Turning Potential into Progress: Taking AI from Pilots to Practice in Public Services

One of the big challenges of our time is that on one hand there is urgent need for digitalisation and automation in the judiciary and public administration. On the other hand, are AI techniques quickly evolving and the risk factor of wrongful or misguided outputs is high. It is quite likely that this situation will further evolve and continue.

The question is whether we need not to get started with putting AI in place and evolve the applications with the future development of AI technologies. After all there is so much, we could do already without taking any risk on transparency or bias before even getting to a level where more complex AI applications come into play.

By taking action now, we move beyond theoretical discussions and create the essential foundation on which digital transformation can thrive. This article makes the case for advancing from pilots to practice in AI applications, highlighting the practical benefits of real-life implementations and offering guidance on how to achieve sustainable, transparent progress in the judiciary and public administration. The time has come to turn potential into progress and make AI a trusted partner in public service.

Introduction: The Need for Action

During the last years there has been tremendous progress in AI technologies related to text and reasoning related tasks, that will obviously enhance digitalisation and automation in the legal domain. Quite a number of use cases for the judiciary and public sector administration have been made. Yet, despite encouraging pilot projects, many promising AI applications remain confined to the experimental stage, largely due to concerns about risks and ethical implications. While these discussions are of essential for ensuring responsible use of technology, they have at times, overshadowed a critical truth: to fully realize AI's benefits and support the digitalization of legal tasks, we need to start implementing practical, real-life applications today.

Moving forward with real life implementation does not mean taking any significant risks. Digitalisation and automation do not start with the implementation of any high risk AI technology. Obviously there needs to be groundwork first. Such basic foundation needs a sobre strategic approach as to data structures and workflow analysis.

Why There Is No Time to Lose

The demand for efficiency, accuracy, and streamlined service delivery in the judiciary and public administration is growing rapidly. With rising caseloads, staff shortages, and increasing public expectations for responsive services, the need for digital solutions has never been more urgent..

Moreover, every day spent in pilot stages without full-scale implementation represents a missed chance to build the foundational data structures and standards necessary for long-term digitalization. As technology evolves, the judiciary and public administration must keep pace to ensure they are ready to integrate more advanced tools in the future.

Safe, secure and reliable as well as transparent AI applications can be build with traditional AI tools that employ NIP, ML and rule based technologies. While traditional AI applications should be the focus today, it’s important to recognize that more complex technologies—such as Large Language Models (LLMs) and advanced autonomous AI—will further evolve. These technologies will offer powerful potential for understanding nuanced language and complex decision making processes. However, their adoption must be approached cautiously, with a strong emphasis on transparency, interpretability, and safety. By waiting until these technologies can be applied responsibly and in alignment with ethical and legal standards, institutions can ensure that they are integrated in a way that enhances, rather than disrupts, public trust.

By then public institutions will already have a solid foundation to support these advanced technologies. The structured data, established workflows, and AI-literate workforce developed through initial real-life applications will serve as invaluable assets. This groundwork will allow public institutions to leverage LLMs and other advanced AI systems effectively, seamlessly building on prior achievements to create a robust, adaptable, and forward-looking digital infrastructure.

Laying the Foundation for Digital Transformation in Legal Tasks

For the judiciary and public administration to truly harness the potential of AI, it is essential to establish a foundation that supports long-term digital transformation. This needs a strategic and planned approach. By starting with low-risk AI applications in routine tasks, public institutions can achieve both immediate operational gains and build the essential data structures needed for more advanced applications in the future.

Routine Tasks as Building Blocks for Digitalization

Routine administrative and legal tasks, such as document processing, case filing, data extraction and entry, as well as routine process automation in the legal workflows are repetitive, time-consuming, and prone to human error. These tasks are ideal candidates for traditional AI applications like Natural Language Processing (NLP), Machine Learning (ML), and rule-based systems, which can reliably handle repetitive workflows while freeing up personnel to focus on more complex issues.

