Case studiesContact centres
11Contact centresLarge enterprise

Supporting agents through complex service conversations

Real-time knowledge, guided actions and automatic summaries help a national contact centre handle diverse enquiries consistently.

Illustrative scenarioAll results shown below are modelled, not verified client outcomes.
THE OPERATING CONTEXT

A realistic starting point.

Agents search across multiple knowledge sources while speaking with customers. New starters take months to become confident, and after-call notes vary in quality and completeness.

THE AIIT SOLUTION DESIGN

A digital employee built around the work.

Agent Assist listens with consent, retrieves approved knowledge, presents process guidance and drafts a concise interaction summary. The employee remains in control, confirms actions and edits all notes before they enter the customer record.

END-TO-END WORKFLOW

From first contact to a useful outcome.

  1. 01

    Detect intent and conversation stage in real time

  2. 02

    Retrieve access-appropriate knowledge

  3. 03

    Show concise guidance and required disclosures

  4. 04

    Surface the next approved action

  5. 05

    Draft structured notes and commitments

  6. 06

    Require agent review before record submission

MODELLED OUTCOMES

What a well-run scenario could change.

Planning estimates only. Not a promise, forecast or report of actual AIIT client performance.

−71%

Knowledge search time

Baseline2 min 25 sec
Modelled42 sec
−66%

After-call work

Baseline3 min 10 sec
Modelled1 min 5 sec
+17 points

Quality checklist completion

Baseline76%
Modelled93%
−29%

New-starter time to proficiency

Baseline14 weeks
Modelled10 weeks
ASSUMPTIONS

The conditions behind the model.

These boundaries make the scenario interpretable. Change the demand, workflow mix, integration quality or adoption rate and the potential outcomes change as well.

Agents review every recommendation and summary

Knowledge quality and ownership improve alongside the technology

The scenario models productivity, not workforce reduction

Conversation monitoring follows notice, consent and employment policies

A CONTROLLED PATH TO VALUE

How this scenario could be implemented.

01

Measure search and note-taking friction

02

Clean and rank authoritative knowledge

03

Pilot with experienced agents and compare guidance quality

04

Expand with coaching and knowledge-owner feedback loops

DESIGN LESSONS

What matters beyond automation.

Weak source content becomes visible quickly.

Experienced agents are essential design partners.

The best assistance is brief, timely and easy to ignore when it is not relevant.

About this scenario: AIIT created this fictional, anonymised operating example to support planning conversations. The figures are calculations based on the assumptions shown above; they do not represent an existing client, testimonial or guaranteed result.

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SAM JOBS

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ENQUIRE WITH AIIT

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