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Strategic Areas of Intervention and policies

The six objectives laid out in the previous section define the goals of this strategic programme. The eleven priority sectors define where Italy intends to invest the most. This section, which lays out the areas of interventions, defines how this strategy aims to achieve the stated objectives in the priority sectors.

There are three key areas of policy intervention:

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In the following sections, this strategy will develop on these areas of intervention and describe the specific policies the Government envisions.

Talent and skills

AI has a transformative impact on all aspects of our society, and the COVID-19 pandemic has accelerated this trend. The 2020 World Economic Forum estimates that by 2025 85 million jobs may be displaced by a shift in the division of labour between humans and machines, while 97 million new roles may emerge. This transition will have significant distributional effects both across and within countries.

To mitigate the effects of such a transition (e.g., unemployment in certain segments of the population) and at the same time ensure that Italy remains on the edge of the technological frontier, the country needs to invest holistically in AI training and skills development for citizens. On the one hand, therefore, the country needs to invest in being at the forefront of AI research. This means expanding and improving the PhD programmes and attracting/retaining top researchers. On the other, Italy must ensure that the whole economy takes advantage of the productivity opportunities inherent in the diffusion of AI. Thus, it needs to strengthen the STEM component of the broader educational system, in order to support the development of a workforce that can interact with AI and exploit its benefits.

A1. Strengthening the National PhD Program

This measure aims at consolidating and expanding Italian PhD programmes overall, with the ambition of assigning an appropriate share to AI.

Objective

To consolidate and expand PhDs to train a larger share of local students to AI and attract high-quality students from abroad.

Initiatives

  • Increasing the number of PhDs. The target is set largely to make up for the loss in PhD fellowships suffered in recent years, as an intermediate step towards a further extension in numbers in the long term
  • Increasing the number of PhDs. The target is set largely to make up for the loss in PhD fellowships suffered in recent years, as an intermediate step towards a further extension in numbers in the long term

Possible source of investments

  • NRRP [1] M4C1 Investment 4.1: Extension in number and career opportunities of PhDs. 430M€ granted on a competitive basis (bottom-up approach).
  • NRRP M4C2 Investment 3.3: Introduction of innovative doctorates that respond to the enterprises’ needs for innovation and promote the hiring of researchers by companies. 600M€ granted on a competitive basis (bottom-up approach).

A2. Attracting and retaining talents

The precarious careers and the slow career advances push Italian talents towards more attractive countries and, at the same time, make Italy unattractive to foreigners. This is generating a severely negative talent balance for the country

Objective

To retain and attract AI talents in Italy and maintain Italian competitiveness in AI research.

Initiatives

Financing research activities managed independently by young researchers, who will immediately gaina first experience of research responsibility. The programme - strongly inspired by the Excellent Science Pillar of the Horizon Europe programme-will aim at attracting young researchers who are beneficiary of high-profile international grants such as the ERC starting grants and the Postdoctoral Fellowships (MSCA).

Recruiting young AI researchers under the “Rita Levi Montalcini” programme created by the Ministry of University and Research.

Possible source of investments

  • NRRP M4C2 Investment 1.2: Funding projects presented by young researchers. 600M€ granted on a competitive basis (not specifically targeting AI).
  • Fondo per la scienza (L.23 luglio 2021, n. 106) 50M€ in 2021 and 150M€ from 2022 granted on a competitive basis (bottom-up approach).
  • Rita Levi Montalcini programme 5M€/year granted on a competitive basis (bottom-up approach).

A3. Strengthening AI skills in the Public Administration

A major weakness [2] of the Public Administration in Italy is the limited share of workers with a STEM degree, particularly in AI and in the digital technologies required to properly handle the huge and increasing amount of PA data.

Objective

To increase the effectiveness of the Italian Public Administration and increase the share of PA workers knowledgeable of the opportunities and risks associated with AI.

Initiatives

Activating three cycles of new PhD programmes specifically designed for the needs of the general government in cooperation with the Ministry of Public Administration and by interacting with the Scuola Nazionale dell’Amministrazione (National School for Public Administration), an educational institution training administrative officials.

