AI-native, or AI bolted on?
Both approaches put AI in front of customers. They differ at the handoff, and that is where most of the experience is won or lost.
| Aspect | Legacy CCaaS | CCaaS + AI add-on | Dialog365 (AI-native) |
|---|---|---|---|
| Where the AI sits | Bolted on beside the stack | Add-on module, separate vendor | One system with routing and assist |
| Context at handoff | Rebuilt, often lost | Partial, via integration | Carried intact, transcript and summary |
| Routing decisions | Queue order and skill tags | Queue order, AI suggests | Intent, sentiment, skill and past outcomes |
| Customer repeats themselves | Usually | Sometimes | No |
| Agent support during a call | Knowledge base search | Suggested replies | Emotion, suggested action and live coaching |
| Voice quality | IVR menus and DTMF | Scripted bot flows | Human-like, sub-second, interruptible |
| Time to change a flow | Weeks, professional services | Days, separate tooling | Same platform, no second vendor |
| Vendors to manage | One, plus AI vendor | Two or more | One |
Questions about the comparison
How teams evaluate the difference in practice.
What does AI-native actually mean?
The voice agent, the routing engine and the agent-assist layer are one system rather than separate products wired together, so they share context instead of passing it between vendors.
Is a bolt-on AI add-on not the same thing?
It gives you AI answers, but the AI and the routing engine remain separate systems. Context has to be handed across that boundary, which is where it is usually lost or rebuilt.
Do we need to leave our current CCaaS to benefit?
Not immediately. Dialog365 can take defined contact types alongside an existing platform, which is a common way to measure the difference before committing.
How do we compare this fairly in a bake-off?
Measure the handoff. Time an escalation end to end and check whether the agent receives the transcript and summary before they speak, and whether the customer repeats anything.
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