Operators are starting to use AI to build custom tools for their teams — not by writing code, but by prompting. A sales ops manager builds a custom lead scoring view in an afternoon. A support team lead builds a custom ticket routing tool in a day. An analyst builds a custom reporting dashboard by describing what they need.
These aren't edge cases. They're the direction the market is moving. And every one of those custom tools is a workflow that used to be a SaaS feature.
The strategic implication is stark: if customers can prompt their way to a feature, that feature's standalone value approaches zero. You can't charge $50/user/month for a feature set that costs $0 to recreate with an AI code assistant.
So what do you actually sell?
You sell the context layer. The features customers prompt into existence are isolated. Your product holds the context that makes those features meaningful — historical data, cross-functional integrations, organizational structure, workflow state. Context can't be prompted. It has to be built over time.
You sell the trust and accountability layer. Who owns the outcome when an AI-built feature produces a bad result? Enterprise buyers are discovering that the answer can't be "nobody." Your product provides the accountability layer — the audit trail, the governance structure, the SLA that says someone is responsible.
You sell the expertise layer. Domain expertise embedded in your product — the workflow logic, the industry-specific rules, the edge case handling — is the residual value after features commoditize. This expertise comes from years of customer conversations, support tickets, and product decisions. It's not promptable.
You sell the network layer. If your product connects customers to each other — through benchmarking, marketplaces, peer networks, or shared data — that network isn't recreatable by prompting.
When every feature becomes a prompt, your product's value is everything that isn't a feature. Build that.