Per-seat pricing assumes the user is doing the work, and the value scales with the number of users. AI changes that assumption. When the product does the work, the value scales with the work done, not the seats logged in. Per-seat pricing for an AI product is a slow leak, both ways: customers feel overcharged for low usage and undercharged for high usage.
What outcome-based pricing looks like
Price the unit of value the product produces. Resolved support tickets. Drafts generated. Tasks completed. The customer pays for what they got, not for who could have logged in. The unit price reflects the cost of producing the outcome plus the margin the value supports.
What it changes about the relationship
Customers stop treating the product as a fixed cost and start treating it as a variable input. Their adoption curve aligns with their value curve. You stop competing on per-seat parity and start competing on the value of each outcome. Expansion happens organically as they use the product more.
Where the model breaks
If the unit is unpredictable to the customer, they will hesitate to scale. The fix is a hybrid: a small platform fee for predictability, plus outcome pricing for scaling. The customer gets a known floor and a known cost per unit beyond it.