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Lenny's 🏦 Vault

Choose Go-to-Market Model Based on Buyer Behavior

by Brett Taylor on August 2, 2025

Brett Taylor outlines three proven go-to-market models for AI products, emphasizing the importance of choosing the right approach based on your product category and buyer dynamics.

Three Proven Go-to-Market Models

  1. Developer-Led

    • Works when your product is a platform used by engineers
    • Appeals to individual engineers within the CTO's department who have latitude to choose solutions
    • Particularly effective when selling to startups where engineers have freedom to select services
    • Examples: Stripe and Twilio pioneered this approach
    • When it fails: Doesn't work for products targeting line-of-business users who lack dedicated engineering teams

  2. Product-Led Growth (PLG)

    • Users sign up directly from website, often with a trial period
    • Purchase begins with a few seats via credit card
    • Critical requirement: Works only when your user and buyer are the same person
    • Ideal for small business software (e.g., early Shopify) where decision-makers are also users
    • When it fails: Breaks down when buyer and user are different people (e.g., expense reporting where employees use but finance departments buy)

  3. Direct Sales

    • Traditional enterprise sales motion targeting lines of business
    • Requires dedicated sales team engaging directly with buyers
    • Examples: Oracle, SAP, ServiceNow, Salesforce, Adobe
    • Coming back into fashion for AI products because many AI opportunities involve separate buyers and users
    • When it works best: Complex products where the value needs explanation and the buyer isn't the end user

Common Founder Mistakes

  • Choosing a go-to-market motion without understanding the actual purchasing process
  • Not thinking through how customers evaluate the value of the software
  • Avoiding direct sales due to personal preference, even when it's the right approach
  • Trying to force PLG when the buyer and user are different people

AI-Specific Considerations

  • Many AI opportunities require direct sales because buyers and users are often different
  • Founders, especially those from technical backgrounds, may need to embrace sales even if it's uncomfortable
  • The right go-to-market approach should match how decisions are made in your target customer organizations

Outcome-Based Pricing for AI

  • AI enables measurable outcomes, making outcome-based pricing more feasible
  • Example: Sierra charges based on successful customer service resolutions rather than usage
  • Aligns vendor and customer incentives around measurable business outcomes
  • Requires building systems that can track and verify outcomes reliably
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