* [Topics](/en/topics "Topics") * [Artificial intelligence](/en/topics/ai "Artificial intelligence") * What is AI in the public sector? What is AI in the public sector? ================================ Updated April 15, 2026•*5*-minute read Copy URL Jump to section --------------- OverviewBenefits of AIKey challengesHow Red Hat can help Overview -------- Government agencies worldwide are witnessing a rise in the adoption of artificial intelligence (AI) and [machine learning](/en/topics/ai/what-is-machine-learning) to solve critical challenges in public service delivery, simplifying complex, time-intensive, and costly processes. The development and application of AI as a tool to support the services required to meet the public’s needs—[from data management and analytics to operational support—](/en/topics/ai/ai-ml-use-cases)play a significant role in driving public sector transformation and modernization. As agencies discover new ways to utilize AI across departments, two primary AI applications are emerging in public sector agencies: predictive AI, which uses historical data to forecast future events and trends to mitigate risk, and [generative AI](/en/topics/ai/what-is-generative-ai), which creates, translates, or modifies content by learning from extensive datasets. As a result, artificial intelligence will streamline and improve the accuracy of customer claims processes, aid in fraud detection and prevention, reduce manual workloads, and provide better data forecasting. [Discover how to integrate Red Hat AI into your organization](/en/products/ai) What are the benefits of AI in the public sector? ------------------------------------------------- Advancements in AI can drastically improve citizen experiences, transforming how citizens interact with government services and providing a more seamless experience. Policy makers and other public administrators can deliver more effective services and better allocate government resources to their constituents. Agencies looking to AI to help them scale public service delivery gain several benefits with its implementation, including culling data from multiple sources to better manage existing claims, and acquiring and distributing the most up-to-date information to aid in the prediction, identification, and prevention of fraud. An improved data distribution process allows administrators to prioritize and verify claims efficiently, thereby streamlining the overall claims process. This helps to improve the accuracy and speed of information communicated to claimants, policymakers, and politicians. The collation of data into public sector algorithms can also help government agencies predict the needs of their citizens and give public sector administrators an increased ability to manage and improve service availability. ### Main advantages of AI Here are some key benefits of AI use in the public sector for citizens, administrators, and policymakers. **Better service experience** Data insights processed by AI algorithms and real-time predictive analytics can enhance overall service delivery and user experience; citizens can get the answers they need when they need them from the right service, leading to better outcomes and a reduction in wasted resources. For example, Eusko Jaurlaritzaren Informatika Elkartea (EJIE), Spain’s IT department located in its Basque region, [used Red Hat® technologies to provide AI-supported digital services](/en/about/press-releases/spanish-regional-government-develops-basque-translator-powered-ai-red-hat) to its citizens[.](https://www.redhat.com/en/about/press-releases/spanish-regional-government-develops-basque-translator-powered-ai-red-hat) The Basque government wanted to support its citizens by providing services in their chosen language. Using AI, the IT team developed language tools within the framework of the [Itzuli project](https://www.euskadi.eus/itzuli/) to enable the translation and synthesis of text-to-speech from Basque into Spanish, French, and English and the transcription of speech-to-text in Basque and Spanish. **Improved claims processing** Handling benefit claims and payments can consume thousands of agency work hours; manual processing can increase the risk of human error, negatively impacting both citizens and agency efficiency. Introducing AI into workflows can automate claim filing and provide data-driven recommendations, expediting the claims process and enhancing employee and citizen experiences. **Mitigation of fraud, waste, and abuse** [Robotic process automation (RPA)](/en/topics/automation/what-is-robotic-process-automation) rapidly analyzes documents with speed and accuracy compared to manual methods. The AI tool can effectively flag fraudulent activity and waste, leading to more efficient use of government resources and funds. With continuous algorithm improvement, the system becomes more adept at detecting fraud, providing scalable protection for citizens and agencies alike. **Broadening access to public sector offerings** The use of AI-assisted guidance can expand the availability and access of services to citizens. Employing AI to validate and process claims enables more administrators to manage benefit claims, reducing the overreliance of agencies on a small handful of specialists and facilitating faster claims processing. **Expediting policy development** Policy development involves many stakeholders and a complex consideration of factors that can impact citizens. Computational AI tools can accelerate the process by potentially replacing trial-and-error methods with more efficient models to support policy creation and review, reducing legal and technical challenges and overall costs. [Build a production-ready AI/ML environment](/en/resources/building-production-ready-ai-environment-ebook "e-book | Top considerations for building a production-ready AI/ML environment") Red Hat resources ----------------- [Keep reading](/en/resources "Keep reading") Key challenges in operationalizing AI/ML ---------------------------------------- Despite