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ServiceNow Retail Australia

Officeworks

Document Processing · IT Operations Automation

Officeworks wanted to automate finance, merchandise, and other back-office processes without adding disconnected RPA tooling or relying on staff to monitor work only during office hours.

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3,500 Hours/year unlocked across seven business processes
3x Faster response to customer queries on merchandise
64% Straight-through processing of finance exception cases

Reported impact

Business outcome

Officeworks unlocked 3,500 hours of productivity a year, achieved 64% straight-through processing for finance exceptions, and answered merchandise queries three times faster.

  • Cost Reduction
  • Productivity Improvement

Solution

Officeworks wanted to automate finance, merchandise, and other back-office processes without adding disconnected RPA tooling or relying on staff to monitor work only during office hours. Officeworks implemented a ServiceNow-centered stack built around ServiceNow AI Platform, Workflow Data Fabric, RPA Hub, and Document Intelligence. Key components included ServiceNow AI Platform provides the common workflow and AI layer; Workflow Data Fabric connects operational data and automation context; RPA Hub executes repetitive bot tasks; Document Intelligence extracts structured data from documents and emails. Work enters as cases, requests, or automations, is classified or summarized by RPA Hub and Document Intelligence before ServiceNow AI Platform and Workflow Data Fabric routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.

Data flow

Work enters as cases, requests, or automations, is classified or summarized by RPA Hub and Document Intelligence before ServiceNow AI Platform and Workflow Data Fabric routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.

Solution architecture

4 components · 3 layers
  1. Ingest
    • Document Intelligence Extracts structured data from documents and emails for downstream workflows.
  2. Compute
    • RPA Hub Runs robotic process automations for repetitive back-office tasks.
  3. Orchestration
    • ServiceNow AI Platform Acts as the common workflow and AI platform that unifies service data, automation, and user experiences.
    • Workflow Data Fabric Connects operational data across systems and powers automation with shared context.

Architecture clues

  • Workflow Data Fabric is used as the shared context layer across multiple systems and workflows.

Evidence from the source

With business growth and a strong culture of innovation, Officeworks wanted to leverage even more of the automation and integration capabilities in the ServiceNow AI Platform to further improve operational efficiency and increase employee productivity
“Deploying Workflow Data Fabric on the ServiceNow AI Platform has saved 780 hours—or 20 weeks per year—on the remittance allocation process alone,” Steven adds
“Leveraging RPA Hub and Document Intelligence capabilities within Workflow Data Fabric, we have unlocked almost 3,500 productivity hours per year across seven business processes,” Steven adds
“Now, using ServiceNow Workflow Data Fabric to automate report generations, we’ve cut 240 hours—or six weeks per year—on data entry
“With that, we now have bots running 24/7.” ServiceNow Workflow Data Fabric enables us to unlock 3,500 hours a year
Transparency Evidence & methodology 95/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. ServiceNow

    Officeworks customer story

    First-party
    https://www.servicenow.com/customers/officeworks.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 28, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 30, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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