Artificial Intelligence in Public Administration: Strategy, Benefits & Examples
Seize opportunities – shape the future with confidence
The book provides a well-founded overview of the current state of technological development and the future prospects of artificial intelligence (AI).
Why use AI in public administration? Potential and current situation.
Public administration faces fundamental challenges: Demographic change, skills shortages and the growing complexity of political, social and technological demands call for new solutions. At the same time, the volume of available data is growing rapidly, often faster than it can be analysed.
Artificial intelligence (AI) offers valuable support in this regard. It can speed up processes, reduce the burden on administrative staff and enable data-driven decisions, not as a substitute for human judgement, but as a complement and a tool for a modern, resilient public administration.
In practice, this means:
Automation of routine tasks
AI can assist with application processing, data capture and minute-taking, freeing up time for more complex tasks.
Making documents readable
With AI-enabled text recognition, information can be extracted from unstructured data such as PDFs or scans and made usable in digital form.
Digital support for citizens
Chatbots and assistants provide guidance on standard questions and simplify form completion or appointment booking – around the clock.
Informing decisions
Forecasting models and data analysis help you identify trends, prepare briefings and support evidence-based management, from transport planning to forecasts of school places.
Examples such as the AI solution Assistent.iQ, which, among other things, summarises texts and supports research, or the analysis platform PLAIN show: AI has long been part of public administration. And it can significantly contribute to making it future-proof.
Public administration in the AI ecosystem: roles and responsibilities
The public sector is not just a user of AI. It takes on several roles in the digital transformation:
- User of intelligent systems in day-to-day work
- Client for AI solutions with high standards of data protection and ethics
- Operator of digital infrastructures such as the platform PLAIN
- Co-designer of standards, interfaces and governance structures
Fulfilling this role requires cooperation: with academia, specialist IT service providers and civil society. At the same time, the administration must further develop its own governance capability to achieve digital sovereignty worthy of the name.
AI in public administration: How to get started
Step 1: Data collection
A great deal of administrative data is still stored in paper archives or in unstructured formats. The first step is therefore systematic capture and digitisation. Tools such as Optical Character Recognition (OCR) convert scanned text into machine-readable information, including context classification. This turns records into knowledge.
Step 2: Make data discoverable
Data is only useful if it can be found. In the federal administration in particular, much information is distributed across different departments. Metadata catalogues and data overviews are helpful, as prototypically implemented in the project “Datenatlas”, including the legal framework and points of contact.
Step 3: Prepare data
‘Garbage in, garbage out’ applies to AI as well. Data must be cleaned, standardised and enriched so that it functions reliably in AI systems. This includes, for example, deduplication, enrichment with metadata and the merging of different sources.
Step 4: Put data to use and deploy AI
Once the data foundation is sound, AI can be put into use. The platform PLAIN shows how this can be done: There, ministries analyse large datasets with AI support to take well-founded policy decisions – confidently, at scale and across departments.
Tip: Those who consider these four steps at an early stage lay the groundwork for viable, interoperable AI projects, including in a federal context.
AI applications in public administration: Examples and benefits
An increasing number of public authorities are already using AI today, with clear added value:
- AI solutions such as Assistent.IQ assist with research, drafting and text summarisation.
- Chatbots help handle citizen enquiries, e.g. in civil registration.
- OCR-based systems automatically recognise, structure and classify scanned documents.
These examples show that AI is no longer just a future topic, and it is being integrated into administrative processes step by step.
Digital sovereignty and infrastructure issues
Anyone wishing to deploy AI in public administration should rely on secure, independent and scalable infrastructures. This means:
- Open-source technologies rather than proprietary black boxes
- European models rather than global dependencies
- Sovereign hosting within reliable legal jurisdictions
This also includes interoperable data spaces and federal platforms, where AI applications can be run in compliance with data protection and common standards.
Examples include:
- The platform PLAIN enables AI-supported data analysis in the federal administration.
- The AI Competence Centre (KI-KC) promotes the transfer of AI innovations into public administration.
- Initiatives such as GAIA-X show how federated data infrastructures can be developed.
Data policy as the foundation of the AI strategy
Artificial intelligence needs data, in quality, structure and context. Public administration holds vast volumes of data. But not all of it is immediately suitable for AI. Much of it is unstructured, spread across specialist systems, or difficult to access due to data protection requirements.
This is where data policy comes in: It determines whether existing information is converted into valuable insights or simply remains as volumes of data that no system can use effectively.
Data provision as a strategic responsibility
Data management is no longer a secondary technical issue, but a strategic management issue. Anyone wishing to deploy AI needs to know:
- What data exists?
- Who has access?
- In what form is it available?
Initiatives such as the Data Atlas of the Federal Administration help to create transparency about data sources and to identify data-use potential in a targeted way.
Data provision as a strategic responsibility
Data policy must also be ethically and legally sound: Not everything that is technically feasible is socially legitimate or legally compliant. That is why linking data strategy and data ethics is crucial, for example when deciding how personal data may be processed or how to prevent bias in datasets.
In practice, this means:
- Taking data thriftiness and purpose limitation into account
- Taking responsibility for data quality
- Creating transparent procedures, for example through documentation and audit processes
Further reading:
FAIR principles: A foundation for machine-readable public administration
Public administration is increasingly guided by the FAIR principles, which are intended to ensure that data is not only readable and usable for people, but also for machines:
- Findable
- Accessible
- Interoperable
- Reusable
These principles help to systematically unlock data, provide it in a standardised way and exchange it efficiently between authorities, platforms or applications – a key prerequisite for scalable AI projects.
