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IBM Software / SaaS global

IBM Chief Information Officer organization

IBM's CIO organization needed to run new AI applications alongside thousands of existing workloads, but its earlier vector-database and model setup relied on costly provisioned GPUs that created resource contention and inefficiency.

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nearly 2,000 existing internal workloads

Reported impact

Business outcome

IBM's CIO organization reduced the operational burden of managing AI infrastructure, improved scaling behavior for internal AI services and created a more flexible platform for enterprise AI workloads across cloud and on-premises environments.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

IBM's CIO organization moved its AI workload management onto IBM Cloud with IBM watsonx.data, IBM watsonx.ai and Red Hat OpenShift to improve scale, flexibility and cost efficiency. The target environment separates concerns more cleanly than the prior arrangement: watsonx.data manages domain-specific data, watsonx.ai supports development, training and inferencing, and the elastic IBM Cloud environment absorbs GPU spikes instead of forcing operations teams to overprovision hardware. OpenShift provides workload-placement flexibility so the same environment can also support on-premises requirements, while watsonx.governance can be layered on in scenarios that need stronger lifecycle oversight and indemnification support. This turns AI hosting from a set of manually managed high-cost resources into a managed platform service for internal applications such as AskIBM, allowing IBM to scale AI services across the enterprise with more predictable operations and better alignment to hybrid-cloud constraints.

Data flow

Domain-specific data is managed in watsonx.data, model development and inferencing run through watsonx.ai on IBM Cloud, elastic GPU capacity serves peak demand, and applications such as AskIBM consume the resulting AI services through the hybrid-cloud platform.

Solution architecture

5 components · 4 layers
  1. Compute
    • IBM watsonx.ai Supports development, training and inferencing for AI applications running on the CIO platform.
  2. Storage
    • IBM watsonx.data Manages the domain-specific data used by internal AI applications and model workflows.
  3. Orchestration
    • IBM Cloud Provides the elastic runtime environment that absorbs GPU spikes and hosts the CIO organization's AI workloads.
    • Red Hat OpenShift Extends the environment across cloud and on-premises placements with a consistent operating model.
  4. Governance
    • IBM watsonx.governance Adds lifecycle governance and covered-model support where stricter AI oversight is required.

Architecture clues

  • OpenShift keeps the environment portable enough for on-premises placement when required.
  • The earlier environment used provisioned GPUs that sat idle during off-peak hours and drove cost inefficiency.
  • Watsonx as a Service on IBM Cloud now absorbs GPU spikes instead of requiring the operations team to manage those resources directly.

Evidence from the source

IBM watsonx.ai
IBM watsonx.data
The spikes in GPU usage are handled by the elasticity of watsonx as a Service on the IBM Cloud
hosting new artificial intelligence (AI) applications alongside nearly 2,000 existing internal workloads
Transparency Evidence & methodology 75/100 reference value · 87% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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
    4 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 specificity8/10
    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 Chief Information Officer organization customer story

    First-party
    https://www.ibm.com/case-studies/ibm-cio-cloud-ai
    Source type
    Vendor-published customer story
    Published
    Oct 28, 2024
    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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