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Automated web request management process


Added by:   OECD analyst
Added on:   06 Oct 2026
Updated by:   OECD analyst
Updated on:   06 Oct 2026

Citizens send questions to INPS through a web form on different topics. In the past, all messages were checked by contact centre operators at the first level and manually forwarded where necessary to back-office officials at the second level. As a result, most requests were processed twice and answers arrived later. The new system uses AI to automatically send each request to the correct level. This helps citizens receive faster replies and reduces unnecessary work for contact centre staff.

Name in original language

Processo di gestione automatizzata delle richieste web

Initiative overview

The initiative addresses the inefficiency of the previous manual process used to route citizens’ requests submitted through the INPS Risponde web channel. In the past, all messages were reviewed by contact centre operators and forwarded to back-office staff where necessary, meaning that most requests were handled twice and resolved with delays.

The initiative leverages the opportunity offered by artificial intelligence to automate this routing process and improve service delivery. The main objective is to reduce processing steps and response times by automatically directing each request to the appropriate operational level, either the Contact Centre or back-office units. Additional objectives include improving the accuracy of request classification, reducing unnecessary work for operators, and ensuring compliance with data-protection rules through an internally managed system.

The initiative is expected to evolve through its integration into ordinary operational processes and its progressive extension to additional request categories. Over time, the initiative has evolved from an experimental automation tool into a structured system based on supervised artificial intelligence models. The main technology used is a BERT Transformer.

The models are retrained weekly through a defined workflow that includes data collection, preprocessing, training, testing, benchmarking against the production model, and feedback generation. At the end of each training cycle, a report is produced highlighting any components that require manual updates.

The system is trained exclusively on internal INPS data, organised by thematic area, and operates in compliance with GDPR requirements. The entire solution is built on open-source technologies and hosted on the organisation’s own infrastructure, ensuring full control over both data and operational processes.

Results, outcomes and impacts

The initiative has led to faster and more accurate handling of citizens’ requests and a reduction in unnecessary work for first-level staff. The system automatically analyses all incoming messages and routes them to the appropriate level, so first-level operators handle only requests appropriate to their level.

Results have been measured through system logs and operational statistics. Since 22 April 2024, more than two million requests have been processed by AI, and approximately 30% have been automatically routed directly to second-level staff.

In the future, further improvements are expected in response times, automation rates and resource efficiency as the system is extended to additional request types and services.

Other relevant details

Challenges and lessons learned: The main challenges encountered were the need for continuous system monitoring and regular updating of the AI models to prevent performance from declining over time. Another risk was potential reliance on external AI providers, which could conflict with the protection of citizens’ personal data. These challenges were addressed by adopting internal control mechanisms for model quality and avoiding solutions that require sensitive data to be sent to external platforms. The initiative instead uses open-source technologies developed and maintained on-premises at INPS. An important lesson is that AI-based services cannot be managed like traditional software. They require specific skills, ongoing supervision and dedicated processes. For initiatives of this type to succeed, it is essential to build internal expertise, ensure strong data-protection measures, and establish long-term governance for system maintenance and improvement.

About the policy initiative


Category:

  • AI policy initiatives, programmes and projects

Initiative type:

  • AI use cases/projects in the public sector

Status:

  • Active

Start Year:

  • 2023

Target Sectors:


OECD AI Principles:

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Other relevant urls:

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