In an era where Artificial Intelligence (AI) is becoming increasingly prevalent, its potential to transform the public sector is undeniable. However, the spread of AI in the public sector largely depends on the availability of adequate skills and the adoption of effective governance practices. This article is based on a synthesis of empirical research, gray and policy literature, an expert workshop, and interviews with representatives from seven European public organizations (Ministry of the Interior of the Czech Republic, Municipality of Gladsaxe in Denmark, Lüneburg District in Germany, Ministry of Digital Governance of Greece, National Social Security Institute in Italy, Municipality of Amsterdam in the Netherlands, and Municipality of Trondheim in Norway) to identify the skills and governance practices needed to generate value in the public sector through AI. The main authors of the research are R. Medaglia, P. Mikalef, and L. Tangi, from Copenhagen Business School, the Norwegian University of Science and Technology, and the European Commission's Joint Research Centre, respectively.
The European Regulatory Framework
The European Union's commitment to AI began with the Declaration of Cooperation in 2018 and further advanced with the revision of the Coordinated Plan on AI in 2021, highlighting AI's strategic role in the public sector. Today, there are numerous initiatives and legislative measures to facilitate the integration of AI in public administration. Among these, the AI Act and the Interoperable Europe Act, both adopted in 2024, stand out. The AI Act establishes a risk-based approach to AI regulation, banning systems that pose unacceptable risks and defining high-risk applications subject to stringent controls. This act also promotes innovation through regulatory sandboxes and led to the formation of the European Artificial Intelligence Board and an EU database for high-risk AI systems.
The Interoperable Europe Act, proposed in November 2022 and adopted in April 2024, aims to improve cross-border interoperability of IT systems used in public services. It introduces the Interoperable European Board, responsible for defining a shared strategic agenda for cross-border interoperability, and requires interoperability assessments for IT systems operating across borders. Additionally, it announced the launch of the Interoperable Europe Portal, a collaborative platform for sharing and reusing IT solutions. This act also encourages innovation through regulatory sandboxes and GovTech partnerships.
Other relevant laws include the Digital Services Act (DSA), which aims to establish clear rules for digital service providers ensuring user safety and greater transparency, the Digital Markets Act (DMA), designed to ensure fair conditions in the digital market, and the Data Governance Act (DGA), which aims to increase trust in data sharing and overcome technical barriers to their reuse. The legislation also includes the Data Act and the Cybersecurity Act, all aimed at creating a secure and interoperable digital ecosystem.
A key initiative in this area is the Public Sector Tech Watch (PSTW), an observatory established in 2023 and managed by the European Commission's Directorate General for Digital Services and the Joint Research Centre (JRC). PSTW serves as a platform for exchanging knowledge, experiences, and educational resources among public employees, private companies, academic institutions, and policy strategists, facilitating digital transformation and compatibility of European public systems. PSTW includes a database of over 1,000 use cases of AI and other emerging technologies in the public sector and promotes a collaborative environment for sharing practices and experiences, also through initiatives such as best use case competitions. Furthermore, the Technical Support Instrument (TSI) and initiatives like "AI-ready public administration" provide tailored technical support to Member States to prepare for AI adoption, including GovTech partnerships and model contracts for procuring reliable and secure AI solutions.
AI Governance for Public Sector Transformation: Research Methodology
The report is based on a three-phase methodology aimed at developing a comprehensive and updated view of the skills and governance practices required for AI use in public organizations. The first phase involved a systematic review of academic literature and policy and gray documentation.
The second phase involved an online workshop with 40 sector experts, held on October 25, 2023, aimed at consolidating and deepening the findings obtained in the literature review phase. The experts came from various public organizations, and the workshop was structured into working sessions divided into discussion groups to explore in depth both AI skills and governance practices. The workshop results were used to verify the findings from the literature review and were summarized in a report.
