This is a reprint of an article originally published in Fast Company authored by Dag Peak.
When you think about conversation, do you imagine a tête-à-tête over a table for two in a cozy corner of a restaurant? Or does it look like sauntering through town holding your phone like a slice of pizza, sharing both halves of the conversation with all passersby? Truth is, it could be both.
Conversation and its rules of engagement are evolving—literally—as we speak.
By my last count, there are more than 650 conversational artificial intelligence apps available. In some conversations, the couple has become a throuple—with an AI translating or transcribing what’s said or joining in to offer timely advice. In other conversations, AI creates the one-on-one, just a person talking to a machine.
A LANGUAGE PROBLEM
It’s clear there’s the will, and the way, for AI to power all kinds of elevated, useful, and novel conversational experiences.
Before we get much further down this line of thinking, we must face up to something: We have a language problem. Conversational AI is a catch-all label for everything from voice agents and interactive voice response to whisper coaching and transcription services. Lumping it all together feels convenient. The confusion slows real progress.
Treating all conversational AI as one thing is like calling every vehicle a car. You lose the ability to choose correctly, design deliberately, or drive safely. The result is predictable: Vendors oversell and buyers purchase the wrong thing. More fundamentally, teams get their wires crossed on the experiences they are trying to create.
To clear up the confusion, it’s time to think of conversational AI as three distinct types.
1. POST-CONVERSATIONAL INTELLIGENCE
In this model, the conversation happens first. AI runs later.
Think about capturing what is important for the call you just had: transcripts, summaries, and action items. Post-conversational intelligence can help with analytics on customer calls. It can score sentiment, surface quality issues, and reveal patterns across thousands of calls—what customers are really calling about, how issues resolve, and where friction lives.
Post-conversational intelligence excels at compliance, record-keeping, coaching, and insight generation. Its value compounds over time, not in the moment.
The metric that matters most here is downstream usefulness from the captured insights. Questions it can answer include: Is this reducing rework? Are people acting on the insights? Is this data available to the applications on which your business runs?
It’s important to extend post-conversational intelligence beyond calls that may go through a contact center to include any high-value conversation that can glean real business insights to use now or later.
2. AI-HANDLED CONVERSATIONS
Here, AI answers the call.
The person talks directly to the machine, not another person. This includes human-like voice bots that answer questions, complete tasks, schedule appointments, check order status, and route calls.
AI-handled conversations work best for high-volume, low-complexity interactions. Done well, they extend availability to 24/7 and materially reduce cost-to-serve.
The metrics that matter here are containment rate with customer satisfaction, clean escalation paths, and the ability to identify how quickly the caller completes the job.
The most common mistake is deploying AI before the business is ready. If knowledge bases are incomplete and workflows are brittle, AI becomes a frustrating gatekeeper rather than an always-available, always-helpful agent.
3. AI-ASSISTED CONVERSATIONS
AI-assisted conversations keep humans at their center. Two people talk to each other, with AI adding value to that conversation—supporting, detecting, and augmenting in real time.
On customer calls, this could include whisper coaching for sales and support, live objection-handling prompts, and compliance nudges like, “You need to read this disclosure.”
In emergency scenarios, AI can detect stress, changes in breathing patterns, or background signals like fire alarms, and then surface that insight instantly to human operators.
Based on my observations in the telecommunications industry, AI-assisted conversation is currently the least implemented category of conversational AI. It moves beyond what was said into nuance, context, and intent, in real time. It adds machine leverage precisely where timing, judgment, and risk matter most.
The metrics here look different and include de-escalation of interactions, better safety outcomes, fewer critical errors, improved sales performance, and increased compliance rates.
AI-assisted conversations are different from post-call analytics or full automation. Their power lives in augmentation, not replacement.
WHY THIS TAXONOMY MATTERS
Knowing which kind of conversational AI you are building—or buying—matters. Using the wrong one at the wrong moment is where most implementations fail.
Once you separate these use cases, architecture choices get cleaner, expectations become realistic, and trust increases. You stop forcing one model to do a job for which it was never designed.
Conversational AI isn’t one thing—it’s three. Progress depends on knowing the difference.