Possible source of investment

NRRP M4C1 Investment 4.1: Extension in number and career opportunities of PhDs. 430M€ granted on a competitive basis (bottom-up approach).

A4. Promoting STEM courses and careers

STEM subjects constitute the basis to develop AI skills and careers. Therefore, it is important to stimulate the interest of young generations towards STEM courses and careers, with special attention to women.

Objective

To increase the share of students studying STEM subjects, the foundations to develop AI skills.

Initiatives

The measure aims to promote the integration within the curricula of all school cycles, of activities, methodologies and contents aimed at developing STEM, digital and innovation skills, with particular attention to equal opportunities.

Possible source of investment

  • NRRP M4C1 Investment 3.1: New competences and new languages (€1.1B).
  • NRRP M4C1 Investment 3.2: School 4.0 - Innovative schools, new classrooms and laboratories

(€2.1B).

A5. AI in ITS (“Istituti Tecnici Superiori”)

The ITS training system [3] needs to respond to the demand of the labour market for specialized technicians trained for technological innovation in leading sectors of the economy.

Objective

To develop professionals who can adjust and customize existing AI technologies to solve problems in various industrial contexts.

Initiatives

Expansion of programming courses and inclusion of applied AI courses and internships in all ITS curricula.

Possible source of investment

NRRP M4C1 Investment 1.5: Development of tertiary technical education (€1.5B).

Research

As shown in chapter 1, the Italian research ecosystem shows signs of strength, yet its fragmentation, lack of resources and low patenting rhythm impede its effectiveness. To address these challenges, this section illustrates policies that aim to bridge the gap between foundational and applied research by fostering collaborations between academia, industry, public bodies and society. The future of AI necessarily implies a strong synergy among public and private research centres, industrial research and innovation centres, start-ups and SMEs, and target users’ domain expertise.

Research programme initiatives are divided into two classes:

B. Building the Italian AI research ecosystem: fundamental and applied research.

C. Horizontal aspects.

B. Building the Italian AI research ecosystem: fundamental and applied research

These initiatives have been designed to connect existing excellence and territorial activities in a single national coordination plan. They are conceived to achieve both low-TRL (Technology Readiness Level) and close-to-market results.

B1. Building on the Italian AI research ecosystem

A structured ecosystem is necessary to define a large critical mass, strengthen synergies among smaller and larger centres and emphasize “vertical” excellence in some foundational aspects.

Objective

To increase Italian competitiveness for grand AI challenges, in line with similar European and international initiatives by creating a structural connection of existing and new AI research centres in Italy.

Initiative

Creating a hub & spoke architecture with territorial expertise, especially in fundamental research.

Possible source of investment

NRRP M4C2 Investment 1.3: Partnerships extended to universities, research centres, companies and funding of basic research projects. 1.610M€ for at least 10 partnerships selected on a competitive basis. One out of 15 partnerships targets AI research (top-down approach) while AI aspects are crucial in the remaining 14 partnerships.

B2. Launching the Italian AI Research Data and Software Platform

A unique platform shared by all Italian ecosystems is necessary to keep intellectual property of Italian scientific results and provide a fast time-to-market from Italian research to Italian industry.

Objective

To generate a critical mass of open datasets and software designed at the research level, made accessible according to the FAIR principle [4], that could be reused, engineered and moved from prototype to market by start-ups and companies.

Initiative

Creating a structural connection of existing and new platforms, data and computing infrastructure devoted to AI, open-source libraries, specialised for the target topics of fundamental research in both specific technologies and trustworthy, regulatory models.

Possible source of investment

NRRP M4C2 Investment 3.1: Fund for construction of an integrated system of research and innovation infrastructures. 1.580M€ granted on a competitive basis (bottom-up approach).

B3. Creating Italian AI Research Chairs

Objective

To reinforce existing excellence and to prevent a brain drain of Italian talents towards research centres of other countries.