Finally, in the third phase, semi-structured interviews were conducted with leaders of seven European public organizations in various countries (Czech Republic, Denmark, Germany, Greece, Italy, Netherlands, and Norway), to enrich and validate the results. A total of 19 interviews were conducted between May and November 2023. The interviews focused on individual experience with AI, the perception of AI's relevance in the specific work environment, and the perceived difficulties in acquiring AI skills in the public sector. The interview transcripts were processed with the help of automatic transcription software and then manually reviewed to ensure accuracy.
Competency Framework for AI in the Public Sector
The report presents a comprehensive framework of the skills required for the adoption and use of AI in the public sector, distinguishing between technical, managerial, political, legal, and ethical skills. These skills are further classified into three clusters: attitudinal skills (knowledge of "why"), operational skills (knowledge of "how"), and literacy skills (knowledge of "what").
Technical skills include in-depth knowledge of technology, data management skills, the ability to evaluate data quality, and to select appropriate AI architectures. On the operational side, data management, AI-targeted software programming, and adherence to the technical standards required in this field are essential. As for the attitudinal aspect, curiosity about technological innovations and a commitment to continuous learning are essential qualities for successfully facing AI challenges.
Managerial skills include leadership, change management, and the ability to mediate between different interest groups. In particular, leadership is seen as the ability to lead AI initiatives and integrate the technology in an ethical and effective way, while change management involves the ability to adapt organizational processes to AI adoption.
Political, legal, and ethical skills include awareness of ethical implications and the ability to work with sector experts to ensure that AI adoption takes place responsibly. It is essential that public officials have the ability to formulate policy questions compatible with AI techniques and collaborate with domain experts to translate complex concepts into practical solutions. The ability to audit and ensure compliance with design and accountability standards is also fundamental.
Literacy skills include an understanding of the fundamentals of machine learning, computer vision, and natural language processing (NLP), as well as a thorough knowledge of legal frameworks and public policies. In addition, the ability to manage the procurement of AI solutions in a manner consistent with public interest values is seen as a crucial skill to ensure that AI is used fairly and transparently in the public sector.
Governance Practices for AI
The report distinguishes governance practices into three main dimensions: procedural, structural, and relational. Each dimension is articulated at three levels: strategic, tactical, and operational. The goal of governance practices is to ensure consistency between the organization's objectives and the technology used to achieve them. This means implementing rules and regulations that guide the responsible use of AI and foster a culture of open and collaborative innovation.
Procedural practices refer to the processes and rules that need to be put in place to manage AI responsibly. These include the adoption of guidelines for ethical AI development, the definition of standards for data management, and the creation of criteria for AI system auditing. A significant example is the use of compliance frameworks that include ethical and legal impact assessments throughout the AI lifecycle to ensure compliance with European regulations such as the AI Act and GDPR.
Structural practices concern the internal organization and the distribution of roles and responsibilities related to AI. This involves creating AI-dedicated units, appointing Chief AI Officers, and defining governance policies to ensure that AI initiatives are aligned with the organization's overall strategy. Public organizations need to establish multidisciplinary teams that include AI experts, data analysts, lawyers, and ethics experts to monitor and oversee AI implementation. This ensures that AI use is managed to respect public interest values.
Relational practices focus on managing relationships among different stakeholders, both internal and external to the organization. This includes collaboration with other government agencies, engagement with local communities, and the creation of partnerships with the private sector and universities. A key element is transparency and citizen engagement through public consultations and sharing information on AI applications in use. These practices aim to build trust and ensure that AI is developed and used responsibly and with public consent.
Strategic governance involves defining a clear vision for AI use, with long-term goals that include innovation and improving public services. At the tactical level, governance practices include resource planning and risk management associated with AI implementation, while at the operational level, they focus on staff training, resource allocation, and continuous monitoring of AI system performance. The adoption of a continuous feedback cycle approach is essential to ensure that AI solutions are adaptive and able to respond to changing organizational requirements and citizen expectations.
