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IBM Financial Services Italy

Unipol Assicurazioni S.p.A.

IT Operations Automation

Unipol needed a secure, scalable IT-operations foundation that could automate monitoring and incident handling, support hybrid-cloud flexibility, and serve as the platform for enterprise-wide AI adoption.

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100% monitoring coverage achieved, up from 26% before the solution
538 escalated events
90 seconds average event response time, down from 20 minutes
incident handling time has dropped by 90%

Reported impact

Business outcome

Unipol cut event-response and incident-handling times, achieved full monitoring coverage, improved accounting and claims-processing efficiency, and created the AI-enabled operational base it plans to expand across the business.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Unipol and IBM created NAMI, an AI-powered automation platform built on the watsonx ecosystem to modernize IT operations. IBM watsonx.ai provides the generative-AI layer and natural-language interfaces, watsonx Orchestrate supports agentic workflows, IBM Cloud Pak for Data prepares operational data, and IBM Cloud Pak for AIOps provides unified event and application visibility. The platform first ran on IBM Cloud and was later moved to a hybrid-cloud model on Red Hat OpenShift, while IBM Fusion HCI supplied the on-premises infrastructure needed to satisfy governance standards. NAMI also uses a multi-model strategy with Llama, Pixtral AI, and IBM Granite so different tasks can be matched to the right model. Together, those components integrate more than a dozen monitoring systems into a shared operational data layer where events can be analyzed, resolved, escalated, and reported with much less manual effort than before.

Data flow

Events from more than a dozen monitoring systems flow into NAMI's shared data layer through Cloud Pak for Data and Cloud Pak for AIOps, where watsonx.ai and the multi-model stack analyze them, watsonx Orchestrate coordinates actions, and the platform resolves or escalates incidents through a unified operational workflow.

Solution architecture

10 components · 4 layers
  1. Compute
    • IBM watsonx.ai Provides the natural-language and generative-AI capabilities used inside NAMI's operational workflows.
    • Llama Supplies one of the models in NAMI's multi-model AI strategy.
    • Pixtral AI Provides another task-specific model in the platform's multi-model approach.
    • IBM Granite Adds IBM foundation-model capabilities to the platform's AI workflow mix.
  2. Storage
    • IBM Cloud Pak for Data Prepares and organizes the operational data used by the AI-powered platform.
    • IBM Fusion HCI Provides the on-premises infrastructure required for Unipol's governance standards.
  3. Serving
    • IBM Cloud Pak for AIOps Provides unified operations management and visibility across applications and systems.
  4. Orchestration
    • IBM watsonx Orchestrate Coordinates agentic workflows and natural-language interactions for operations teams.
    • IBM Cloud Hosted the original deployment of the NAMI platform before its hybrid-cloud migration.
    • Red Hat OpenShift Enables the hybrid-cloud deployment model and scalability for the operations platform.

Architecture clues

  • A multi-model approach is used to match different tasks to different models.
  • The platform was migrated from IBM Cloud to a hybrid-cloud model on OpenShift.
  • The roadmap includes full event automation and eventual support for IBM z17 technology.

Evidence from the source

Event response times dropped from 20 minutes to just 90 seconds
NAMI has taken over 100%
Supporting these capabilities were the IBM watsonx Orchestrate solution for agentic AI workflows
To modernize IT operations and facilitate enterprise-wide AI adoption, Unipol partnered with IBM to develop next automation monitoring insurance (NAMI), an AI-powered automation platform built on the IBM watsonx ecosystem.
the solution was migrated to a hybrid cloud using the Red Hat OpenShift Platform
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    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

    Unipol Assicurazioni S.p.A. customer story

    First-party
    https://www.ibm.com/case-studies/unipol
    Source type
    Vendor-published customer story
    Published
    Nov 10, 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

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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