IBM Software / SaaS global

IBM CIO Organization

Customer Service Automation · IT Operations Automation

IBM needed to reduce the wait time and manual workload created by roughly 785,000 annual internal IT support tickets without forcing employees or help-desk staff to spend time on repetitive issues such as password resets, device setup, or VPN troubleshooting.

75% of tickets successfully handled by AskIT
785,000 IT support tickets each year
80% of IBM’s top IT issues can be addressed with AskIT
available to more than 280,000 IBM employees globally

Reported impact

Business outcome

Within four months, AskIT had already been used by more than 133,000 IBM employees, resolved more than 75% of submitted queries itself, reduced waits for chat and phone help, and let human advisors focus on higher-touch and more complex issues.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

IBM CIO built AskIT as a conversation-based self-service support layer for employees using IBM watsonx Assistant, then trained it with knowledge drawn from more than 300,000 historical support tickets, internal support articles, and recurring issue patterns. The assistant handles natural-language requests for common IT tasks, retrieves the best-fit resolutions, and drives direct ticket creation when a self-service answer is not enough. IBM deployed the service globally across more than 40 languages for more than 280,000 employees, with always-on availability so users can resolve issues without waiting for phone or chat queues. The team is now extending the same flow with IBM watsonx.ai so large language models can fill intent gaps when watsonx Assistant is uncertain, improving coverage while keeping the core support workflow inside IBM’s internal IT operations.

Data flow

Employees submit natural-language IT questions to AskIT, watsonx Assistant matches those requests against patterns learned from ticket history and internal support content, and the system returns self-service guidance or creates the next support action; watsonx.ai is being added to answer gaps when intent confidence is low.

Solution architecture

2 components · 2 layers
  1. Compute
    • IBM watsonx.ai Adds large language model support to cover questions that fall outside the assistant’s confident intent matches.
  2. Serving
    • IBM watsonx Assistant Runs the AskIT virtual agent that interprets employee questions, retrieves answers, and automates common IT support tasks.

Architecture clues

  • AskIT was designed from analysis of more than 300,000 support tickets.
  • Language auto-detection was added after launch to support multilingual interactions.
  • The assistant operates globally with 24x7 availability.

Evidence from the source

“AskIT is trained on 80% of the IT issues the company faces most frequently.”
“Based on the knowledge gained from analyzing over 300,000 support tickets, AskIT uses the AI and natural language processing (NLP) capabilities of watsonx Assistant.”
“It covers more than 200 common support topics in more than 40 languages and is available to more than 280,000 IBM employees globally.”
“Of the queries that were submitted, over 75% were resolved by the new assistant itself.”
“Presently, the team receives around 785,000 IT support tickets each year.”
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
    2 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 CIO Organization customer story

    First-party
    https://www.ibm.com/case-studies/cio-watsonx-askit
    Source type
    Vendor-published customer story
    Published
    Jul 15, 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.