AI in government is no longer a hypothetical or early-stage experiment. Public institutions are moving from isolated pilot projects to large-scale and systemic adoption of AI across core government functions: from social services and healthcare to transportation, public safety, and administrative operations.
This shift reflects a broader digital transformation in which AI is becoming part of the underlying infrastructure that supports decision-making, service delivery, and policy design.
As adoption accelerates, however, governments face a new set of regulatory, ethical, and governance challenges that are more urgent and complex than before. Ensuring transparency in automated decisions, protecting sensitive public-sector data, and addressing algorithmic bias have become central priorities.
Explore AI in government applications, best practices to mitigate these challenges, and real-world examples.
Government services use cases
Tax administration
AI is used in tax administration to support fraud detection, improve compliance work, and strengthen services for taxpayers. Many administrations began with rules-based systems and now apply machine learning and language models to handle larger volumes of data and more complex patterns.
- AI helps identify tax evasion by analysing structured and unstructured data, including images and social media content.
- It supports administrative decision processes by sorting cases and routing them to appropriate staff.
- Risk assessment models use historical data, transaction records, and digital payments to assign risk scores.
- Virtual assistants and LLMs help taxpayers with routine questions and filing.
- Pre-population of tax returns and anomaly detection reduce filing errors.
Healthcare
Tracking disease spread: AI can be used to prevent it.
- Building a machine learning algorithm that cross-checks patients with similar symptoms from different locations, detects patterns, and warns when an outbreak might occur.
- Using graph analytics, as in the case of China during COVID-19, to identify contacts with a known carrier of the virus
Triaging patients has been used in hospitals’ emergency services, but it became necessary after the Coronavirus spread. AI-powered tools can analyze patient data to predict patients’ risk scores, enabling doctors to prioritize.
Handling citizens’ health-related queries: Public health was endangered by misinformation about pandemic measures, particularly at the beginning of the COVID-19 pandemic. For example, misinformation about COVID-19 in Canada resulted in at least 2,800 deaths and $300 million in hospital costs over a nine-month period during the pandemic.1
Conversational AI technologies can assist governments in informing their people and authorities in responding to frequently requested health-related queries.
Regulatory design and delivery
AI supports regulators in analysing legislation, drafting rules, assessing impacts, and monitoring compliance. It is also applied in inspections and economic regulation.
- AI can find gaps or overlaps in existing regulations by scanning large text collections.
- NLP tools assist drafting, cross-referencing, and the preparation of explanatory materials.
- AI can estimate compliance costs and help regulators understand how proposed rules may affect different groups.
- Simulation tools allow testing of policy scenarios before adoption.
- In delivery work, AI supports risk-based inspections, identification of non-compliance, and analysis of market behaviour.
Domestic security
Predicting a crime and recommending optimal police presence: AI can be used to identify patterns in policing heat maps to forecast where and when the next crimes are likely to occur (See figure below).
Though AI algorithms’ fairness in predictive policing is still questionable, and they don’t favor minority groups, AI-based recommendations can be used to identify optimal police patrol presence.
Figure 1: Oakland PD’s crime map for 90 days.2
Surveillance: AI surveillance describes the process of ML and DL-based algorithms analyzing images, videos, and data recorded from CCTV cameras.
Though techniques like facial recognition enable governments to identify people from video footage, the ethical implications of AI-powered surveillance remain controversial. For instance, IBM stopped offering, developing, or researching facial recognition technology for mass surveillance due to racial profiling and violations of basic human rights and freedoms.
Military
Autonomous drones: Autonomous military drones, also referred to as Unmanned combat aerial vehicles (UCAV), are military weapons that carry combat payloads, such as missiles, and are usually under real-time human control, with varying levels of autonomy.
One of the latest examples of military drones, though they were mostly piloted by humans, was used by Azerbaijan at Nagorno-Karabakh in the combat against Armenia.3
Transportation
Self-driving shuttles: Autonomous shuttles are a flexible solution for moving people at sub-50km/h along predetermined, learned paths such as industrial campuses, city centers, or suburban neighborhoods. Self-driving shuttle trial deployments are expected to accelerate quickly because:
- The shuttle segment is less regulated than the automotive market.
- Consumers’ trust in autonomous shuttles is higher than in other autonomous vehicles. According to a survey conducted by the University of Michigan, 86% of riders said they trusted shuttles after riding in them, as did 66% of non-riders.4
Monitoring social media to identify incidents: Traffic congestion is an issue for citizens and governments alike. Congestion mostly results from road accidents, negatively impacting travel times, fuel consumption, and carbon emissions. Artificial intelligence can be used to monitor social media to identify tweets about recent accidents.
Education
- Personalized education: ML algorithms can help provide personalized education irrespective of the number of students. AI can analyze students’ progress and find the gaps between what is taught and what is not yet understood.
- Marking exam papers: Automated text analysis reviews students’ work to identify strengths and recommend revisions.
Public procurement
AI is used to support planning, tendering, and contract management. It can classify spending, assist in evaluation, and identify irregularities.
- Spend analysis and classification tools help standardise procurement data.
- Workflow tools automate tasks in tender preparation and document review.
- Chatbots provide support to procurement staff and suppliers.
- AI detects anomalies in bidding patterns and contract data, supporting audits.
- Open data platforms with AI features enable civil society to review procurement activity.
