The Problem
A single contact is usually split across two worlds that don't talk to each other. First the caller interacts with self-service — the IVR, a voice bot, a chat assistant — where they state their reason, authenticate, maybe try a few things. Then they get to an agent and, more often than not, start again from scratch. When the call ends, the agent writes a wrap-up note in their own words, capturing only what they remember and none of the self-service part.
The result: the customer repeats themselves, the agent does manual after-call work, and the record of what actually happened is partial and inconsistent. The full story of the interaction exists only in fragments.
The AI Use Case
Have AI read the entire interaction — everything the caller did and said in self-service, plus the conversation with the agent — and generate a single, structured summary of the whole thing. One record that captures the journey end to end, written the same way every time.
A good summary pulls together:
- What the caller was trying to do — the reason, in plain language.
- What happened in self-service — steps taken, information given, what did or didn't get resolved before the handoff.
- What was discussed with the agent — the key points, decisions, and any commitments made.
- The outcome and next steps — resolved, pending, or a follow-up owed.
Where the Summary Earns Its Keep
Live, at the point of handoff
When self-service passes the call to an agent, a short summary of what already happened lands with it — so the agent opens the conversation already knowing the reason and context, and the caller doesn't have to repeat themselves. That single change removes one of the most common customer complaints about contact centres.
At wrap-up
Instead of typing notes from memory, the agent gets a draft summary to check and tweak. After-call work shrinks, notes get more consistent, and agents get back to the next customer sooner.
In analytics and the record
A consistent, structured summary of every interaction is far easier to search, report on, and learn from than free-text notes. It also feeds naturally into the kind of 100%-of-interactions analysis that AI makes possible.
Keep the human in the loop for anything that matters. A summary is a draft, not gospel. For records that drive decisions or go on a customer's file, let the agent review and correct it — summarisation models can miss nuance or overstate certainty. The time saved comes from editing a good draft, not from trusting it blindly.
The Value
- No more repeating themselves — context carries across the handoff.
- Less after-call work — agents edit a draft instead of writing from scratch.
- Consistent records — every interaction summarised the same structured way.
- Better analytics — structured summaries are easier to search and learn from.
- Smoother experience — the whole contact feels joined-up rather than fragmented.
What You Need to Build It
- Access to both halves — self-service transcripts/events and the agent conversation, tied to one contact.
- A summarisation model — an LLM prompted to produce a consistent, structured output.
- Delivery points — the summary surfaced at handoff and at wrap-up, and stored on the record.
- A review step — a quick agent check for anything consequential.
Explore More AI Use Cases
Part of a growing series of practical AI use cases for contact centres. Browse the full set on The AI Use Cases hub, and see the wider picture in 15 ways AI can transform your contact centre. Got one you'd like covered? Get in touch.