the advantages of AI in the public sector, its implementation presents a set of challenges for public sector agencies. **Managing data collection and analysis** Government-powered AI solutions rely on large, real-time datasets for effective training while also needing to protect personally identifiable information (PII). Public sector workflows typically rely on manual processes and maintain rigid structures and hierarchies.. This highlights a challenge for many departments integrating new data acquisition procedures and technologies into existing workflows. Additionally, citizen data is siloed and fragmented across multiple networks; in some instances, the data is still in paper form, making it difficult to centralize it into a single database. **Addressing stakeholder needs** Creating alignment among multiple stakeholders is critical for successful AI/ML implementation and adoption. This includes citizens, data scientists, IT departments, operation teams, public sector administrators, policymakers, [and vendors, including independent software vendors (ISVs)](/en/topics/digital-transformation/isv-partners). Building consensus among all stakeholders reduces friction and empowers organizational decision-making around AI/ML enablement and use cases. Many private sector and telco organizations have established AI Centers of Excellence to optimize AI workstreams. **Addressing privacy concerns** Data is a vital asset for many organizations. To effectively train government AI tools using large datasets, especially those containing PII, agencies must adhere to [GDPR compliance regulations](https://gdpr.eu/) and the [EU AI Act](https://artificialintelligenceact.eu/), which reinforces citizen privacy rights under GDPR. Access to data is only granted on a legal basis or a claim. **Handling regional policy challenges** Traditionally, Europe has taken a more precautionary approach to AI use in comparison to the United States and China, with a more stringent regulatory environment. EU laws typically consist of a collective of layered policies like GDPR, the AI Act, the Data Act, DSA, and DMA. Each of the 27 EU member states also has its own policies, presenting compliance expectation challenges to non-EU countries doing business across borders. **Maximizing optimization and efficiency** Maximizing optimization and efficiency is easier when your moving pieces are working together. It means deploying your AI workloads at scale requires less resources and more time and energy spent elsewhere. A large factor that impacts your AI efficiency is your inference server and how it supports larger AI models and complex inference requirements. These AI tools use resources more efficiently to scale faster: * [**llm-d**](/en/topics/ai/what-is-llm-d): LLM prompts can be complex and nonuniform. They typically require extensive computational resources and storage to process large amounts of data. An open source AI framework like llm-d allows developers to use techniques like distributed inference to support the increasing demands of sophisticated and larger resoning models like LLMs. * [**Distributed inference**](/en/topics/ai/what-is-distributed-inference): Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices. Think of it as the software equivalent of the saying, “many hands make light work.” * [**vLLM**](/en/topics/ai/what-is-vllm): vLLM, which stands for virtual large language model, is a library of open source code maintained by the vLLM community. It helps large language models (LLMs) perform calculations more efficiently and at scale. Find out how Red Hat AI incorporates these tools and capabilities to help customers use AI at scale. [Explore Red Hat AI](/en/products/ai) How Red Hat can help -------------------- [Red Hat® AI](/en/products/ai) is built for fast, flexible, and efficient inference through its [vLLM-powered](/en/topics/ai/what-is-vllm) server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our products give you full control of AI workflows from end-to-end at any scale. The Red Hat AI portfolio includes [Red Hat AI Enterprise](/en/products/ai/enterprise), a platform for deploying, managing, and scaling AI inference, agentic AI workflows, and AI-powered applications on any infrastructure. [Explore Red Hat AI](/en/products/ai) Success story Government of Ireland automates compliance and security | Red Hat ----------------------------------------------------------------- The Government of Ireland partnered with Red Hat to create SmartText, a machine learning platform that achieves compliance and security goals [Read the success story](/en/success-stories/government-of-ireland "Government of Ireland automates compliance and security | Red Hat") Red Hat Helps AGESIC Scale AI Innovation Across Uruguay ------------------------------------------------------- AGESIC, the agency that leads the e-government strategy and its implementation in Uruguay, has adopted Red Hat OpenShift AI to extend AI platform capabilities and democratize standards and processes for the use of AI across Uruguayan government agencies. [Read the press release](/en/about/press-releases/red-hat-helps-agesic-scale-ai-innovation-across-uruguay "Red Hat Helps AGESIC Scale AI Innovation Across Uruguay") Keep reading ------------ ### AIOps explained AIOps (AI for IT operations) is an approach to automating IT operations with machine learning and other advanced AI techniques. [Read the article](/en/topics/ai/what-is-aiops "article | AIOps explained") ### What is parameter-efficient fine-tuning (PEFT)? PEFT is a set of techniques that adjusts only a portion of parameters within an LLM to save resources. [Read the article](/en/topics/ai/what-is-peft "What is parameter-efficient fine-tuning (PEFT)?") ### What is AI in healthcare? Discover the benefits and challenges of AI in healthcare and how Red Hat is helping the industry. [Read the article](/en/topics/ai/what-is-ai-in-healthcare "article | What is AI in healthcare?") 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