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ServiceNow Commercial and Professional Services Europe

Kainos

Customer Service Automation · Enterprise Knowledge Search

Kainos needed to serve EMEA clients faster and more economically while reducing the time support teams spent searching knowledge and drafting responses.

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600 Knowledge articles created with Now Assist
71% Reduction in average time to resolve
99% Customer satisfaction, up from 80%

Reported impact

Business outcome

Kainos raised customer satisfaction from 80% to 99%, reduced average resolution time by 71%, and created about 600 knowledge articles with Now Assist.

  • Customer Experience
  • Productivity Improvement

Solution

Kainos needed to serve EMEA clients faster and more economically while reducing the time support teams spent searching knowledge and drafting responses. Kainos implemented a ServiceNow-centered stack built around Now Assist, Customer Service Management Pro, and Knowledge Base. Key components included Now Assist adds generative search, summarization, and drafting; Customer Service Management Pro adds advanced automation for customer support; Knowledge Base stores reusable support content. Work enters as cases, requests, or automations, is classified or summarized by Now Assist, and uses context from Knowledge Base before Customer Service Management Pro routes or fulfills the work. Updates then flow back to agents, account teams, or customers through the same connected experience. Running customer support and case management on one data model reduced manual handoffs and made expansion easier.

Data flow

Work enters as cases, requests, or automations, is classified or summarized by Now Assist, and uses context from Knowledge Base before Customer Service Management Pro routes or fulfills the work. Updates then flow back to agents, account teams, or customers through the same connected experience.

Solution architecture

3 components · 3 layers
  1. Compute
    • Now Assist Adds generative-AI capabilities such as summarization, drafting, search, and guided responses inside workflows.
  2. Storage
    • Knowledge Base Stores reusable knowledge content that search and assistants reference during support.
  3. Orchestration
    • Customer Service Management Pro Adds advanced customer-service workflows, automation, and agent tooling for support operations.

Architecture clues

  • Knowledge content is reused as a source for self-service or AI-assisted answers.

Evidence from the source

Now Assist spreads knowledge and Using Now Assist’s GenAI capabilities and natural language tools, Kainos has quickly and significantly improved both its customer-facing service experience as well as its internal operations
“Compared to writing to colleagues on a chat, using GenAI to search the employee portal is so much faster, and we’re only at the very start of this,” Peiter says
“I see it evolving significantly over the next year.” Peiter also sees important value in Now Assist’s case summarization feature, which gives an overview of account activity to agents who aren’t familiar with a particular case or customer
“So, within seconds we have information that tells us how to best deploy staff and where we need to cross-skill.” These insights, combined with the core capabilities of CSM Pro, have resulted in a significant improvement in customer satisfaction for Kainos
“There’s definitely a common market challenge for Kainos: everyone wants to do it cheaper and faster,” explains Peiter la Cour Freiesleben, Director of Application Management and Strategic Growth—EMEA, at Kainos
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
    2 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

    Kainos customer story

    First-party
    https://www.servicenow.com/customers/kainos-now-assist.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 Nov 19, 2024 · 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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