Initiative

Allocating specific funds for a single Principal Investigators (PIs), already enrolled in universities and national research centres to promote collaboration with industries and public bodies, according to the interests of local ecosystems. According to objective expertise, these calls could be devoted to specific free research in foundational or applicative topics [5] pproposed by a PI. 20% of the total budget may be devoted to bridge gender and territorial gaps.

Possible source of investment

Fondo per la scienza (L.23 luglio 2021, n. 106) 50M€ in 2021 and 150M€ from 2022 granted on a competitive basis (bottom-up approach).

B4. Creating AI-PRIN Curiosity-Driven Initiatives

Objective

To improve research and scientific results and collaboration among research centres.

Initiative

Calls devoted to fundamental curiosity-driven AI research (in machine learning, NLP, computer Vision, sensing, perception and action, symbolic reasoning, edge-AI, HPC-based AI) and trustworthy AI for progressing in software development, human-machine interaction, AI regulation and explanation.

Possible source of investment

NRRP M4C2 Investment 1.1: Fund for the National Research Programme (PNR) and Research Projects of Significant National Interest (PRIN). 1.800M€ granted on a competitive basis (bottom-up approach).

B5. Promoting multi-disciplinary AI National Champions

Objective

To have a high impact in the world of research and increase research result adoption.

Initiative

Challenges on specific themes with measurable and competitive result evaluation. They could be related to critical aspects of AI and linked to the defined targets of Applied Research [6]. The challenges could be coordinated with existing infrastructures such as national HPC centres, Gaia-X nodes, public and private research centre infrastructures.

Possible source of investment

NRRP M4C2 Investment 1.4: Strengthening research structures and supporting the creation of “national R&D leaders” on some key enabling technologies. 1,600M€ for 5 National Centres selected on a competitive basis. One out of 5 centres targets HPC (top-down approach) while AI aspects are crucial in the remaining 4 centres.

B6. Launching Italian AI 60-40 research-innovation calls

Objective

To impact and promote public-private partnership and contribute to giving a local characterization of AI research by allowing a regional or local support to the projects.

Initiative

Large projects on priority sectors but with free initiative proposals (similar to the National Operational Plans (PON) but 60% for public labs; 40% for companies) aiming at passing skills from research to industries, working together, creating start-ups and “innovators”. At least 10% should be devoted to creating new AI start-ups.

Possible source of investment

NRRP M4C2 Investment 1.5: Establishing and strengthening of “innovation ecosystems for sustainability”, building “territorial leaders of R&D”. 1,300M€ for up to 12 innovation ecosystems selected on a competitive basis (bottom-up approach).

C. Horizontal aspects

C1. Funding projects for Creative AI for creative Italy

Objective

To create scientific excellence in research applications in specific sectors, such as creative manufacturing.

Initiative

Grants for pioneering research in the world for creative AI, a frontier research topic that puts together new models of learning and reasoning, neuroscience experts and psychologists and creative people.

Possible source of investment

  • Fondo per la scienza (L.23 luglio 2021, n. 106) 50M€ in 2021 and 150M€ from 2022 granted on a competitive basis (bottom-up approach).
  • NRRP M4C1 Investment 4.1: Extension in number and career opportunities of PhDs. 430M€ granted on a competitive basis (bottom-up approach).
  • NRRP M4C2 Investment 1.2: Funding projects presented by young researchers. 600M€ granted on a competitive basis (not specifically targeting AI).
  • PNRRP M4C2 Investment 1.3: Partnerships extended to universities, research centres, companies andfunding of basic research projects. 1,610M€ for up to 10 partnerships selected on a competitive basis.

C2. Promoting bilateral projects for returning professionals

Objective

To increase Italy’s attractiveness to researchers and investors.

Initiative

Call for projects focused on specific topics defined by Italian priorities co-funded by another country with at least one researcher that is coming back to Italy with the same salary they had before. A similar grant should be given to the Italian PI.

Possible source of investment

  • Fondo per la scienza (L.23 luglio 2021, n. 106) 50M€ in 2021 and 150M€ from 2022 granted on a competitive basis (bottom-up approach).
  • NRRP M4C2 Investment 1.2: Funding projects presented by young researchers. 600M€ granted on a competitive basis (not specifically targeting AI).

