Accenture Commercial and Professional Services Global

Accenture

Accenture wanted leaders to make decisions from integrated enterprise data instead of siloed systems and needed an IT foundation that could add advanced technologies quickly and cost-effectively, but its legacy on-premises landscape made it harder to scale analytics, scenario planning and generative AI across the company.

30% of our outcomes driven by data, analytics or AI- and generative AI-led initiatives
35% productivity
95% in the cloud
over 250,000 of our people

Reported impact

Business outcome

Accenture gained a cloud-heavy enterprise platform that makes shared data, AI scenario planning and generative AI productivity tools easier to scale, helping teams make faster decisions, reduce maintenance effort and push advanced technologies into daily work.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Accenture rebuilt its internal IT estate into what it calls a digital core: a composable architecture that is now 95% in the cloud and designed to make data and AI reusable across the business. The transformation integrated operational data from SAP, Workday and Salesforce so leaders could use the 360-degree Value Navigator to see enterprise performance in one place and drill into issues such as bookings projections by market. On top of that shared data model, Accenture added cognitive capabilities and AI-driven scenario planning, then used the same foundation to deploy Copilot for Microsoft 365 and build an integrated generative AI hub that acts as a Center of Excellence for use-case prioritization, skills and delivery. Additional services such as the Identity Trust Engine and SAP finance analytics extend the core, turning cloud, data and AI into an operating platform rather than isolated pilots.

Data flow

Operational data from SAP, Workday and Salesforce is integrated into the cloud-based digital core, where leaders access a shared enterprise view through 360-degree Value Navigator and AI-assisted scenario planning. The same data foundation supports Copilot for Microsoft 365, the generative AI hub and security controls such as the Identity Trust Engine.

Solution architecture

7 components · 4 layers
  1. Ingest
    • SAP Supplies core enterprise data that feeds the shared digital-core foundation.
    • Workday Contributes workforce and operating data into the unified enterprise view.
    • Salesforce Provides customer and commercial data used within the integrated digital core.
  2. Serving
    • 360-degree Value Navigator Gives leaders a unified performance view with drill-down analytics and scenario-planning support.
    • Copilot for Microsoft 365 Applies generative AI to employee productivity workflows across the business.
  3. Orchestration
    • integrated generative AI hub Acts as the internal center of excellence for aligning, prioritizing and scaling generative AI use cases.
  4. Governance
    • Identity Trust Engine Measures identity risk to improve security as digital operations scale.

Architecture clues

  • 360-degree Value Navigator is the analytics layer leaders use for enterprise visibility and scenario planning.
  • An internal generative AI hub was created as a center of excellence for use-case alignment and skill building.
  • The digital core integrates SAP, Workday and Salesforce into one enterprise data foundation.
  • The environment includes identity-risk controls through the Identity Trust Engine.

Evidence from the source

"30% of our outcomes driven by data, analytics or AI- and generative AI-led initiatives"
"We deployed Copilot for Microsoft 365, and our people who are using it report saving up to three hours of time per day"
"we built an integrated generative AI hub, a Center of Excellence"
"we can integrate data from across our core systems including SAP, Workday and Salesforce"
"we have made all this possible by creating an interconnected foundation of cloud, data and AI-what we call the 'digital core.'"
Transparency Evidence & methodology 100/100 reference value · 89% 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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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 confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Accenture

    Accenture customer story

    First-party
    https://www.accenture.com/en/case-studies/about/transforming-it
    Source type
    Vendor-published customer story
    Published
    Aug 17, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Aug 17, 2026 · Accenture · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.