Recommendations and Future Perspectives
Based on the analysis carried out, the report presents six recommendations for the development of AI skills and governance practices in the public sector. These recommendations aim to create a favorable environment for the ethical and effective adoption of AI, promoting a culture of innovation, continuous improvement, and social responsibility. Below, the main recommendations and related actions are outlined in detail:
Continuous Training and Skill Development
Continuous training is an essential element to ensure that public sector personnel can make the most of AI's potential. Several strategic actions have been identified to develop the skills needed for the adoption and effective management of AI technologies.
Continuous Training Programs: Training programs should be designed to include various levels of complexity, starting from general AI literacy for all public employees to advanced courses for those working directly with AI technologies. The content of these courses should include the fundamentals of machine learning, basic natural language processing concepts, AI's ethical implications, and data management practices.
Practical Workshops and Case Studies: Theory must be complemented with practical workshops and case studies. Workshops can include sessions on programming and configuring AI models, as well as simulations to understand automated decision-making processes. Case study analysis, on the other hand, will allow officials to see examples of both successful and unsuccessful AI applications, helping to understand real challenges and opportunities.
Collaborations with Universities and Research Centers: The public sector should actively collaborate with universities and research centers to develop specific and customized courses. Such collaboration can guarantee continuous access to the latest technological innovations and academic best practices, as well as foster the co-creation of training content that meets the specific needs of public administrations.
Mentorship Programs: Mentorship represents an important tool to accelerate skills transfer. AI experts and senior figures within public organizations can be assigned as mentors to new staff members or those needing to develop specific AI skills. Mentorship can be useful not only for conveying technical knowledge but also for addressing aspects related to change management and communicating AI projects to various stakeholders.
Training in Ethical and Regulatory Aspects: Training must not be limited to the technical aspects of AI but must also include skills in the ethical and regulatory fields. Staff must be aware of the ethical implications of AI use, understand the potential risks associated with algorithmic biases, and ensure the protection of personal data. Knowledge of relevant regulations, such as the AI Act and GDPR, must be an integral part of training programs.
Modular and Customized Approach: A crucial aspect of training programs must be modularity. Each public employee has different needs and levels of competence; therefore, training must be customized and modular. This allows learning paths to be adapted based on specific roles and the level of responsibility of employees in the adoption of AI.
Use of E-Learning Platforms and Certifications: E-learning platforms can be used to ensure continuous access to training resources, allowing employees to learn at their own pace. The introduction of official certifications can also encourage participation in courses and ensure the recognition of acquired skills.
Continuous Evaluation and Updating of Programs: Training programs must be subject to periodic evaluation to ensure their effectiveness and updating concerning continuous technological and regulatory changes. The needs of the public sector evolve, as do AI technologies; therefore, course content must be regularly updated to maintain relevance and effectiveness.
2 . Promotion of Public Private Partnerships:
Public-private partnerships are a key element in fostering the adoption of innovative AI solutions and accessing cutting-edge skills and technologies. Collaboration between public administrations, technology companies, and research institutions can ensure faster and more effective development of AI solutions, as well as contribute to building a sustainable innovation ecosystem oriented towards the needs of the community.
Below, the main elements and benefits of public-private partnerships are outlined in detail:
Collaboration with Technology Companies: Public administrations can greatly benefit from the experience and innovation of the private sector. Partnerships with technology companies enable access to advanced resources and technical skills that are often not available internally. For example, through these partnerships, public organizations can benefit from the use of advanced analytics platforms, pre-trained machine learning systems, and cloud computing solutions for data management.
R&D Projects with Academic Institutions: Collaboration with universities and research centers is essential for developing applied research and technology transfer projects. These partnerships not only foster innovation but also ensure that AI solutions are based on solid scientific principles and rigorously tested before large-scale implementation. Such collaborations can also involve creating joint innovation labs and co-designing technology solutions with researchers and students.
Access to Funding and Resources: Partnerships with the private sector can also facilitate access to additional financial resources needed to support AI implementation. Private companies can co-finance innovative AI projects, reducing the financial risk for public administrations and making it easier to experiment with pioneering solutions. In addition, partnerships can allow administrations to benefit from technological infrastructure and advanced tools they would otherwise not have access to.
