# AI conversation insights: sentiment, signals and team review

Review customer sentiment, recurring topics, staff communication tone and observed recoveries in the enabled Boni-only pilot, using fictional examples and source context.

## Understand what your group conversations need

AI conversation insights adds a management review to Bow Chat Groups Monitoring. It helps you inspect customer sentiment, recurring topics, staff communication tone and observed replies or recoveries without treating a fast reply as proof of a good customer experience.

**Availability:** currently in the Boni-only pilot. This guide explains the enabled workflow; it does not mean the add-on is enabled for every workspace. [Ask for a demonstration](https://bow.chat/solutions/whatsapp-conversation-insights).

The illustrations below use **fictional demo data**, not customer records or measured business results. They simplify the product rather than reproduce every control.

[Watch the narrated explainer](https://bow.chat/video/ai-conversation-insights).

## Open a report

1. Sign in to [panel.bow.chat](https://panel.bow.chat) in an enabled workspace.
2. Open **Groups Monitoring → SLA reports → AI insights**. Some menus still use **Group SLAs**.
3. Choose **Try demo** to explore fictional messages, or select a monitored group and date range and explicitly choose **Analyse group**.
4. Read the coverage and limitations before interpreting the report. Check the selected period, included messages, participant roles and any partial or unknown coverage.
5. Move between **Overview**, **Customer signals**, **Topics & trends** and **Team conversations**.
6. Use **Download report** to export the current report. Treat the CSV with the same care as the source conversation; do not post a real export publicly.

Analysis is on demand. Opening a report or changing report views does not start another analysis. The report does not send a message, change a conversation or change response/resolution SLA state. If the feature is unavailable in your workspace, ask about a demonstration rather than changing another workspace's access.

## Overview: start with the customer

![Simplified overview with fictional demo counts](https://bow.chat/media/ai-conversation-insights/overview-phone.svg)

The manager briefing highlights groups with signals to review. Negative customer messages, groups to review and observed recoveries answer different questions; they are not a single satisfaction score.

In the fictional example, 4 of 13 customer messages are negative, 3 groups have customer signals and 2 recoveries are observed. Customer and staff sentiment remain separate, so a helpful staff response cannot conceal a frustrated customer. Unknown classifications remain unknown.

**Before acting:** read the denominator and coverage, not just the headline count. Inferred sentiment is not NPS or CSAT and is not a survey response.

## Customer signals: read the concern in context

![Simplified customer signal with a fictional late-delivery message](https://bow.chat/media/ai-conversation-insights/signals-phone.svg)

This view surfaces frustration, escalation and cancellation signals for a manager to review. Source references help you return to the conversation and check what was actually said.

For example, a fictional customer says a delivery is two days late and nobody has provided an update. A manager should read the exchange, establish the current situation and choose an appropriate next action.

**Before acting:** these are not churn predictions or instructions to send a message. A flagged phrase can be quoted, sarcastic, multilingual or missing context. Read the original messages before drawing a conclusion.

## Topics & trends: find what keeps coming up

![Simplified fictional topic counts, led by delivery](https://bow.chat/media/ai-conversation-insights/topics-phone.svg)

Review recurring classified subjects such as delivery, service, billing, availability and quality. The daily breakdown keeps customer messages, negative customer messages and staff messages distinct.

**Before acting:** a topic is not a proven root cause. One day is not a trend. Compare equivalent groups, time windows, message coverage and participant-role configuration; a changed selection can explain a changed result.

## Team conversations: use the source examples for coaching

![Simplified staff tone and observed-recovery report with fictional values](https://bow.chat/media/ai-conversation-insights/team-phone.svg)

Review helpful, dismissive, neutral and unknown staff communication tone, together with observed replies and recoveries. The fictional delivery follow-up contains two observed replies and one observed recovery.

A recovery means a qualifying customer-positive or resolution sequence was observed after a negative message within the selected window. It does not prove that every underlying issue is resolved. Silence and a staff claim alone are not customer confirmation.

**Before acting:** tone observations are coaching cues, not employee ratings, a measure of effort or a basis for automated disciplinary action. Read the source context and consider the work the teammate was handling.

## A practical manager review

Start with the briefing, choose one concern, read its source conversation and confirm the present situation. Decide who needs to follow up through your normal process. Use topics to select a recurring issue to investigate and staff examples to discuss a specific exchange. Revisit comparable periods rather than treating one example as a business-wide conclusion.

AI insights complements, rather than replaces, [Groups Monitoring](https://docs.bow.chat/groups-monitoring), [activity reports](https://docs.bow.chat/groups-monitoring/activity-reports) and response/resolution SLA reporting.

Canonical: https://docs.bow.chat/groups-monitoring/ai-insights
Version: 1
Updated: 2026-11-07T09:30:29.267Z