Automating these tasks through AI not only improves efficiency but also creates a consistent, high-quality output that is crucial for future digital applications. For example, NLP applications can categorize documents, extract key information, and organize case data in a standardized format. These structured data outputs serve as valuable building blocks that enable the future deployment of predictive analytics, automated decision support, and other advanced AI applications.

Building High-Quality Data Structures

One of the most critical outcomes of starting with real-life AI applications is the development of structured data ecosystems. Each AI-driven process generates and organizes data in a consistent, interoperable format, establishing a foundation that future applications can build upon. Knowledge graphs, for example, are highly effective tools for connecting related data points across cases, regulations, and jurisdictions, creating an interconnected data framework that enhances information retrieval and decision-making.

As more routine tasks become automated, the resulting high-quality data can be fed into more sophisticated models, such as those used for case prioritization or legal precedent analysis. This structured data not only supports immediate needs but also sets the stage for innovation, making it easier to implement more advanced AI applications that require clean, organized datasets to operate effectively.

Strengthening Data Governance and Standards

In addition to improving efficiency, these initial AI applications create an opportunity to establish clear standards for data governance. Consistency in data handling—across workflows, departments, and jurisdictions—ensures that AI systems can access and interpret data accurately. Setting these data governance standards early on is essential for scalability and interoperability, as it allows future AI applications to be integrated without encountering data compatibility issues.

Developing robust data standards also contributes to public sector transparency, as clear governance policies reinforce accountability and ethical compliance. By focusing on structured data collection and standardized processing, public institutions can build a strong foundation for responsible digital transformation, ensuring that AI systems are used safely, ethically, and effectively.

Enabling Future-Ready Digital Transformation

The initial implementation of AI in routine tasks is more than a quick win; it is a strategic move that paves the way for continued digital progress. Each real-life application adds value not only by improving current workflows but by contributing to a data-rich environment capable of supporting future AI technologies. As data structures grow and standards solidify, the judiciary and public administration are better positioned to adopt complex AI tools like predictive analytics, advanced decision support systems, and automated reasoning.

In summary, starting with low-risk AI applications in routine workflows is a practical and impactful first step toward digital transformation. This approach enables public institutions to realize immediate benefits while establishing a foundation for more advanced AI solutions. By laying these essential building blocks now, the judiciary and public administration can achieve a scalable, future-ready digital infrastructure that supports ongoing innovation in legal and administrative tasks.

Benefits of Moving from Pilots to Practice

Implementing AI in real-life applications within the judiciary and public administration can unlock significant benefits that extend far beyond efficiency gains. By transitioning from pilot projects to practical deployments, institutions can address immediate challenges while creating a solid foundation for future innovations.

Creating Immediate Operational Gains

One of the primary advantages of real-life AI applications is the immediate improvement in operational efficiency. Automating routine workflows, such as document processing and case prioritization, allows public institutions to reduce backlogs, minimize delays, and improve service quality. By handling repetitive tasks efficiently, AI frees up human resources to focus on more complex responsibilities, enhancing productivity across departments.

Establishing a Reliable Data Ecosystem for Future Applications

Moving from pilots to practice allows institutions to begin building a structured and reliable data ecosystem. Real-life AI applications generate standardized, high-quality data that can be organized and integrated across workflows, creating a foundation that supports future digital advancements. Each AI-driven process adds to this growing data framework, making it easier to deploy more complex applications in the future.

For example, data generated from NLP-based document categorization or rule-based decision-making can be used to support predictive analytics, advanced search capabilities, or automated reasoning. By establishing a well-organized data ecosystem early on, public institutions ensure they are prepared for more sophisticated AI tools that require clean, structured data to operate effectively.

One of the key steps in scaling AI is investing in a strong data infrastructure that can support both current and future applications. Ensuring data quality, consistency, and interoperability across departments is essential, as it enables AI systems to access reliable information and produce accurate results. This infrastructure includes data governance frameworks that outline standards for data collection, storage, and processing.

