The AI framework for public administration: Which technology for which task?
Why is an AI framework important for public administration?
Public administration is facing major challenges: Rising expectations of efficiency, transparency, and service quality are met with a shortage of skilled workers and complex procedures. Digitalisation is therefore indispensable. Artificial intelligence (AI) can help to simplify processes, relieve pressure on resources, and improve service quality.
For this to work, AI must be used in a targeted way. It is important to choose the right technology for each specific task and integrate it meaningfully into existing processes. This article shows how this can be achieved and which AI technologies are particularly suited to day-to-day administrative work.
What is an AI framework for public administration?
An AI framework is a methodological framework that helps systematically assign different AI technologies to the right administrative tasks, for example in application submission, preliminary checks, or evaluation.
What challenges does public administration face?
Public administration bodies in Germany and worldwide are facing a wide range of challenges that increase the need for more efficient and citizen-friendly solutions.
The solution: Use AI technologies in a targeted way
Integrating AI into public administration offers enormous potential for making workflows more efficient. The challenge lies in the targeted selection and allocation of the right AI technology to the respective tasks and processes.
AI technologies in public administration
Essentially, AI technologies can be used to automate processes, support decision-making, and improve communication with citizens. Particularly promising are:
Robotic Process Automation (RPA)
Automates recurring, rule-based tasks and is ideally suited to formal checks or the automatic routing of applications.
Large Language Models (LLMs)
Understand and generate natural language – for example for virtual assistants, text analysis, or drafting decision templates.
Machine Learning (ML)
Analyses large volumes of data, identifies patterns, and produces forecasts – for example on processing times or for detecting irregularities.
The success of AI in public administration is crucially dependent on how well the technology is tailored to the respective requirements. A fundamental question here is: Which tasks should the AI take on, and which technologies are best suited to them? This allows AI applications to be targeted at processes that offer a clearly defined output and a structured data basis.
Basis for the AI framework: a modular perspective
Modularity is a key success factor for the successful use of AI in public administration. Instead of developing isolated end-to-end solutions for entire procedures, administrative processes should be broken down into recurring components, such as application submission, preliminary checks, and subject-matter assessment.
These building blocks can be supported in a targeted way using different AI technologies: Large Language Models (LLMs) make application assistance and communication easier, while Robotic Process Automation (RPA) automates rule-based checks. This creates a scalable and transferable system that add efficiency gains across functional boundaries and modernises administrative procedures in the long term.
The AI framework: Systematisation of technologies
In public administration, primarily ‘weak’ AI is used. These are systems that are specialised in clearly defined tasks, such as text recognition, data analysis, or predictive modelling. Their goal is not to replicate human intelligence, but to support specific work steps.
Central to this is the systematic categorisation of AI applications according to their functional purpose. The AI framework distinguishes between four task types, which are differentiated by the degree of human involvement and the desired outcome.
- Inform: AI processes information, summarises content, or provides relevant data in context, for example for reports or knowledge bases.
- Interpret: AI analyses data, identifies patterns or checks correlations, for example when running plausibility checks on applications or detecting anomalies.
- Advise: AI supports decision-making processes by deriving recommendations for action, for example when prioritising cases or selecting suitable procedures.
- Act: AI independently carries out clearly defined, rule-based steps, such as formal preliminary checks or automated data transfer.
This classification enables the targeted allocation of technologies to tasks, regardless of specific products or systems. It forms the basis for methodically analysing administrative procedures and integrating suitable AI solutions efficiently and in a legally compliant way.
Matching: Which technology for which task?
For AI applications to have an impact in public administration, they must be technically feasible and make sense from a functional perspective. The key is to find the right match between the administrative task at hand and the technology used.
There are two key criteria here:
Objective of the component = output of the AI technology.
The chosen technology must effectively support the desired work outcome – such as structured application entry or a reliable forecast of processing time.
Available data = required data.
AI can only work as well as the available data allows. Quality, structure, and access options are therefore crucial to success.
This principle makes it clear: Not every technology is suitable for every task, and vice versa. AI delivers the greatest added value where processes are clearly defined and sufficient data is available.
One example is the parental allowance procedure: It combines typical administrative components – such as application submission, preliminary checks, and payment – and illustrates how different AI technologies can work together to enable an end-to-end digital process.
Future vision: AI in practice – the parental allowance example
The parental allowance procedure provides a very clear example of the future use of AI in public administration. It combines almost all typical components of an administrative process, from application submission and preliminary checks through to payment. This makes it very suitable for demonstrating the potential added value of different AI technologies when used together.
The procedure is currently digitally accessible in many federal states, but in practice it is still fraught with media discontinuities, manual data entry, and long processing times. Here, AI can help to simplify processes, improve data quality, and provide noticeable relief for staff.
What such a procedure could look like in future:
When combined, they create an end-to-end digitalised process that makes application submission significantly easier for citizens and help public administration to become a proactive, data-driven service provider.
Frequently asked questions about AI frameworks for public administration
Because not every AI technology is suitable for every task. The framework helps identify potential and limitations, and ensures that AI is used in a targeted, efficient, and legally compliant manner.
Key technologies include Robotic Process Automation (RPA), Large Language Models (LLMs) and Machine Learning (ML). They automate, structure, or analyse administrative processes.
It creates a consistent basis for implementing AI projects in a planned, scalable, and comparable way, from pilot projects through to comprehensive applications.
An adequate data basis, standardised interfaces, clear responsibilities, and trained staff are prerequisites for using AI. In addition, data protection, IT security, and traceability must be ensured.
Selection is based on two criteria:
- Objective of the component = output of the AI technology
- Available data = required data
This ensures that AI solutions are both professionally and technically appropriate.
In standardised, data-driven processes such as application submission, formal preliminary checks, or report drafting, efficiency can be increased quickly and visibly.
It makes it possible to break procedures down into recurring building blocks and enhance them in a targeted way with suitable technologies. This increases scalability and reusability.
Es zeigt, wie verschiedene KI-Technologien – LLMs, RPA und ML – über mehrere Prozessschritte hinweg zusammenspielen können, um einen vollständigen digitalen Ablauf zu schaffen.
Through transparency, human oversight (“human in the loop”), clear documentation of the decision logic, and compliance with legal requirements such as the EU AI Act.
