◆ Comparison
AI-native vs. legacy support platforms.
Not every platform that mentions “AI” was built for it. Here's how an AI-native platform compares to a legacy ticketing system with AI added on, and to a narrow point-solution chatbot.
| AI-native platform | Legacy + AI bolt-on | Point-solution chatbot | |
|---|---|---|---|
| Designed around AI from day one | |||
| Full account context before every reply | |||
| Confidence-gated autonomous resolution | |||
| Omnichannel inbox, one queue | |||
| Revenue signal detection & routing | |||
| Automatic article creation from resolutions | |||
| Predictable ticket-based pricing | Per-seat | Per-usage | |
| Time to see resolution impact | Weeks | Months | Weeks |
| Cost as conversation volume grows | $300/mo + $0.50/ticket | Scales with seats | Scales with usage |
Full support Partial / limited Not supported
Legacy + AI bolt-on
Why legacy platforms fall short
Legacy ticketing was designed around a queue-and-agent model built well before generative AI. Adding a reply-suggestion feature on top doesn't give it account context, revenue-signal routing, or confidence-gated resolution — those require the underlying architecture to be built for AI, not retrofitted.
Point-solution chatbot
Why a chatbot only solves part of it
A narrow chatbot can deflect FAQs quickly, but it typically operates without CRM or billing context, can't take real actions like updating a subscription, and has no way to route churn or upsell signals to the teams that need them.
Comparison FAQ
Common questions.