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IBM Commercial and Professional Services Global

IBM Consulting

Application Refactoring · IT Operations Automation

IBM Consulting needed to help developers create and modernize Ansible automation faster because skilled developer capacity is constrained and legacy automation content is time-consuming to build, refactor, and validate by hand.

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20%-45% initial build productivity improvements
Up to 45% improvement in initial build productivity for Ansible Playbooks

Reported impact

Business outcome

IBM Consulting improved developer productivity, broadened who can create Ansible automations, and accelerated modernization work without sacrificing traceability or content quality.

  • Productivity Improvement
  • Speed to Market

Solution

IBM Consulting used IBM watsonx Code Assistant for Red Hat Ansible Lightspeed to turn natural-language requests into higher-quality Ansible playbooks and modernization recommendations. The technical preview combined IBM and Red Hat work on a Granite foundation model trained and tuned on Ansible best practices, then surfaced suggestions with traceable source matches so developers could validate them before production use. IBM Consulting teams applied the assistant to new and legacy playbooks for installing SAP databases and applications on AWS, provisioning SQL and server components, and refactoring automations tied to user access management, security policy enforcement, and IBM WebSphere, MQ, and DB2 environments. Because the assistant produced recommended components rather than opaque code, it shortened the learning curve for newer automation developers while increasing the velocity and consistency of playbook creation. The implementation effectively added a generative-AI copilot to the IT-automation lifecycle without removing human review.

Data flow

Developers submit natural-language requirements, watsonx Code Assistant and the Granite model generate and explain candidate Ansible content, developers validate the recommended components, and the resulting playbooks are deployed through Ansible automation workflows.

Solution architecture

3 components · 2 layers
  1. Compute
    • IBM watsonx Code Assistant for Red Hat Ansible Lightspeed Generates playbook recommendations from natural-language inputs and helps modernize or create Ansible content faster.
    • Granite foundation model Provides the trained model that powers content recommendations and source-match transparency for Ansible tasks.
  2. Orchestration
    • Red Hat Ansible Automation Platform Serves as the automation environment whose playbooks are created, validated, and modernized by the assistant.

Architecture clues

  • IBM Consulting used the tool for both greenfield playbooks and legacy-playbook modernization.
  • The assistant shows the three closest matched sources of content for trust and transparency.
  • The solution was applied to SAP on AWS, SQL provisioning, and WebSphere/MQ/DB2 automation scenarios.

Evidence from the source

"During the technical preview ... we observed initial build productivity improvements in the range of 20%-45%."
"Each suggestion will include the three closest matched sources of content"
"Through AI-generated recommendations that are based on natural language inputs, developers can use watsonx Code Assistant for Red Hat Ansible Lightspeed to modernize and build Ansible Playbook content faster"
Transparency Evidence & methodology 100/100 reference value · 84% 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
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence84%
  • 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 evidence15/20
    3 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 Consulting customer story

    First-party
    https://www.ibm.com/case-studies/ibm-consulting-watsonx-code-assistant
    Source type
    Vendor-published customer story
    Published
    May 14, 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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