These initiatives will be supported by existing Italian Infrastructures such as national HPC facilities for machine learning training, 5G networks for data acquisition, Gaia-X national cloud for data storing and virtualizing computation, as well as all the data infrastructures developed by the research communities, particularly those established within the ESFRI Road map.

Applications

As evidenced in the introductory chapters, the Italian AI ecosystem suffers from low patenting and a slow technology transfer process. In addition, Italian firms, large and small, have so far been slow to adopt AI solutions resulting in an AI market of limited size.

To address these challenges, this strategy proposes a set of policies aimed at broadening the breadth of AI application in industries and society, as well as measures to foster the birth and growth of innovative AI enterprises. In addition, these policies are meant to insist on priority areas and accompany the growth of sectors that have so far shown potential in AI development and adoption.

All initiatives share common issues and targets:

  • To pay particular attention to smaller companies, those operating in the most peripheral and disadvantaged geographical or socioeconomic contexts, focusing on the priority sectors (Section 2.3) and on national strategic sectors (Critical Infrastructures, sectors defined in “Decreto Golden Power”).
  • To increase the number of female AI entrepreneurs and experts, as well as attracting foreign AI-based start-ups and practitioners with economic incentives to be applied in all of the initiatives outlined below.
  • To align all AI policies related to data processing, aggregation, sharing and exchange, as well as data security with the National Strategy for Cloud and with the initiatives underway at EU level, starting with the European Data Strategy and the recent proposal for a Data Governance Act and AI Act.

To that end, this strategy identifies two areas of intervention that we deem to be of highest impact as well as most strategic.

D. AI for more modern enterprises.

E. AI for a more modern public administration.

D.AI for more modern enterprises.

The impact of AI on businesses will be of enormous relevance and should concern all enterprises. In fact, AI implies a real revolution in their modus operandi, from internal processes and customer relations to the development of new AI-based products and services. In turn, AI implies that Italian corporates would need to transform their workforce as well as processes, hiring new talent, upskilling the existing workforce and making sure such transition is carried out with the most effective and responsible use of AI solutions.

Overall, the proposed initiatives aim at:

  • Supporting the hiring process of highly skilled AI personnel in private companies, so as to reinforce their 4.0 Transitions process (machinery, HW, SW and people),
  • Increasing the adoption of AI solutions in private companies, so as to increase their competitiveness,
  • Helping start-ups and spin-offs to scale up, avoid the “valley of death” and support their national and international growth,
  • Establishing a regulatory context that may help the experimentation and the certification of reliable AI products and services that have passed such experimentation.

Therefore, this strategy supports the following initiatives:

D1. Making AI a pillar that supports enterprises’ Transition 4.0

Objective

To stimulate the transition towards a knowledge based economy; to increase the intensity of R&D expenditure compared to GDP; [7]; to curb the substantial and lasting loss of technical scientific talents, especially young people; to improve the intellectual protection of AI solutions for better competitiveness of enterprises.

Possible initiatives

Introduction of clear guidelines on AI experts salaries [8] which should be in line with international salary benchmarks,

With regard to the recruiting of senior AI experts, promotion of double appointment positions through incentives for all the parties involved,

Introduction of tax credit or vouchers for the recruitment of STEM profiles,

Updating the list of software and hardware expenses that are eligible for Transition 4.0 incentives, [9].

Leveraging the existing successful initiatives that offer educational training by academic and industrial partners, a second level Master Degree for participants and a clear path towards employment where needed [10].

Source of investment

NRRP M1C2 Investment 1: Transition 4.0 (€13.38B)

Recommended sectors

It is recommended to begin the implementation (Y1-Y2) through a couple of priority sectors - Industry & manufacturing and Banking, finance & insurance - as data indicates these are the sectors where the measure may have the largest impact. In addition to these sectors, National Security and Information technologies should also be considered. From Y2-Y3, all Priority sectors should be added.

