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DXC Technology

Data Lake / Data Warehouse Modernization · Real-time Analytics · Security and Compliance

However, years of growth through mergers left the company with fragmented systems, siloed data and low trust in AI.

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3 AI agents in production, 8 more in pilot or development phases
30% Reduction in platform TCO after Databricks migration (validated by finance team, Q2 2025)
As a result, DXC cut time-to-insight from months to days, reduced platform total cost of ownership (TCO) by 30% and empowered teams across the business to build and deploy AI solutions faster and with greater confidence.
DXC Technology, a global leader in IT services, set out to boost productivity and streamline operations by developing 3 generative AI agents across sales, HR, finance and project management.

Reported impact

Business outcome

DXC Technology achieved measurable business impact on Databricks, including 30% Reduction in platform TCO after Databricks migration (validated by finance team, Q2 2025); 3 AI agents in production, 8 more in pilot or development phases; DXC Technology, a global leader in IT services, set out to boost productivity and streamline operations by developing 3 generative AI agents across sales, HR, finance and project management..

  • Compliance
  • Cost Reduction
  • Productivity Improvement

Solution

DXC Technology, a global leader in IT services, set out to boost productivity and streamline operations by developing 3 generative AI agents across sales, HR, finance and project management. However, years of growth through mergers left the company with fragmented systems, siloed data and low trust in AI. DXC turned to the Databricks Data + AI Platform to unify data, enable governed access and accelerate AI development with a single, scalable environment. As a result, DXC cut time-to-insight from months to days, reduced platform total cost of ownership (TCO) by 30% and empowered teams across the business to build and deploy AI solutions faster and with greater confidence.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed into curated datasets

Solution architecture

1 components · 1 layer
  1. Compute
    • Agent Bricks Supports agentic or GenAI applications that DXC Technology uses to automate or accelerate work.

Architecture clues

  • Cloud: Azure
  • Key product: Agent Bricks
  • Real-time or streaming data processing

Evidence from the source

3 AI agents in production, 8 more in pilot or development phases
30% Reduction in platform TCO after Databricks migration (validated by finance team, Q2 2025)
As a result, DXC cut time-to-insight from months to days, reduced platform total cost of ownership (TCO) by 30% and empowered teams across the business to build and deploy AI solutions faster and with greater confidence.
DXC Technology, a global leader in IT services, set out to boost productivity and streamline operations by developing 3 generative AI agents across sales, HR, finance and project management.
The company set an ambitious goal: to develop 3 AI agents designed to drive operational efficiency and workforce productivity across sales, HR, finance and IT, with more planned.
Transparency Evidence & methodology 90/100 reference value · 92% confidence · 2 sources

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 value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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
    5 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency9/10
    2 source references 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. Databricks

    DXC Technology customer story

    First-party
    https://www.databricks.com/customers/dxc-technology
    Source type
    Vendor-published customer story
    Published
    Nov 11, 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
  2. Built In

    DXC Technology Careers, Perks + Culture | Built In

    Independent secondary
    https://builtin.com/company/dxc-technology

    The Organization JSON-LD lists addressLocality 'Ashburn', addressCountry 'US', and numberOfEmployees value 86261.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • Published on source page Nov 11, 2025 · Databricks · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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