Development of Shared Solutions: Solutions developed through public-private partnerships can often be adapted and reused in different contexts. This reduces costs and speeds up the digital transformation process. For example, an AI model developed to improve healthcare efficiency in one region can be used as a basis for developing similar solutions in other regions or in other public administration sectors, such as education or transport.
Ensuring Transparency and Compliance: It is crucial that public-private partnerships are structured to ensure maximum transparency and citizen data protection. For this reason, clear protocols must be defined for data management, privacy, and information security. Defining standards and guidelines for transparency is essential to maintain citizens' trust in AI use by public administrations. Partnerships must include detailed agreements defining roles, responsibilities, and data sharing methods.
Promotion of Innovation through Competitions and Awards: One way to encourage private companies to participate in developing AI solutions for the public sector is through competitions and hackathons. These events can attract startups, small and medium-sized enterprises (SMEs), and large companies to contribute ideas and innovative solutions. Healthy competition and the possibility of winning prizes or contracts with public administrations stimulate creativity and the generation of new ideas.
Support for the Creation of Innovation Ecosystems: Public-private partnerships can also support the creation of local innovation ecosystems, involving not only large companies but also startups, SMEs, and business incubators. These ecosystems are essential to create a fertile environment where new ideas can be tested and developed. Public administrations can facilitate the creation of such ecosystems by promoting access to funding, offering tax incentives, and creating physical spaces where public and private entities can collaborate.
These actions aim to create effective synergy between public and private sectors to maximize the value generated by AI for the common good and ensure that the solutions adopted are aligned with ethical standards and community needs. Only through joint commitment and open cooperation will it be possible to fully exploit AI's potential to improve public services and citizens' quality of life.
Regulatory Experimentation and Sandbox Areas
Regulatory experimentation and sandbox areas are fundamental tools for the effective adoption of AI in the public sector. These initiatives allow testing new technologies and innovative approaches in a controlled environment (a sandbox is a protected environment where solutions can be tested without impacting real systems or violating regulations), minimizing the risks associated with implementation and ensuring that solutions comply with existing regulations.
The main elements and actions related to regulatory experimentation and sandboxes are described below:
AI Sandboxes: Sandboxes allow public administrations to test new AI solutions in a regulated environment with an adequate level of supervision. These sandboxes are created to ensure that emerging technologies can be developed, evaluated, and refined before their widespread deployment. Sandbox areas provide a protected environment where administrations can collaborate with technology companies, startups, and universities to develop innovative AI applications, reducing the risk of costly failures and improving the quality of final solutions.
Citizen Involvement: Citizen involvement is a crucial aspect of sandbox areas. Public consultations and feedback processes allow the social impact of AI technologies to be evaluated, ensuring that the solutions developed respond to community needs and respect public interest values. Directly involving citizens in experimentation processes can also help increase trust in AI solutions, showing how the risks associated with technology implementation are managed.
Impact Assessment and Transparency: Every project initiated within sandbox areas must be subject to a rigorous ethical, social, and legal impact assessment. The impact assessment allows potential risks related to privacy, algorithmic discrimination, or other critical aspects to be identified and corrective measures to be introduced before large-scale implementation. Moreover, it is essential to ensure the transparency of the test results conducted in sandbox areas by publishing detailed reports describing the experimentation process, results obtained, and lessons learned.
Guidelines for Sandbox Implementation: To ensure effective use of sandbox areas, clear guidelines must be established defining the process of creating and managing sandboxes, the criteria for selecting projects to be tested, and the methods of supervision. These guidelines must ensure that all projects are in line with the values and objectives of the public administration, comply with existing regulations, and adopt a risk-based approach to ensure the safety and compliance of developed solutions.