One frequently overlooked aspect is that public administration is not only a data steward, but also a data producer. Every administrative procedure generates structured information – for example on applications, decisions, contacts, approvals or inspections. This data is a valuable resource for AI-supported automation, analysis and forecasting.
To realise this potential, you need:
- Systematic capture of structured data
- Uniform interfaces and data models between processes
- The political will to view public administration as a knowledge organisation rather than merely a data repository
Only when information is systematically captured, made accessible and properly prepared does it form the foundation for powerful, trustworthy AI in public administration.
Capability building and governance structures
Using AI effectively requires more than technology: People, processes and structures are key.
Three levels are particularly important:
- Competence centres such as the AI Competence Centre (KI-KC) create space for pilot projects, exchange and scaling.
- Governance: Clear responsibilities, role definitions and review mechanisms must be established.
- Qualification: Staff need digital and ethical competences to understand and use AI solutions.
Tip: A practical approach is provided by the ‘five-step model’ for introducing AI, from data capture and preparation through to use.
Values-driven use of AI: Guidelines, ethics and responsibility
The use of AI must always be human-centred, non-discriminatory and transparent. This is essential not only legally but also socially. The AI guidelines of the Federal Ministry of the Interior (BMI) provide important guidance for public administration.
What this means:
- AI systems must be comprehensible; think explainable AI.
- Clear responsibilities are needed, including for automated decisions.
- Public administration plays a central role as a trust-building institution in the digital state.
Recommended article: Content Credentials and trustworthy AI (german)
Overview: Scaling and future outlook
AI has long been part of public administration, pilot projects, platforms and individual applications. However, widespread, sustainable deployment is still to come. The path to a learning, resilient administration requires:
- Scalable infrastructures that include municipalities, federal states and the federal government
- Open standards that make systems interoperable
- An administrative culture that sees technological innovation as an opportunity
If these conditions are met, AI can be far more than a means of boosting efficiency. It becomes a strategic instrument – for better decisions, closer engagement with citizens and an ever-evolving digital state.
What next? Three pointers for scaling AI
1. Evolve platforms
Use existing structures such as PLAIN or the AI Competence Centre (KI-KC) as a base and develop them further so that federal states and municipalities can plug in.
2. Create standards
Invest in open interfaces, metadata models and data spaces usable across all levels of government, to avoid siloed solutions.
3. Make innovation routine
Strengthen your organisation’s readiness to test new approaches, for example through pilot projects, iterative methods and a clear vision for AI use.
Frequently asked questions about AI in administration
AI can be used in many areas of public administration to make processes more efficient and more citizen-friendly. Here are some examples:
- Citizen communication: Chatbots and virtual assistants answer simple enquiries around the clock.
- Document processing: AI can automatically review applications and documents, classify them and extract data.
- Data Analysis: By analysing large datasets, AI can detect patterns, which helps with transport planning or resource allocation.
- Proactive services: AI systems can proactively inform citizens about relevant services.
Using AI in public administration offers several key benefits:
- Increased efficiency and speed: Routine tasks are automated, which shortens processing times for applications and reduces the burden on staff.
- Better service quality for citizens: They receive faster responses and can access services around the clock.
- Improved decision-making: Data analysis allows administrative processes and strategies to be better planned and optimised.
- Reducing the burden on staff: Administrative staff have more time for complex tasks and direct contact with citizens, as they are no longer tied up with monotonous routine work.
Yes, as a rule, specialised IT infrastructures are necessary. AI applications often require substantial computing power, especially for training and running complex models. This means that, in many cases, high-performance servers (e.g. with specialised graphics processors), cloud solutions or a modern data platform are required. A well-structured data foundation is the most important prerequisite for the successful use of AI.
Compliance with legal requirements is of paramount importance when using AI in public administration:
- Data protection (GDPR): The handling of personal data must comply with the strict provisions of the General Data Protection Regulation. This includes data subjects’ consent, purpose limitation and data security.
- Transparency and explainability: Decisions made by AI systems must be transparent and understandable to citizens. If a decision is based on AI, it must be possible to explain how the outcome was reached.
- Liability: The issue of responsibility is complex. As a rule, the public authority remains liable for decisions, even when they are supported by an AI system.
- AI Act (EU AI Act): In future, the European AI Act will set further binding rules for the use of AI, particularly regarding risk classifications and safety requirements.
AI is not intended to replace staff, but instead to support and transform their work. AI creates various opportunities:
- Avoidance of monotonous tasks: Staff are relieved of routine work and can focus on more demanding, creative and complex activities.
- Skills development: Working with new technologies and interpreting AI-supported analyses requires new competences that upskill staff.
- More time for people: The time freed up can be used for personal advice to citizens and for solving complex individual cases.
Interaction between citizens and AI systems generally takes place via digital interfaces:
- Chatbots and voice assistants: Citizens can express their requests directly in natural language to obtain information or initiate a process.
- Online portals: AI-supported forms can guide citizens through completion and automatically flag missing information or errors.
- Personalised recommendations: In future, citizens can be proactively informed about relevant government services tailored to their specific situation.
Informing citizens is a key factor in building trust in public administration. This can be done in the following ways:
- Transparency on websites: Clear, accessible explanations on official public administration websites stating which AI systems are used and for what purposes.
- Notices in direct communications: Citizens should be clearly informed when they are interacting with an AI system (e.g. a chatbot).
- Citizen participation and dialogue: Involving the public in the development and introduction of AI systems can increase understanding and acceptance.