Outcome-Based Pricing Model: The Ideal Quadrant

The most successful companies master both market share and wallet share simultaneously rather than focusing on just one engine of growth. For AI companies specifically, monetization strategy must be considered from day one, as early pricing decisions anchor customer expectations permanently.

The Pricing Power Matrix: A Two-by-Two Framework

The ideal pricing model depends on two key dimensions:

• Attribution: How clearly can you connect your product's value to specific business outcomes?
• Autonomy: How independently can your product operate without human intervention?

Four Pricing Quadrants:

1. Low Attribution, Low Autonomy (Bottom Left)

   • Best model: Seat-based/subscription pricing
   • Example: Traditional SaaS products
   • Challenge: Limited pricing power due to inability to prove direct impact
   • Strategy: Work to increase attribution capabilities to move rightward

2. High Attribution, Low Autonomy (Bottom Right)

   • Best model: Hybrid pricing (base subscription + consumption)
   • Example: Cursor (coding assistant)
   • Approach: Maintain seat-based model for core access but add consumption elements (AI credits/tokens)
   • Value proposition: "We can prove our impact, but humans remain in the loop"

3. High Autonomy, Low Attribution (Top Left)

   • Best model: Usage-based pricing
   • Example: Backend/infrastructure AI products
   • Characteristic: Fully autonomous but difficult to connect directly to business KPIs
   • Strategy: Usage becomes a proxy for value delivered

4. High Attribution, High Autonomy (Top Right)

   • Best model: Outcome-based pricing
   • Example: Intercom Fin (99¢ per AI-resolved support ticket)
   • Power position: Can capture 25-50% of value created (vs. 10-20% in traditional SaaS)
   • Only ~5% of companies currently achieve this position

Moving Toward Outcome-Based Pricing

To increase pricing power, companies should:

1. Increase attribution capabilities:

   • Build functionality that tracks impact on customer KPIs
   • Create dashboards showing direct value attribution
   • Conduct regular value audits with customers
   • Co-create ROI models with customers from day one

2. Increase autonomy:

   • Develop more agentic capabilities that remove humans from the loop
   • Build fully autonomous workflows that deliver complete outcomes
   • Design products that can operate independently

3. Structure POCs around business cases:

   • Frame pilots as "co-creating an ROI model" rather than testing functionality
   • Charge for POCs to filter out non-serious buyers
   • Contextualize pricing discussions around value (e.g., "We typically unlock $10M in value and take 10%")

Practical Negotiation Strategies

When negotiating deals:

1. Master gives and gets:

   • Never give concessions without getting something in return
   • Example "get": Request a value audit every six months to document ROI

2. Focus on value selling:

   • Create needs rather than just discovering them
   • Build affirmation loops where customers verbalize the value they see
   • Co-create ROI models with customers from day one

3. Present options, not single prices:

   • Offer multiple pricing tiers or models to shift conversation from price to value
   • Example: "100K + 10% of value created" OR "500K fixed fee"
   • Taper concessions (15%, then 5%, then 2%) to signal negotiation endpoints

The winners in AI will be those who master monetization from day one, avoiding the trap of underpricing that trains customers to expect more for less.