Institutions should also consider implementing centralized databases or knowledge graphs that organize data in structured, interconnected formats. This not only enhances data accessibility for current AI tools but also creates a solid foundation for predictive models, advanced analytics, and other complex applications that may be introduced in the future.

Increasing Judiciary and Public Sector Readiness and Confidence in AI

Full-scale AI implementations build valuable experience and confidence among staff and stakeholders, reducing hesitation and fostering acceptance of AI-driven innovations. By incorporating AI into real workflows, institutions give personnel the opportunity to work with and understand the technology, making them more comfortable with its potential and limitations.

This practical exposure to AI also enhances skill development within the workforce, enabling staff to manage, monitor, and optimize AI systems effectively. As confidence grows, public institutions are better prepared to explore more advanced AI applications, ensuring a smoother transition into future innovations.

Demonstrating Transparency and Accountability

Transparency is fundamental to public sector AI applications. Choosing traditional AI methods like rule-based systems and supervised ML models allows institutions to maintain clear, interpretable processes. These methods generate outputs based on explicitly defined rules or supervised learning, which can be reviewed and explained in detail. This transparency is critical for ensuring that AI implementations remain accountable and trusted by both staff and the public.

By using these applications in daily operations, public institutions can establish best practices for ethical AI use, laying the groundwork for more complex applications.

Transparent AI practices build public trust by showing that AI systems are being implemented responsibly, with clear safeguards in place. This transparency not only supports the success of current applications but also helps foster a positive environment for future AI projects, where public buy-in is essential for scaling digital transformation efforts.

To reinforce transparency, institutions should also establish clear guidelines for AI use, including routine audits and regular reporting on AI-driven decisions. Transparent practices build public confidence and ensure that AI applications meet ethical standards, making it easier to gain support for more advanced applications down the line.

Quick Wins and Proof of Concept for AI’s Value

Starting with real-life applications allows public institutions to achieve “quick wins” that demonstrate AI’s practical value. By delivering immediate results, such as faster processing times or improved accuracy, these applications serve as proof of concept for AI’s effectiveness in public services. These quick wins generate momentum and can lead to broader AI adoption across departments, encouraging further exploration and innovation.

Moreover, successful early applications can attract support from stakeholders, enabling institutions to secure funding, resources, and policy support for future AI projects. Demonstrating concrete outcomes in the present builds a strong case for continued investment in AI as a strategic tool for

Identifying Low-Risk, High-Impact Applications

To maximize AI’s effectiveness in the judiciary and judiciary and ?public sector, it’s essential to start with low-risk applications that offer significant, measurable benefits. Routine, repetitive tasks such as document processing, case classification, and data extraction are ideal candidates, as they are both operationally impactful and easy to monitor for accuracy. These applications deliver clear, immediate gains in efficiency and consistency without introducing undue complexity or ethical risks.

By targeting workflows that are high in volume and prone to human error, institutions can achieve quick wins that demonstrate AI’s value. This targeted approach builds confidence within the organization and helps pave the way for more advanced AI solutions in the future.

Integrating AI into Existing Workflows

Scaling AI effectively requires a thoughtful integration process that aligns AI tools with existing workflows. This means that AI should complement and enhance current operations rather than disrupt them. For example, NLP applications that categorize documents or extract information should be seamlessly embedded into case management systems, enabling staff to easily retrieve and apply AI-generated insights.

To ensure smooth integration, institutions can adopt phased approaches where AI applications are gradually introduced into workflows. This allows staff to adapt to the technology at a manageable pace and provides opportunities to address any issues that arise. A well-executed integration not only improves workflow efficiency but also fosters greater acceptance of AI within the organization.

Training and Skill Development for Staff

For AI to be successfully scaled, public institutions need a workforce that is knowledgeable and comfortable working with AI tools. Training programs are essential to equip staff with the skills to understand, monitor, and manage AI applications effectively. Training should cover both the technical aspects of using AI and the ethical, legal, and operational implications of AI in public services.

