to do branding, performance, and product marketing with insufficient staffing.
• Underfunding data integration; no event tracking or clean CRM.
• Relying on a single channel; missing cross-channel attribution.
• Vendor lock-in without exit criteria; long-term contracts for uncertain needs.

• RFP with decision matrix: cost, speed, domain expertise in AI, data privacy.
• Pilot first: 90-day pilot with fixed milestones; define success criteria (ROAS, lead quality, time to value).
• In-house starter kit: create a Growth Ops playbook, dashboards, a data layer, and a weekly marketing huddle.
• Agency collaboration: require weekly performance reports, access to data, joint sprint planning, and clear escalation paths.