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IBM Software / SaaS Global

IBM Enterprise Performance Management

Real-time Analytics

IBM needed to overcome fragmented financial and workforce planning data spread across hundreds of applications so analysts and executives could forecast, plan, and make decisions without relying on siloed tools and spreadsheet-heavy handoffs.

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97% benchmark achieved by the touchless forecasting process
>USD 200M in annual business value delivered through AI-driven automation since 2023
~140K data points predicted monthly
~40% gain in FP&A productivity since 2020

Reported impact

Business outcome

IBM simplified finance and workforce planning, improved forecast accuracy and analyst productivity, reduced tool sprawl, and generated substantial annual business value through AI-driven automation.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

IBM built its Enterprise Performance Management platform as a unified data and planning environment powered by IBM Cognos Analytics, IBM Planning Analytics, and IBM watsonx.ai. Cognos gives users a single experience for exploring performance insights, while Planning Analytics adds forecasting and became the core driver of forecast accuracy after being infused into production with AI capabilities in 2019. The platform links historically siloed sources through an integrated data model and supports a "touchless" forecasting process that predicts roughly 140,000 data points each month. Self-service dashboards and standardized workflows replaced more than 500 finance tools with fewer than 20, and the same Planning Analytics foundation later powered SCORE for workforce planning across IBM Consulting. The result is an enterprise planning stack where finance and workforce decisions draw from common historical data and AI-enhanced scenario planning instead of disconnected applications.

Data flow

Finance and workforce data from historically siloed systems is unified in EPM, Cognos exposes self-service performance views, Planning Analytics runs forecasting and scenario models, and watsonx.ai helps automate and enrich analytics across reporting and planning workflows.

Solution architecture

3 components · 2 layers
  1. Compute
    • IBM Planning Analytics Drives forecasting, workforce-planning models, and the touchless AI forecasting process.
    • IBM watsonx.ai Adds generative and AI-driven analytics to forecasting and planning workflows.
  2. Serving
    • IBM Cognos Analytics Provides the self-service interface where users discover and work with performance insights.

Architecture clues

  • Executives use real-time dashboards rather than static reports and spreadsheets.
  • Planning Analytics was later reused for IBM Consulting's SCORE workforce-planning solution.
  • The platform serves as the historical data foundation for touchless AI forecasting.

Evidence from the source

>USD 200M in annual business value delivered through AI-driven automation since 2023
EPM, powered by IBM Cognos Analytics, provides users with a single experience to discover and work with performance insights.
Planning Analytics became the driving force behind forecast accuracy.
This implementation builds upon IBM’s integrated data model that links data from historically siloed sources and includes solutions such as IBM watsonx.ai for generative AI-driven analytics.
~95% model accuracy using IBM watsonx and Planning Analytics
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
    4 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. IBM

    IBM Enterprise Performance Management customer story

    First-party
    https://www.ibm.com/case-studies/ibm-enterprise-performance-management
    Source type
    Vendor-published customer story
    Published
    May 27, 2025
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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