Building internal expertise also enables institutions to identify and troubleshoot potential issues, ensuring that AI implementations remain accurate and effective.

Fostering a Culture of Innovation and Adaptability

Successfully scaling AI in the public sector requires a culture that embraces innovation and adaptability. This involves encouraging departments to explore AI’s potential, experimenting with different applications, and learning from each implementation. By fostering an environment that is open to change, public institutions can drive continuous improvement, ensuring that AI remains an evolving tool for public service transformation.

Integrating AI with Legacy Systems

Many public sector institutions operate with legacy systems that may lack the compatibility or processing capacity required to support modern AI applications. Integrating AI with these systems can be challenging and may disrupt existing workflows if not carefully managed. . But this is an opportunity as well.

A phased integration strategy allows AI applications to interface with legacy systems incrementally. Middleware, such as APIs or data connectors, can help bridge the gap between AI tools and older infrastructure, facilitating data exchange without the need for complete overhauls. By planning integration steps thoughtfully, institutions can maintain workflow stability while gradually modernizing their system.

A Call to Action: Taking AI from Pilots to Practice in Public Services

The time has come for public institutions to move beyond experimental AI projects and bring real-life applications into daily workflows. By advancing from pilots to practical implementations, the judiciary and public administration can harness AI’s full potential to improve service quality, reduce operational burdens, and set the foundation for a digitally transformed future. This chapter outlines why now is the right moment to act and provides a roadmap for decision-makers and stakeholders to take AI forward in a responsible, impactful way.

Making the Shift from Pilots to Practice

AI pilot projects have demonstrated the technology’s potential, but without transitioning to real-life applications, institutions risk stagnating at the proof-of-concept stage. It’s crucial to begin integrating AI into core operations to achieve tangible benefits that address the immediate needs of public services, such as case backlogs, resource constraints, and workload pressures. Moving from pilots to practice not only allows institutions to reap the rewards of increased efficiency and accuracy but also builds the organizational knowledge needed to manage and optimize AI systems.

Focusing on Quick Wins and High-Impact Areas

To build momentum, public institutions should focus on quick wins—low-risk, high-impact applications that deliver immediate value. These could include automating document categorization, streamlining case prioritization, and enhancing data retrieval processes. Targeting these high-volume, routine tasks allows institutions to demonstrate AI’s practical benefits, making it easier to justify further AI adoption across departments.

Quick wins serve as valuable proof points, showing that AI can produce measurable improvements without overhauling existing systems. These early successes not only validate AI’s role in public services but also foster internal support and build trust among stakeholders.

One of the most important aspects of moving from pilots to real-life applications is the opportunity to create strong, structured data foundations. Implementing AI in daily workflows generates consistent, high-quality data that can be used to support future, more sophisticated AI applications. Knowledge graphs, centralized databases, and standardized data formats all contribute to a data-rich environment that will enable predictive analytics, advanced decision support, and automated reasoning as these technologies evolve.

Investing in data quality and governance now prepares institutions for the next phase of digital transformation, ensuring that future AI tools can operate on a reliable and scalable data infrastructure. This data-centric approach provides a lasting advantage, positioning public services to adapt seamlessly to ongoing technological developments.

Moving Forward with a Vision for Sustainable Progress

Taking AI from pilots to practice is about more than immediate improvements; it’s a strategic step toward a future-ready judiciary andjudiciary and ?public sector. By prioritizing real-life applications now, institutions not only address current needs but also create an infrastructure that supports ongoing AI advancements. Each successful application contributes to a digital ecosystem that is resilient, scalable, and adaptable to future challenges.

Conclusion

In conclusion, public institutions have an unparalleled opportunity to turn AI’s potential into progress. By acting now, they can enhance service quality, improve operational efficiency, and position themselves at the forefront of digital transformation