D2. Supporting the growth of innovative spin-off and start-ups

Objective

To increase the number of AI start-ups by 30% with respect to 2021; to improve the average revenues of AI start-ups by 50% in the domestic market and 30% in export; to improve the number of scale-ups; to detect and support scale-ups and unicorns.

Initiative

Fostering talents as start-up founders: supporting entrepreneurship education for children/youngsters, encouraging university students to start a business, supporting female entrepreneurs, ensuring equal innovation opportunities, scale-up fair.

Fostering collaboration within start-up ecosystems: offering public procurement to start-ups for purchasing goods and services, fostering open innovation, fostering spin-offs, co-creating flagship projects to connect start-up ecosystem players, fostering tax incentives for growth.

Source of investment

CDP Venture Capital – Fondo Nazionale Innovazione: established by the Ministry of Economic Development, has a starting budget of 1B€ and it aims at unifying and multiplying public and private resources dedicated to the strategic topic of innovation. The Fund is a muti-fund entity, operating exclusively through the so-called venture capital methodologies.

Recommended sectors

Industry & Manufacturing, Agri Food, Health and Wellbeing, Environment, Infrastructures and networks (specifically communication and energy utilities), Banking, Finance, and Insurance and Information technologies.

D3. Promoting and facilitating experimentations of AI technologies going to market

Objective

To increase by 30% the AI products and services tested via authorized controlled experimentations.

Initiative

Promotion of Sperimentazione Italia, a sandbox which allows start-ups, companies, universities and research centres to experiment with their own innovative project for a limited period of time through a temporary exemption from the rules in force under art 36 DL 76/2020. This specific instrument facilitates the access of corporates, spin-offs, start-ups, research bodies, universities, higher technical institutes and technology transfer centres to authorised controlled experimentation for testing AI technologies under real or close to real conditions with regulatory exceptions of limited duration and perimeter, prior to their potential introduction on the market.

Recommended sectors

All Priority sectors.

D4. Supporting enterprises in AI Product Certification

Objective

To increase by 30% the number of EU-certified AI products and services from enterprises in sectors where EU certifications already exist.

Initiative

Definition of a national governance system (referring as much as possible to existing national institutions and authorities in the sector) supporting the c ertification of AI products (with higher risk profiles, in particular to health, safety or rights) going to the market with the definition of clear harmonised tools in line with the new proposal for a Regulation on artificial intelligence issued by the European Commission on 21 April 2021 (COM (2021) 206). In the health sector, a close collaboration will be warranted between the Italian government system and technical / scientific bodies at European level, called upon to provide detailed technical indications for the implementation of the rules, both of the future AI Regulation and of the Medical Device Regulation, i.e. the Regulations EU 745 and 746/2017 (the first became fully applicable on May 26, 2021), so that all the appropriate corrective measures are adopted. The goal is to ensure that the two regulations are coherent and well coordinated with each other, to the benefit of the development of the AI sector.

Recommended sectors:

All priority sectors.

D5. Promoting AI information campaigns for enterprises

Objective

To promote communication and awareness-raising campaigns on the benefits of AI products and services by reaching at least 80% of trade associations, 30% of trade association members, 80% of Competence Centres and Digital Innovation Hubs.

Initiative

Organisation of 20 communication and awareness actions on AI. The campaign will include the dissemination of the National Strategic Programme for AI to entrepreneurs and managers of interested enterprises through a coordinated action with trade associations, Competence Centres and Digital Innovation Hubs. The campaign will also focus on the risks and obligations for marketing AI products and services under national and European legislation, especially in the context of the upcoming European regulation on AI.

Source of investment

NRRP M1C2 Investment 1: Transition 4.0 (€13.38B).

Recommended sectors

All Priority Sectors.

E. AI for a more modern Public Administration

The transition to new technological paradigm based on AI will strongly affect the public administration. Indeed, thanks to AI, the Italian PA has the opportunity to embrace a modernisation process that can no longer be avoided. The use of AI allows public administrations to adapt and customise the supply of specific services and in general exploit the big-data generated within the PA to expand the public sector’s services and the opportunities for integration with firms (e.g., in healthcare, mobility), in line with privacy regulations.