Regulatory and Financial Support: Creating sandbox areas requires adequate regulatory and financial support. Public administrations must be able to rely on a flexible regulatory framework that allows regulatory experimentation without excessive constraints. At the same time, financial resources must be available to support the costs of experimentation, including those related to technological infrastructure and training of involved personnel.
Feedback and Continuous Improvement: One of the goals of sandbox areas is to create a continuous cycle of feedback and improvement. Every experimentation should be followed by a careful analysis of results to improve not only the tested technology but also the experimentation process itself. This iterative approach allows AI solutions to be adapted to the real needs of public administrations and citizens, ensuring that every development phase is based on learning and continuous improvement.
Integration with European Innovation Policies: Regulatory sandbox areas must be closely integrated with European policies on innovation and AI, such as the AI Act and the Interoperable Europe Act. This integration is essential to ensure that solutions developed in sandboxes are aligned with European regulations and can be easily scaled at a cross-border level, promoting greater interoperability and a wider spread of best practices in the public sector.
These practices of regulatory experimentation and sandbox areas aim to reduce the risk associated with adopting innovative technologies, improve the quality of developed solutions, and ensure that AI is used responsibly and transparently in the public sector. The combination of experimentation, collaboration, and impact assessment represents a comprehensive approach to maximizing AI's potential and ensuring that the benefits are equitably distributed among all citizens.
Strengthening Ethical and Legal Governance Practices:
Strengthening ethical and legal governance practices is crucial to ensure that AI adoption in the public sector takes place responsibly and in line with community values.
Below are the main actions to be taken to ensure ethical and legal AI implementation:
Creation of ethical guidelines for AI development: Ethical guidelines are needed to establish clear criteria for the development and use of AI in the public sector. These guidelines must cover various aspects, including data collection and use, bias management, responsibility of developers and operators, and privacy protection. The guidelines must be integrated into procurement and development processes, ensuring that each adopted AI solution aligns with approved ethical principles and the European regulatory framework.
Ethical and legal impact assessments: Each AI project must be accompanied by an ethical and legal impact assessment that analyzes its potential consequences in terms of fairness, privacy, security, and transparency. These assessments must be conducted early and updated throughout the project lifecycle, identifying potential risks and providing corrective measures to mitigate them.
Establishment of ethical committees: The creation of ethical committees at the national or local level aims to oversee key AI decisions. These committees must be composed of ethics experts, public sector representatives, academics, and civil society members. Their role is to assess AI projects, offer ethical recommendations, and ensure that the principles of fairness and non-discrimination are respected and that the public interest is always at the center of decisions made.
Definition of standards for algorithmic auditing: Algorithms used by public administrations must be subject to periodic audits to ensure compliance with regulations and prevent bias or misuse. Auditing must include a transparent analysis of the algorithm's functioning, identification of possible distortions, and verification of accuracy and reliability. It is important to establish a formal process for auditing and identify key performance indicators (KPIs) that allow the effectiveness and impact of algorithms to be evaluated.
Ensuring transparency and accountability: To strengthen AI governance, it is essential to promote transparency at every stage of AI technology development and implementation. Public administrations must clearly communicate the purposes for which AI is used, the data employed, and how algorithmic decisions are made. Accountability must be ensured through a governance system that includes mechanisms for accountability and that allows citizens to challenge decisions made by AI technologies where they may significantly impact their rights.
Control over data collection and use: Data is the foundation on which AI models are trained, and it is therefore essential that data collection and use are carried out responsibly. Public administrations must ensure that the collected data is of high quality, relevant, and managed according to privacy regulations. Data minimization, i.e., collecting only the strictly necessary data, and pseudonymization (a technique that replaces identifying data with pseudonymous identifiers to protect individuals' identities) are key practices for ensuring the safe and compliant use of personal data.
These actions aim to ensure that AI adoption in the public sector takes place safely, responsibly, and in line with public interest values. Strengthening ethical and legal governance practices is a crucial component to promoting citizen trust in AI use and ensuring that this technology contributes to improving public services without compromising individual rights and freedoms.
