EY Manufacturing global

Xylem

Xylem saw significant enterprise AI potential, but disconnected experiments, unclear ownership and a lack of shared governance made it hard to move from isolated initiatives to trusted, repeatable AI deployment across the business.

secure internal AI environment built in under 10 weeks

Reported impact

Business outcome

Xylem created a trusted enterprise AI foundation that accelerated new deployments, made ownership clearer and helped employees apply AI more consistently across the business while reinforcing security, compliance and water-sector operational resilience.

  • Productivity Improvement
  • Risk Reduction
  • Scalability
  • Speed to Market

Solution

Xylem worked with EY Consulting professionals to turn enterprise AI from an experimentation program into an operating capability. Leadership first aligned on four priorities: a common AI vision, a disciplined delivery approach, a trusted technology foundation and enterprise-wide adoption. Execution centered on xylem.ai, a secure internal AI environment built in under 10 weeks by Xylem, EY and Microsoft teams. The platform brought enterprise data, AI capabilities and governance controls together in one trusted environment so new use cases could start from an approved foundation instead of being rebuilt as one-off pilots. Clear ownership was established for building, deploying and sustaining AI solutions, while role-relevant enablement helped employees apply AI in daily work. The result was a reusable platform and operating model that embedded intelligence into workflows while supporting security, compliance and responsible scale.

Data flow

Enterprise data, AI capabilities and governance controls were brought into xylem.ai as a shared internal environment. New use cases begin from that approved foundation, ownership is defined for build and sustainment, and employees apply the resulting AI capabilities in day-to-day workflows and business processes rather than in isolated pilots.

Solution architecture

4 components · 4 layers
  1. Compute
    • AI capabilities Deliver the intelligence that employees use to make decisions and execute work more effectively.
  2. Storage
    • enterprise data Supplies the business context and reusable information foundation that new AI use cases start from.
  3. Serving
    • xylem.ai Provides the secure internal environment where Xylem can launch and scale approved AI use cases.
  4. Governance
    • governance controls Apply security, compliance and responsible-use guardrails across the shared enterprise AI environment.

Architecture clues

  • Enterprise data, AI capabilities and governance controls were combined in a single trusted environment.
  • New AI use cases start from an approved foundation that supports security, compliance and responsible use.
  • The operating model explicitly defined ownership for building, deploying and sustaining AI solutions.
  • xylem.ai acts as a secure internal AI environment rather than a collection of disconnected pilots.

Evidence from the source

"At the center of that execution was xylem.ai, a secure internal AI environment built in under 10 weeks."
"By bringing enterprise data, AI capabilities and governance controls together in a single trusted environment, Xylem created a reusable foundation employees could trust and use."
"Clear ownership was established for building, deploying and sustaining AI solutions, helping ensure AI would not remain a series of isolated initiatives."
"New use cases can start from an approved foundation, helping accelerate deployment while supporting security, compliance and responsible use across the enterprise."
Transparency Evidence & methodology 86/100 reference value · 85% 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 value86/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    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. EY

    Case study: How Xylem scaled enterprise AI | EY - Global

    First-party
    https://www.ey.com/en_gl/insights/industrial-products/how-xylem-turned-ai-ambition-into-enterprise-execution
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Industry
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Oct 1, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source