Most conversational AI experiences are built for chat interfaces. In-app assistants, browser widgets, dedicated portals. They work well for users who are already inside your product. The problem is that most users are not inside your product most of the time. They are living their lives, and their phone is with them.
SMS open rates sit above 95%, and most messages are read within three minutes of delivery. No push notification required, no app to install, no account to create on a separate platform. The message arrives and the user reads it. That baseline reliability is something no other channel has reliably achieved at scale.
The format fits the model
Conversational AI works best in short, focused exchanges. A question, an answer, a follow-up. SMS enforces exactly that structure. The character limit is not a constraint so much as a forcing function that keeps interactions crisp and makes latency less noticeable. A 160-character reply from an AI feels immediate. A paragraph-length response would feel out of place.
This makes SMS a natural fit for use cases like appointment scheduling bots that confirm a time and ask for confirmation, order status agents that reply to “where is my package” with a tracking update, and simple triage flows that ask a screening question before routing to a human. None of these require a sophisticated UI. They require a reliable two-way channel, and SMS provides that without friction.
The economics change with flat pricing
One reason AI-over-SMS has not been more common is that traditional SMS providers charge per message. A multi-turn conversation can involve five, ten, or more exchanges. At per-message rates, the cost accumulates quickly and unpredictably. Businesses building AI-driven workflows cannot reliably forecast that spend.
Flat monthly pricing per device changes the model entirely. The cost of a conversation with ten turns is the same as a conversation with two. That predictability makes it practical to deploy AI agents over SMS at scale, without designing the conversation to minimize message count.
What this looks like in practice
A healthcare provider deploys a scheduling assistant that texts patients the day before their appointment, collects a confirmation or reschedule request, and updates the booking system automatically. A retailer runs a post-purchase agent that handles common questions about delivery and returns without involving a support agent. A financial services company sends account alerts and handles one-turn responses like blocking a card or confirming a transaction.
In each case, the AI handles the exchange, SMS handles the delivery, and the user never has to open an app. That combination is more powerful than either piece on its own.