One-stop-shop for Artificial

Intelligence and Machine Learning 

for Official Statistics (AIML4OS)

AIML4OS Implementation

One-stop-shop for Artificial Intelligence and Machine Learning for Official Statistics Project (AIML4OS) is an ESSnet collaborative project involving 16 countries, started on 1 April 2024 and will last 4 years.

The overall aim is to develop a ‘One Stop Shop’ for AI and ML in official statistics; that is, a platform which provides a single entry point for ESS staff to access a coherent set of capabilities for implementing AI/ML based solutions. This will provide them with tailored guidance and assistance in deploying AI/ML solutions within adequate methodological and implementation frameworks.  The project is developed through 13 WPs, 6 supporting implementation and 7 dedicated to use cases. 

The knowledge and the framework realised and use cases experiences developed by the Consortium will allow them to use AI/ML to enable the development of more efficient and effective ways to deliver growing demand on official statistics.


AIML4OS expected outcomes

Expected outcomes from this project include:

  • the delivery of a framework for developing AI/ML solutions to be used in the context of official and European statistics
  • the provision of access for ESS staff and partners to established and proven AI/ML solutions/resources to be leveraged in the context of official statistics production
  • the encouragement of engagement from ESS organisations with AI/ML for innovation purposes and to facilitate their understanding and realisation of the benefits of AI/ML
  • the delivery of economies of scale and resources through cooperation inside and outside the ESS and the acceleration from ideas regarding AI/ML to actual production.

AIML4OS Key activities

Key activities for this project are to:

  • Develop, maintain and evolve a coherent set of relevant capabilities – including methodologies, guidelines, sandboxes, labelled data, processes, methodological, implementation and quality frameworks for implementing AI/ML based solutions in official statistics across the ESS
  • Set up a platform/hub providing a single entry point for ESS staff to access relevant capabilities
  • Provide support and guidance for the integration and maintenance of relevant AI/ML based solutions in ESS organisations through training and active and efficient support
  • Build communities around open-source solutions developed and maintained by ESS members
  • Share ideas, experiences, success stories and lessons learned to stimulate innovation based on the use of AI/ML 
  • Enable and facilitate the transition from development and experimentation of AI/ML based solutions to actual production.

AIML4OS Supporting Implementation Work Packages



WP1 Project Management and Coordination

Support, coordinate and facilitate the consortium activities, ensuring quality and sustanability of the outputs.

WP2 Communication and Community Engagement

Sharing ideas, experiences, and success stories around AI/ML solutions, and building a community around these results to stimulate innovation.

WP3 ESS AIML: Technical Infrastructure and organisational setup

Deliver an AI/ML sandpit for experimentation and development in use cases WPs, and provide documentation enabling NSIs to create their own AI/ML sandpit.


WP4 State of play & Ecosystem monitoring

Gather evidence of the current state of AI/ML use in the ESS and beyond, and identify the needs of NSIs in relation to AI/ML.

WP5 Standards & Methodologies

To improve and further develop the use of AI/ML by generalizing knowledge, developing guidelines, achieving standardization, and supporting practical implementation.

WP6 Knowledge repository and training Material

Provide support for the integration and maintenance of AI/ML-based solutions through specific training, as well as for the development of AI/ML models.

AIML40S Use Cases Work Packages

The project will develop seven use cases to test and promote AI/ML-based solutions for the production of official statistics. 


The aim is to enhance the creation of official statistics by integrating advanced artificial intelligence and machine learning techniques related to these use cases. Additionally, it includes a comprehensive review of the best practices developed to date within National Statistical Institutes (NSIs). 


The use cases were selected to ensure broad applicability across the EU. Four of the use cases aim to build on areas where AI/ML is currently being considered and, in some cases, actively used (WP7 AI/ML on Earth observation data, WP8 and WP9 focusing on Editing and Imputation in official statistics by AI/ML, and WP10 exploring the potentials of AI/ML for Classifying and Coding). These use cases will assess the progress made to date in these areas before considering ways to enhance robustness, deepen development, and broaden use across Member States. Additionally, two completely new areas for AI/ML use cases will be explored. The first new area is linked to the use of Large Language Models, while the second one investigates the feasibility of mapping supply chain networks starting from company-level data.

AIML4OS WP7 AI/ML on earth observation data, satellite imagery


AIML4OS WP10 From text to code - Experiences and potential of the use of AI/ML for classifying and coding

Experiences and potential of the use of AI/ML for classifying and coding.

AIML4OS WP13 Synthetic data

Generation of synthetic data in official statistics: techniques and applications 

AIML4OS WP8 Editing focus


The WP 8 Use case: Editing focus - Statistically valid and efficient editing and imputation in official statistics by AI/ML – with a special focus on editing aimes at improving the quality of official statistics through the innovative use of AI/ML in data editing and imputation, with a special emphasis on editing. Data Editing deals with issues that are essential for the quality of official statistics.

AIML4OS WP11 Applying ML for estimating firm-level supply chain networks

In the past decades, supply chains have become crucial in the world economy and in our daily lives.

Disruptions can spread quickly because of network effects and may have severe consequences all over the world. They also play an important role in making economies more sustainable and circular supply chain networks.


AIML4OS WP9 Imputation focus


The main objectives in this work package are to develop, test, and, if shown successful, implement AI/ML-based solutions for imputation processes. We propose three tasks.

  1. Methodological developments
  2. Development of PoC/MVP/prototypes and preparation for deployment in production
  3. Quality aspects


AIML4OS WP12 Large Language Models

This work package focuses on the use of Large Language Models (LLMs) within official statistics. The overarching goal is to explore and implement LLM technologies to enhance data management, quality control, and allow for process automation.

AIML4OS Project partners

The project is carried out by the following partners and is financed by the European Commission. 



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