Italian Strategy for Artificial Intelligence 2024-2026
ITALIAN STRATEGY FOR ARTIFICIAL INTELLIGENCE 2024-2026
The Italian Strategy for Artificial Intelligence 2024-2026, published by the Agency for Digital Italy (AgID), provides a comprehensive national framework to foster AI development, integration, and governance across research, public administration, enterprises, and education. It emphasizes human-centric, reliable, and sustainable AI solutions aligned with European values, while addressing key risks such as non-action, homogenization, and digital divides. The strategy outlines concrete action areas, infrastructural enablers like a national AI knowledge registry, and a foundational governance model to boost Italy's international competitiveness.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Policymakers, public administrators, researchers, educators, and enterprise leaders looking to understand or contribute to Italy's national AI roadmap.
What the document contains
The document covers national strategic objectives, challenges, and specific actions across research, public administration, enterprise support, and education. It includes infrastructural needs such as dataset registries and network improvements, as well as monitoring mechanisms and regulatory frameworks.
The document does not establish direct binding legal obligations or fines, as it is a strategic policy document rather than primary legislation.
- Italy aims to position itself as a leading player in trustworthy, anthropocentric, and sustainable AI in line with European frameworks.
- The strategy targets four core pillars: Research, Public Administration, Enterprises, and Training.
- An enabling infrastructure, including a national registry of datasets and models (Action A.1) and a Foundation for AI coordination, will support implementation.
- Special emphasis is placed on uplifting SMEs, retaining and attracting talent, and addressing digital literacy and upskilling across the workforce.
- A risk-based approach is adopted to mitigate the risks of inaction, cultural homogenization, regulatory over-supervision, and the digital divide.
· INTRODUCTION · CONTEXT ANALYSIS · PRINCIPLES OF THE STRATEGY · STRATEGY DESIGN · STRATEGY FOR RESEARCH · STRATEGY FOR PUBLIC ADMINISTRATION · STRATEGY FOR ENTERPRISES · STRATEGY FOR TRAINING · MONITORING THE STRATEGY · REGULATORY BODIES
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
3/5Clear responsibility for AI outcomes
research on new challenges created by AI on issues such as copyright, deepfakes, online disinformation, and responsibility
R.4 | SYNERGIES: INTERDISCIPLINARY PROJECTS FOR SOCIAL WELLBEING
screening score · 2026-10-23
Fairness
3/5Non-discrimination and equitable treatment
ensure compliance with non-discrimination laws, privacy (GDPR), human rights protection
R.3 | DESIGN OF ITALIAN LARGE MOLECULE MODELS (LMM)
screening score · 2026-10-23
Transparency
4/5Openness about how AI systems work and are used
transparency of training data, to ensure compliance with non-discrimination laws, privacy (GDPR), human rights protection, providing reliable information on the sources from which content is generated
R.3 | DESIGN OF ITALIAN LARGE MOLECULE MODELS (LMM)
screening score · 2026-10-23