AI Dashboard

This page explains the AI tab on the Analytics Dashboard, especially AI Resolution Rate, how it should be calculated, and how it differs from Chat Dashboard Resolution Rate.

Where to find it

Open Analytics Dashboard.
Switch to the AI tab.
Pick a date range.
Export charts with ⋮, then Export chart (PNG or CSV) when needed.

The AI tab typically covers:

Metric

What it answers

AI Resolution Rate

What share of AI-handled conversations did the AI close on its own?

AI-assisted rate

How often did AI help (suggest or reply) even if a human finished the case?

Response speed

How fast did AI first respond?

Deflection savings

Rough value of conversations handled without a human

Revenue impact

Optional commercial impact views when configured

Screen tour

KPI overview

At the top of the AI tab you typically see:

KPI cards: AI Cases Resolved, AI Resolution Rate, AI-Assisted Rate, Human-Only Resolution Rate, and Median AI Response.
AI vs Human donut (share of cases resolved by AI vs humans).
AI Cases by Channel (for example Chat vs other channels).
Daily AI vs Human stacked bars for case volume over the selected range.

Use these cards to sanity-check volume and mix before reading the trend charts below.

AI Resolution Rate trend

This chart usually combines:

A line for AI Resolution Rate (often a 7-day rolling average).
Volume bars under the line so you can see how many AI-related cases each day contributed to that day's percentage.

A sharp drop with stable product usage often points to a definition or filter issue (workflows, disposition mapping) rather than a real drop in AI close success. Check recent workflow and disposition changes first.

Impact charts

Lower on the page you typically find:

Case Volume AI vs Human (daily or period comparison).
AI Impact on Service Level (how AI-handled volume relates to SLA or service-level views).
AI Resolution by Hour (when in the day AI closes cases).
AI Resolution by Day of Week (weekday patterns).

These views help spot staffing, channel, and time-of-day patterns alongside the headline resolution rate.

AI Resolution Rate

What it is

AI Resolution Rate is the percentage of conversations that were resolved by the AI agent (Kai / Chat AI Agent), without needing a human agent to take over and close the case.

Many workspaces show this as a 7-day rolling line (daily rate smoothed over the last 7 days), with daily case volume as bars.

How it should be calculated

Intended definition (product intent):

AI Resolution Rate = (Cases closed by AI / AI-handled cases in the period) × 100%

Closed by AI means the case was auto-closed by the AI lifecycle, for example AI Agent Session Timeout (or equivalent AI auto-close / AI resolved status). It is not whether a human wrap-up form later shows a custom disposition.

AI-handled cases means conversations that entered the AI agent path in that period (bot was active on the room).

Important: dispositions and workflows

AI Resolution Rate must ignore wrap-up disposition values set by workflows.

If you add a workflow that changes disposition to a custom disposition when the AI times out or auto-closes a room, the AI Resolution Rate chart should not drop. Those rooms are still AI-resolved.

If your rate falls right after enabling such a workflow, the metric is likely filtering on disposition incorrectly. Contact support or your CSM and ask engineering to recalculate using AI close status (for example AI Agent Session Timeout) rather than disposition.

What it is not

Not the same as Chat Dashboard → Resolution Rate (that page describes human or agent chat resolution).
Not CSAT, sentiment, or SLA Meet rate.
Not "every closed case with any disposition named Resolved."

Reading the trend chart

Line: rolling AI Resolution % (often 7-day).
Bars: daily case volume for the AI-related population. Taller bars mean that day's % is based on more cases.
A sudden drop with stable product usage usually means a definition or filter bug (workflows, disposition mapping) or a real drop in AI close success. Check recent workflow and disposition changes first.

Historical data after a fix

When engineering corrects the metric:

Ask whether the fix is forward-only or includes a backfill / recalculation from the date the bad filter started.
Until a backfill runs, numbers in the broken window may remain wrong even after the code fix.
Pre-change periods (before the disposition workflow) are usually still trustworthy.

Page

Use when

Chat Dashboard

Human or agent chat volumes, AFRT, ART, member status

Call Dashboard

Voice queue and abandon metrics

Ticket Dashboard

Ticket volumes and statuses

CX Log

Case-level history, dispositions, CSAT, sentiment

Troubleshooting

Symptom

Likely cause

What to do

AI Resolution Rate drops after a new workflow

Workflow sets custom disposition on AI auto-close; metric wrongly requires a specific disposition

Confirm with eng; metric should key off AI close status

Rate looks fine but CX Log shows many AI closes

Dashboard filter does not match CX Log filters (channel, queue, date TZ)

Align date range, timezone, and queue filters

Rate jumps on low-volume days

Small denominator (few AI cases)

Prefer 7-day rolling; check volume bars

Need help interpreting a specific tenant chart? Reach out to support@klink.cloud or your CSM.

Last updated 1 month ago