IBM Public Sector Finland

City of Helsinki

Customer Service Automation

The City of Helsinki needed to improve citizen service speed and flexibility, reduce pressure on overworked customer-service teams, and connect siloed service organizations without compromising privacy for sensitive public data.

10 virtual assistants currently running
6 healthcare and social services combined in the multi-chatbot
650,000 citizens
less than three months from start date to first launch

Reported impact

Business outcome

Helsinki created a scalable digital-assistant foundation that handles significant daily citizen demand, shortens queues, supports employees, and makes it easier to add new multilingual service assistants over time.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

The City of Helsinki worked with IBM Consulting to build a network of virtual assistants on IBM watsonx Assistant so citizens could reach city services faster and employees could absorb inquiry volumes. IBM Consulting and the city co-designed chatbot scope, tone, and user experience, then used IBM Garage workshops to build intent models from customer-service expertise when chat logs were unavailable. As the program expanded, assistants were connected to internal departments, other Finnish municipalities, and outside vendors, while IBM Watson Discovery was added to the internal IT assistant so unanswered questions could search a large instruction library. A multi-chatbot serving healthcare and social services also uses IBM Watson Language Translator to deliver Finnish, Swedish, and English experiences. The architecture lets Helsinki reuse training data, run sensitive workloads from a Finland data center when needed, and add new assistants without rebuilding from scratch.

Data flow

Citizen or employee questions enter watsonx Assistant over web chat, APIs connect the assistant to city departments and outside systems, Watson Discovery searches instruction content when needed, and Language Translator expands multi-chatbot responses across Finnish, Swedish, and English experiences.

Solution architecture

6 components · 2 layers
  1. Serving
    • IBM watsonx Assistant Runs the city's citizen-facing and employee-facing virtual assistants across multiple service domains.
    • IBM Cloud Hosted the initial virtual-assistant deployment for the city.
    • IBM Watson Discovery Searches a large instruction library when the internal IT assistant cannot answer directly.
    • IBM Watson Language Translator Translates skills training services for the multi-chatbot into Swedish and English.
  2. Orchestration
    • IBM Consulting Co-designed the virtual-assistant program, user experience, and implementation approach with city teams.
    • IBM Garage Provided the workshop methodology used to co-create chatbot training and implementation patterns.

Architecture clues

  • Chatbot training data was released on Helsinki Region Infoshare so other cities could reuse it.
  • The city wanted the solution to run from a local Finland data center when needed for sensitive workloads.
  • The team continues to meet weekly with a local IBM team to plan new assistants and capabilities.

Evidence from the source

"Chief among the platform requirements were natural language processing and the ability to connect to other systems ... using APIs."
"Currently, the City of Helsinki is running 10 virtual assistants"
"The team began work on the digital assistant in December 2020 and launched it in early March of 2021"
"The team then built an internal IT virtual assistant for employees that incorporated IBM Watson Discovery."
"Virtual assistants currently handle up to 300 customer contacts per day"
Transparency Evidence & methodology 86/100 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 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 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

    City of Helsinki customer story

    First-party
    https://www.ibm.com/case-studies/city-of-helsinki
    Source type
    Vendor-published customer story
    Published
    May 14, 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.