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ServiceNow Energy Global

Hitachi Energy

IT Operations Automation

Hitachi Energy had fragmented support tools and too much manual work across IT, HR, and finance, limiting its ability to scale AI-enabled employee services.

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25% Reduction in service desk requests in first week
26K Hours saved through automation and self-service
88% Of users rate the new solution as helpful
88% find the virtual agents helpful and easy to use when searching for knowledge

Reported impact

Business outcome

Hitachi Energy saved 26,000 hours, reduced service desk requests 25% in the first week, earned 88% helpfulness ratings, and expects US$3.2 million in IT and HR savings.

  • Cost Reduction
  • Productivity Improvement

Solution

Hitachi Energy had fragmented support tools and too much manual work across IT, HR, and finance, limiting its ability to scale AI-enabled employee services. Hitachi Energy implemented a ServiceNow-centered stack built around Now Assist, Virtual Agent, AI Search, and IT Service Management. Key components included Now Assist adds generative search, summarization, and drafting; Virtual Agent handles conversational self-service; AI Search returns contextual answers from enterprise content; IT Service Management standardizes incidents and requests. The stack also connected Microsoft Teams so the workflows could reuse existing records, documents, and collaboration channels instead of forcing teams back into manual handoffs. Requests enter through Virtual Agent and AI Search, are classified or summarized by Now Assist, and uses context from Microsoft Teams before IT Service Management and HR Service Delivery routes or fulfills the work.

Data flow

Requests enter through Virtual Agent and AI Search, are classified or summarized by Now Assist, and uses context from Microsoft Teams before IT Service Management and HR Service Delivery routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.

Solution architecture

5 components · 3 layers
  1. Compute
    • Now Assist Adds generative-AI capabilities such as summarization, drafting, search, and guided responses inside workflows.
  2. Serving
    • Virtual Agent Handles conversational self-service and ticket deflection for users.
    • AI Search Searches enterprise content and returns contextual answers to users.
  3. Orchestration
    • IT Service Management Standardizes incident, request, and fulfillment workflows on the platform.
    • HR Service Delivery Moves HR cases, onboarding, and employee support workflows onto the same platform.

Evidence from the source

Now Assist for Virtual Agent empowers end users to access answers directly, without being transferred to a Service Desk
Now Assist helps Hitachi Energy implement AI for IT, HR, and Finance 26K Hours saved through automation and self-service 25% Reduction in service desk requests in first week 88% Of users rate the new solution as helpful Replacing fragmented tools with an enterprise platform Hitachi Energy is on a mission to inspire the next era of sustainable energy, making electricity more accessible to all and empowering societies to thrive
“And Now Assist for Virtual Agent empowers end users to access answers directly, without being transferred to a live agent.” The financial impact is clear
“By automating manual tasks and surfacing real-time insights with Now Assist, we’re also freeing up agents to focus on higher-value work,” Oliver adds
“The aim is to further increase standardization and efficiency and reduce the need for post-incident audits.” It is also launching Now Assist for HR Service Delivery to simplify how employees access HR
Transparency Evidence & methodology 95/100 reference value · 79% 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 value95/100
  • 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
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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 provenance0/10
    The publication date has no documented provenance.
  • 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. ServiceNow

    Hitachi Energy customer story

    First-party
    https://www.servicenow.com/customers/hitachi-energy-ai.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Mar 27, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 30, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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