The public administration can become a real driver of AI development, thanks to the data it produces and to its role as a more potential purchaser of innovative goods or services. Consequently, it is essential to make existing data usable by public administrations, in accordance with GDPR regulations, the principles of privacy by design, ethics by design and human-centred design, and by creating forms of data aggregation (e.g. data lake). At the same time, the availability of data is a necessary but not sufficient condition for designing a new PA. To do so, it needs to be equipped with appropriate skills, procedures and tools.

To this end, we propose the following initiatives for the promotion of AI within the PA and for the PA:

E1. Creating integrated datasets for Open Data and Open AI Models

Objective

To ensure common standards in terms of form, structure and granularity on Data and AI Software and Services as well as compliance protocols with national and EU regulations. To favour the development of advanced analysis and/or software solutions that exploit the enormous big data potential of the PA from its interactions with citizens.

Initiative

Integrating the various PA data feeds to make them highly interoperable, open to private companies for AI-software development but also to be used in the design and implementation phase of new algorithms, new learning models and AI systems released by the different administrations and open to be reused, with regard for the trustworthiness issues of national and EU regulations and in compliance with the rules for the protection of personal data. In addition, regularly updating the guidelines for reusable Open Data for AI models with extensively large and annotated datasets (e.g. data for smart mobility). Policies will be the basis for Italian Participation in the Common European Data Space of the PA, envisaged by the European Data Strategy. This will be done jointly with the implementation of already existing standards alongside the establishment of reward mechanisms for individual PA managers based on the compliance of their data structure and AI-based services with the indicated criteria.

E2. Strengthening AI solutions in the PA and the GovTech ecosystem in Italy

Objective

To develop AI solutions matching the needs outlined in the priority actions linked to the PA and public sectors, namely: 1) digitalisation and modernisation of public administration; 2) protection of land and water resources; 3) road maintenance 4.0; 4) telemedicine, innovation and digitalisation of healthcare. Support the development of an Italian GovTech start-up ecosystem.

Initiative

Introduction of periodic calls to identify and support start-ups with potential AI-based solutions to PA’s pain points, through an accelerator-like programme that turns ideas/research projects into applicable solutions and scalable companies. CITD [11] periodically identifies, through a technical committee of experts supported by ministerial staff, well-defined key challenges for the PA that could also represent large revenue opportunities for solutions suppliers (e.g. AI for simplifying and accelerating the management of public procurement contracts and related guarantees). Challenges are published and a professionally run accelerator partner develops acceleration programmes revolving around the challenges. MITD ensures that innovative procurement offers possibility for PA contracts after acceleration and supports start-ups in abiding to European AI and data regulation.

Investment

Il comitato tecnico del MITD valuta il raggiungimento degli obiettivi da parte delle start-up e assegna premi per le prime tre aziende che raggiungono ciascun obiettivo. L’acceleratore partner, in coordinamento con il comitato tecnico del MITD [12], esegue la prima selezione delle start-up e fornisce il finanziamento iniziale, il mentoring e l’accesso a investitori di venture capital.

E3. Creating a common Italian language resource dataset for AI development

Objective

To ensure that researchers, businesses and public administration have access to a high-quality shared language resource (very-large datasets of Italian language documents on which AI language models can be trained), thus increasing Italian competitiveness in the field as well as the AI-based solutions available for Italian citizens.

Initiative

Creation of an open and shared language resource structured collection of digital datasets of Italian documents available to everyone for free through a collaboration between both public and private players. This resource will collect text files, sound files, and terminology banks, which can be used to develop text mining, chatbots, conversational interfaces, multilingual translation, text generation or other services improving both public and private services. The initiative will effectively help bridge the scale gap between aspiring Italian AI companies/services and larger international tech companies that have access to their own private databases.

E4. Creating datasets and AI/NLP based analytics for feedback and service improvement in PA

Objective

To improve the quality of in-person and digital interactions of citizens with the PA.