Creating a Support Ecosystem for Digital Transformation:
Creating a support ecosystem for digital transformation in the public sector is not just about providing financial and technological resources but also about developing a network of actors and institutions that work together to foster innovation. Below are the main components and actions necessary to ensure an effective and resilient ecosystem for digital transformation:
Institutional and political support: It is essential that there is solid institutional support for digital transformation. Governments must develop clear strategic plans for AI adoption and other digital technologies, including specific objectives and defined deadlines. This support must be accompanied by favorable policies that encourage digitalization, remove bureaucratic barriers, and promote a coordinated vision across all levels of public administration, from national institutions to local communities.
Knowledge-sharing platforms: Knowledge sharing is a key element for digital transformation. Public administrations must have access to platforms that facilitate the exchange of experiences, best practices, and case studies. Platforms such as the Public Sector Tech Watch (PSTW) can help reduce the learning curve for new technologies and enable the rapid dissemination of innovations that have been successful in other settings. The availability of easily accessible resources and documentation is crucial to accelerating the digitalization process.
Financial support and access to European funds: Digital transformation requires significant investments, and it is essential that public administrations have access to adequate funding. Funds such as Horizon Europe, the Digital Europe Programme, and the Recovery and Resilience Facility (RRF) are crucial to supporting large-scale digital transformation projects. However, it is equally important to provide technical and consulting support to administrations to facilitate access to these funds, ensuring that even small and medium administrations can benefit from these financial opportunities.
Incentives for innovation and recruitment of digital talent: Public administrations must create incentives to attract and retain talent with digital skills. Hiring experts in AI, data science, and digital transformation is crucial for the success of any innovation strategy. Incentives such as innovation awards, advanced training opportunities, and dedicated career paths can help build an expert team capable of driving change within administrations. Additionally, recruitment programs targeting new generations of digital talent can help bridge the technology skills gap in the public sector.
Flexible regulatory framework: The success of digital transformation also depends on the presence of an appropriate regulatory framework. Member States must adopt a regulatory approach that is flexible enough to allow innovation while at the same time protecting citizens from potential abuses. Regulations must be updated periodically to reflect the evolution of technologies and societal needs, ensuring that they align with ethical principles and human rights protections.
These actions and components are essential for creating a support ecosystem for digital transformation in the public sector. Only through access to adequate resources and strong institutional commitment will it be possible to fully harness the potential of AI and emerging technologies.
Promoting a Culture of Innovation and Calculated Risk:
Promoting a culture of innovation and calculated risk is essential to ensure that the public sector can experiment with and adopt new technologies such as AI without being paralyzed by fear of failure. A culture that accepts calculated risk and encourages innovation can produce more creative and effective solutions to respond to public sector challenges. Below are the main actions to take to build a culture of innovation and calculated risk:
Encourage experimentation and learning from mistakes: It is crucial to create an environment where mistakes are considered part of the learning process, rather than failures to be avoided at all costs. Public administrations must promote a culture in which staff are encouraged to experiment with new solutions and learn from mistakes. This can be achieved through pilot programs that allow new ideas to be tested in a protected environment without the negative consequences of immediate large-scale implementation.
Training and support for managing innovation: Innovation management requires specific skills that are often not present in traditional public sector structures. For this reason, it is important to provide specific training for managers and project leaders to develop the skills needed to manage innovative processes and make strategic decisions in situations of uncertainty. This training must also include aspects related to risk management, opportunity identification, and mitigation of negative effects.
Encourage a proactive and open-minded attitude towards change: Administrations must actively work to encourage a proactive and open attitude towards change. This can be achieved through internal communication campaigns that emphasize the benefits of innovation and showcase success stories, as well as through sharing innovation stories and best practices within the organization. A leadership that actively supports change and innovation is crucial to creating an environment that encourages staff to be proactive and experiment with new ideas.
Promote the adoption of Design Thinking techniques: Design thinking is a creative and user-centered approach that can help public administrations solve complex problems. Integrating design thinking into decision-making processes allows new ideas to be explored, tested quickly, and adapted based on feedback received. This approach keeps the focus on citizens' needs and finds innovative solutions that improve the quality of public services.
