/ Document extraction · PUBLISHED CASE

SPM Accounting Solutions

Scanned invoices converted into structured data for review

A virtual CFO firm used AI-assisted extraction to convert scanned invoices into structured data, with employees validating the output.

Virtual CFO and accounting servicesProfessional services teamSource pages 44–45

MCCIA-PUBLISHED CASEANALYSED BY DOTSUPERNOT PRESENTED AS A DOTSUPER CLIENT

/ THE SHORT VERSION

A virtual CFO firm used AI-assisted extraction to convert scanned invoices into structured data, with employees validating the output.

01 / BUSINESS CONTEXT

The work before the tool.

SPM Accounting processes recurring financial documents for clients. Invoice extraction and content preparation consumed specialist time that could otherwise support review and advisory work.

THE OPERATING CONSTRAINTEmployees manually read scanned invoices, transferred fields and checked each batch. The repetitive work was slow and vulnerable to transcription errors.

02 / THE INTERVENTION

What changed in the workflow.

The team used Microsoft Copilot and related tools to extract invoice information into structured records and accelerate first drafts for business content.

DEPLOYED VS. PLANNED

The extraction workflow was in business use. The report does not describe accuracy thresholds, exception rates or integration depth.

03 / REPORTED OUTCOMES

The numbers, with their labels attached.

3–4 hr → 10 min100-invoice batchReported by the organisation in the MCCIA publication. Not independently audited.

2 hr → 10–15 mincontent preparationReported by the organisation in the MCCIA publication. Not independently audited.

Before and after, based on the published case narrative
Workflow area	Before	After
Invoice intake	Fields copied manually from scanned documents	AI extracts a structured first pass
Quality control	Review followed manual entry	Review focuses on exceptions and extracted values
Specialist time	Routine transcription occupied finance staff	More time can move toward analysis and client work

04 / HUMAN CONTROL

Capability matters when people stay in control.

Employees checked extracted values before they entered the accounting workflow. Financial interpretation and client advice remained with qualified people.

05 / WHAT A SIMILAR BUSINESS SHOULD TEST

Transfer the pattern, not the conclusion.

01
Document automation needs an explicit exception and review path.
02
Accuracy should be measured by field and document type, not by a single average.
03
The most useful first win may be better review allocation rather than full automation.

06 / SOURCE AND LIMITS

What this page can and cannot establish.

This analysis summarises outcomes published in MCCIA’s AI: Compilation of case studies (2026), printed pages 44–45. The work was conducted through the MCCIA Applied AI Studio. The report credits the Studio team managed by Neeraj Thakur, with Gauri Kale and Ismail Patel. The featured organisation is not presented as a dotSuper client.

01The time reduction is self-reported and the report does not publish an extraction error rate.
02Financial documents can contain sensitive data and require appropriate access, retention and review controls.

Read the original MCCIA publication

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