Let AI Fly. The JARVIS project: Executive Summary of AI Challenges Whitepapers

The rapid evolution of artificial intelligence (AI) is reshaping every industry, and aviation is no exception. As the aviation industry seeks to meet higher demands for capacity, safety and sustainability, questions arise: How can AI technologies be integrated into day-to-day operations in a responsible manner? Which AI-specific challenges have to be addressed? The JARVIS project tackles this question head‑on. By developing a range of AI‑driven digital assistants, one for air‑traffic controllers, one for cockpit crews, and one for airport‑ground personnel, JARVIS demonstrates how AI can automate tasks, augment decision‑making, reduce workload and improve situational awareness across the entire air‑traffic‑management (ATM) ecosystem. The project therefore serves as a specific case study of digital transformation in aviation operations. This document summarizes the project activities and lessons learned from the development of digital assistants, as well as the four grand AI challenges identified: AI Assurance, Dataset Creation, Human-AI Teaming and Ethics. These findings are published in whitepapers and form the theoretical foundation of the JARVIS project. This executive summary provides a concise overview of the whitepapers, their lessons learned and recommendations and is a navigation aid for readers. This document is part of the JARVIS (Just a rather very intelligent system) project that has received funding from the SESAR 3 Joint Undertaking under grant agreement No 101114692 under European Union’s Horizon Europe research and innovation programme. It summarizes contributions from the Work package “Foundational AI”, as well as lessons learned derived by the whole project community. This work would not have been possible without all the great people from the JARVIS project and from EASA who shared knowledge, ideas and insights. We are indebted to the teams behind SESAR solutions 0364, 0365, 0554, 0555, 0556 and 0557. Ourlessons learned are standing on the shoulders of giants.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-21
DOI
https://doi.org/10.5281/zenodo.20325760
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Let AI Fly. The JARVIS project: Executive Summary of AI Challenges Whitepapers

Dmitrii Kirov, Arthur Clavière, Anne Papenfuß, Gabriella Gigante et al.
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

Let AI Fly. The JARVIS project: Executive Summary of AI Challenges Whitepapers

Dmitrii Kirov, Arthur Clavière, Anne Papenfuß, Gabriella Gigante, Jakob Würfel, Elisa Spiller, Antoine Alaverdov, Elena Rankova, Rolf Zon, Ana Ferreira
article en

Abstract

The rapid evolution of artificial intelligence (AI) is reshaping every industry, and aviation is no exception. As the aviation industry seeks to meet higher demands for capacity, safety and sustainability, questions arise: How can AI technologies be integrated into day-to-day operations in a responsible manner? Which AI-specific challenges have to be addressed? The JARVIS project tackles this question head‑on. By developing a range of AI‑driven digital assistants, one for air‑traffic controllers, one for cockpit crews, and one for airport‑ground personnel, JARVIS demonstrates how AI can automate tasks, augment decision‑making, reduce workload and improve situational awareness across the entire air‑traffic‑management (ATM) ecosystem. The project therefore serves as a specific case study of digital transformation in aviation operations. This document summarizes the project activities and lessons learned from the development of digital assistants, as well as the four grand AI challenges identified: AI Assurance, Dataset Creation, Human-AI Teaming and Ethics. These findings are published in whitepapers and form the theoretical foundation of the JARVIS project. This executive summary provides a concise overview of the whitepapers, their lessons learned and recommendations and is a navigation aid for readers. This document is part of the JARVIS (Just a rather very intelligent system) project that has received funding from the SESAR 3 Joint Undertaking under grant agreement No 101114692 under European Union’s Horizon Europe research and innovation programme. It summarizes contributions from the Work package “Foundational AI”, as well as lessons learned derived by the whole project community. This work would not have been possible without all the great people from the JARVIS project and from EASA who shared knowledge, ideas and insights. We are indebted to the teams behind SESAR solutions 0364, 0365, 0554, 0555, 0556 and 0557. Ourlessons learned are standing on the shoulders of giants.

Zenodo (CERN European Organization for Nuclear Research)
École Nationale de l’Aviation Civile (FR), Italian Aerospace Research Centre (IT), Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE), Deep Blue (Italy) (IT), Piaggio Aerospace (Italy) (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Ethics and Social Impacts of AI
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