Initiative

Create annotated anonimised dataset of citizens-PA interactions (online activity but also feedback from in-person interactions, e.g. from the National Institute for Social Security known as INPS) to support the development or integration of AI tools/technology providers to develop new services of conversational interfaces, sentiment analysis, pain-points detection and prediction and support employers to identify possible solutions. Create specific calls for providing scalable solutions at national level for the PA.

E5. Creating datasets and AI/Computer Vision based analytics for service improvement in PA

Objective

To support the PA in extracting knowledge from digitized visual documents, video and satellite images.

Initiative

Create a very large annotated dataset of satellite images of urban and environmental landscapes, digitized land registry images, urban and suburban video for mobility 5.0 applications, and support specific calls to provide computer vision solutions with open source code or software licensed for PA use. Potential applications could be a) land registry categorization, anomaly identification in land registry, recognition of cadastral anomalies versus urban planning data, b) satellite images of urban and suburban areas to support construction sectors and infrastructure monitoring, c) satellite data and urban camera video of national roads for short-term and long-term traffic prediction.

E6. Introducing cross-authority case processing

Objective

To improve the quality of service centres for citizens and simplify the problem-solving process in a more efficient way by reducing case processing time.

Initiative

Introduce AI-based technologies to automate the sorting and preparation of inquiries for processing. For instance, automation will involve: screening, comparison, categorisation and decision support in case processing; automatic comparison of textual/visual digitized documents; robotic process automation (RPA); supporting PA employers in standard answers. Hence, case officers will be able to concentrate on the most critical cases. Optimisation with case processing is relevant for various authorities, such as the citizen service centres and the subsidies administration area.

TAll the initiatives for the applications of AI to the PA will be funded predominantly via PCM [13]-MITD/PA resources, in partnership with other public/private institutions where relevant.


[1]National Recovery and Resilience Plan
[2]According to the Worldwide Governance Indicators of the World Bank, the effectiveness of the Italian Public Administration (PA) ranks well below the PA effectiveness in France, Germany and Spain.
[3]8ITSs are schools of excellence with a high technological specialisation that allow students to obtain a higher technical diploma. They represent an opportunity of absolute importance in the Italian training panorama connecting education, training and employment policies with industrial policies: the aim is to support interventions in productive sectors, with particular reference to the innovation and technology transfer needs of small and medium-sized enterprises.
[4]Cfr https://www.go-fair.org/fair-principles/
[5]Some projects could be highly risky and foundational, e.g. sustainable energy saving machine learning or applicative: e.g. predicting congestion and traffic jams in some interchange mobility nodes near airports and finding automated solutions for minimizing pollution.
[6]It could include Public Sectors and society challenges (e.g. for technologies in support to Justice as defined in NRRP), initiatives for Transitions 4.0, co- funded by MUR and by private companies with NRRP incentives, for Space data analysis, for Environment and ecological transitions (e.g. working on satellite and aerospace images), for health (e.g. working con COVID data) and for cultural economy and renewing tourist offers with AI technologies and eventually for new initiatives for climate change.
[7]1.4% nel 2019
[8]E.g.salary guidelines of the Marie Sklodowska Curie Action
[9]These assets should include for instance (the list is just explicative and not exhaustive). For tangible assets: computing hardware such as HPC based on GPU or CPU units, GPU computers, data storage and management, etc. Devices equipped with on board/edge computing facilities and/or digital sensors, with various degrees of autonomy such as: drones, robot arms, wearable devices, etc. For non tangible assets: AI software licenses, subscription to editorial resources and participation to high-ranked, international AI academic conferences and events.
[10]For instance the 2nd level Specializing Master’s programme in “Artificial Intelligence & Cloud: Hands-on innovation” offered by Politecnico di Torino or the initiative “Advanced School in AI” funded by Regione Emilia Romagna with the contribution of all regional universities.
[11]Inter-ministerial Committee for the Digital Transition
[12]Ministry for Technological Innovation and Digital Transition
[13]Presidency of the Council of Ministers