> ## Documentation Index
> Fetch the complete documentation index at: https://irisdocs.prescientlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Worked Example — Accounting Firm Client Onboarding

> Illustrative end-to-end diagnostic for a 25–100 employee accounting firm with a manual client-onboarding workflow.

<Note>This is an illustrative training example, not a named client engagement.</Note>

## Situation

A 60-person accounting firm is growing quickly. Partners complain that new clients take too long to become fully operational and staff repeatedly chase missing information. The initial buyer hypothesis is: “We need an AI onboarding assistant.”

## Diagnostic scope

**Business question:** where is avoidable effort and delay created from signed engagement letter to client-ready status?

## Stakeholders

Operations director, onboarding manager, two onboarding administrators, compliance representative, client-facing manager and IT/system owner.

## Evidence

Iris interviews capture the intended workflow, recurring exceptions and workarounds. Consent-based screen observation with an onboarding administrator reveals repeated copying between systems, manual checks and multiple client-chasing steps.

## Findings

1. Client information arrives through inconsistent channels.
2. Staff manually re-key the same details into multiple systems.
3. Compliance checks often begin before required information is complete.
4. Missing information creates repeated client chasing.
5. Senior approval is used for routine exceptions that could be governed by clearer rules.

The initial AI-assistant hypothesis is only partly correct. Much of the value sits in process standardisation and integration.

## Opportunity backlog

* controlled client intake;
* deterministic transfer between systems;
* AI-assisted missing-information follow-up with human review;
* clearer exception-approval rules.

## Prioritisation

The consultant recommends fixing intake and duplicate entry first. The AI follow-up use case becomes smaller once input quality improves.

## Business case

The consultant builds the ROI model outside Iris using client-agreed assumptions for annual case volume, manual effort, follow-up frequency and loaded operations cost.

## Report Composer

The validated process, findings, opportunity sequence and consultant-side business case are assembled into the final report using Iris Report Composer.

## Executive recommendation

> Standardise intake and remove duplicate system entry first. These changes address the dominant evidence-backed causes of delay and create a stronger foundation for later AI-assisted communication.