Risk assessment and management of uncertainties: Innovation inevitably involves risks. Therefore, it is crucial to implement risk management practices that allow for identifying, evaluating, and mitigating the risks associated with adopting new technologies. Public administrations must develop methodologies to assess uncertainties and make informed decisions that balance opportunities and risks, ensuring that adopted innovations are sustainable and do not jeopardize citizens' safety or trust.
Leadership that supports change: Promoting a culture of innovation and calculated risk requires visionary leadership willing to support change. Leaders must be the first to demonstrate openness to innovation, creating an environment that not only accepts but encourages reasoned risk. This type of leadership is essential to overcome internal resistance and motivate staff to engage in digital transformation projects.
These actions aim to develop a public sector culture that is oriented towards continuous improvement, learning from mistakes, and experimentation. Only by creating an environment where calculated risk is considered an integral part of the innovation process will it be possible to fully harness the potential of AI and other digital technologies to improve public services and meet the ever-changing needs of citizens.
Conclusions
AI governance in the public sector is not just a matter of technical or regulatory skills but represents a profound cultural and strategic change. In this transition, the public sector faces a crucial challenge: adopting AI not only as a technological tool but as a catalyst for rethinking how the state interacts with citizens and responds to their needs. A public organization’s ability to leverage AI depends not only on financial resources or adequate regulations but above all on a clear and shared vision that sees technology as an opportunity to build trust, equity, and innovation.
The greatest risk for the public sector is not the improper adoption of AI, but the failure to adopt the cultural transformation necessary to make it a tool for social progress. AI, with its ability to automate complex processes and analyze massive amounts of data, can improve the efficiency of public services, but without inclusive governance, it risks creating an even greater gap between institutions and citizens. The most vulnerable communities could be excluded from these benefits, not due to a lack of adequate technologies, but because of systems that do not consider everyone’s needs. This is where ethical governance becomes the real strategic pillar: not as a constraint but as a lever to ensure that AI serves the public interest.
Another fundamental aspect is the value of experimentation. The creation of regulatory sandboxes, which is much discussed, should not be seen merely as a protected environment to test technologies but as a symbol of the new attitude that the public sector must adopt. These spaces allow failure to become learning, a concept that radically challenges the traditional risk aversion typical of public bureaucracy. Organizations that manage to cultivate a culture of calculated risk will become examples of how AI can not only be implemented but also continuously improved in response to citizens’ needs.
The skills required by AI go far beyond technology. Certainly, the public sector needs experts in machine learning or data science, but the true engine of transformation will be the ability to integrate these skills with visionary leadership and strong ethics. Leadership in this context does not mean merely being able to make technological decisions, but above all being able to communicate an inclusive and future-oriented vision. This leadership must be capable of navigating the complexities of regulations, citizen expectations, and partnerships with the private sector.
Public-private partnerships represent another strategic turning point. However, the public sector must not settle for being a "customer" of the private sector. It must become an active partner, capable of negotiating solutions that respect public values and are transparent in their implementation. This collaboration must go beyond simple technological supply: the public sector has a duty to lead the dialogue on how AI should be designed, implemented, and monitored to ensure equitable benefits.
Finally, the real transformation will be measured not only by operational improvements but by AI's ability to strengthen the social contract between the state and citizens. AI can become a tool to make institutions more transparent and accountable, but only if citizens are actively involved in its design and monitoring. Trust will be the true success indicator: not blind trust in technology, but trust built on open processes, tangible results, and a visible commitment to the common good.
This reflection highlights that AI adoption in the public sector is not just a matter of how to do it but of why to do it. The risk is not technological but strategic: missing the opportunity to make AI an ally of social progress rather than a mere machine at the service of efficiency. The decisions made today will not only determine the effectiveness of public services but will define the role of institutions in an increasingly digital and interconnected society.
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