Emergency
- Classifying emergency calls based on their urgency: Voice recognition technologies & ML algorithms can help governments automate emergency call lines by understanding and categorizing queries.
- Fire prediction: ML & DL algorithms can map forest dryness to predict wildfires better.
- For example, researchers at the University of Southern California (USC) have developed a model that integrates generative AI with satellite imagery to predict wildfire spread accurately.
- The research team analyzed historical wildfire data from high-resolution satellite images to identify patterns in ignition, spread, and containment based on weather, fuel types, and terrain.
- Using this data, they trained a generative AI model (cWGAN) to predict wildfire behavior, which accurately forecasted fire spread in California from 2020 to 2022.5
Public relations use cases
Customer service chatbots: Chatbots enable governments to perform a variety of tasks, including:
- Scheduling meetings.
- Answering FAQs.
- Directing requests to the appropriate area within the government.
- Filling out forms.
- Assisting with searching documents.
Corruption and public integrity use cases
Integrity institutions use AI to detect fraud, analyse networks, process documents, and anticipate corruption risks.
- Machine learning helps detect suspicious transactions and anomalies in procurement or spending data.
- Network analysis uncovers hidden links between entities that may signal conflicts of interest or collusion.
- NLP supports the review of large document collections and the extraction of relevant information.
- Generative AI assists with summarisation, translation, and public communication.
- Predictive tools help identify areas with elevated corruption risk.
What does artificial intelligence offer to governments?
Artificial intelligence provides governments with capabilities similar to those in the private sector, enhancing government operations across various domains. These offerings can be categorized into three key areas:
1. Savings due to operational efficiency
AI-driven automation helps government agencies optimize workflows, manage service delivery, and reduce administrative burdens. AI tools powered by machine learning techniques can process data sets more efficiently than traditional methods, leading to improved cost savings.
Federal agencies and local governments can leverage AI for fraud detection, personnel management, and code generation, ensuring more effective resource allocation.
2. New and improved services
AI adoption enables state and local governments to enhance customer experience through intelligent AI applications. Some examples include autonomous vehicles like self-driving shuttles, improving public transportation, and natural language processing, enabling better citizen engagement.
Personalized AI training in education and AI-powered healthcare solutions further demonstrate how emerging technologies can improve services for all citizens, including underserved and marginalized communities.
3. Enhanced data-driven decision making
Governments collect tens of thousands of data points daily, but without advanced analytics, this input data is underutilized. AI technologies allow decision makers to analyze data, predict outcomes, and identify patterns more effectively.
By using AI-powered computer vision, deep learning, and data science, public agencies can make informed policy decisions, enhance security measures, and protect national interests.
Additionally, AI aids in technology policy development, ensuring the responsible use of AI in governance.
AI in government case studies
What are the challenges of AI in the public sector?
Employment
Unemployment can be the scariest part of artificial intelligence if we disregard the hypothetical scenario of an AI takeover. Governments, as public service providers, should be concerned about the impact of AI on government jobs.
To mitigate the impact of potential unemployment due to automation, governments need to ensure that humans focus on higher-value-added tasks or move on to the private sector if their current tasks are going to be automated.
According to the European Commission’s Eurobarometer survey6 that presents European citizens’ thoughts on the influence of digitalization and automation on daily life:
- A majority (73%) agree that robots and AI accelerate the speed at which workers perform tasks, with nearly a quarter (24%) strongly agreeing, while 20% disagree.
- Two-thirds (66%) believe that AI and robots take away jobs from workers, with 28% in full agreement.
- Over six in ten (61%) think AI harms workplace communication, with almost a quarter (24%) expressing strong agreement.
AI biases
AI algorithms may contain biases due to prejudices of the algorithm development team or misleading data. Though building an unbiased AI algorithm is technically possible, AI can be as good as data, and people are the ones who create data. Therefore, the best thing governments can do for AI bias is minimizing it by applying best practices.
Explainability
It is not easy to explain how all AI algorithms arrive at their predictions (i.e., inferences); however, technical approaches are being developed to address this shortcoming.
This is problematic for the public sector, where providing a rationale for decisions is more critical than in the private sector since the public sector is accountable to the public. In contrast, the private sector is foremost accountable to shareholders.
Accountability
Accountability of AI systems is an issue of AI ethics. Governments in the US and the UK are introducing new laws about companies’ AI algorithms’ accountability. It will be hypocrisy if governments and companies are not held accountable for accidents & false predictions their AI algorithms make.
Check out responsible AI best practices to learn more.
Difficulty of transformation
AI transformation in government can be difficult because:
- Age of public servants: As of 2024, the average age of U.S. federal government employees is approximately 47 years.7 The workforce in the government is older than the private sector, making it potentially harder to implement the culture change.
- More ambiguous/complex KPIs: Compared to the private sector’s drive for profit, governments have more complex, harder-to-measure goals. As a result, government KPIs tend to be more activity-oriented rather than result-oriented, making it harder to measure improvements.
- Number of stakeholders: Government watchdogs, labor unions, and opposition parties are all stakeholders whose views of AI will shape how the public will perceive AI in government. This makes communication about transformation projects even more important.
In addition to technical and organizational barriers, the successful integration of AI in government depends on the public’s trust. Public input is crucial in shaping AI guidelines that reflect societal values and protect citizens’ rights.
Future-oriented frameworks like constitutional AI offer a promising approach to embedding ethical constraints directly into AI systems, ensuring they operate within boundaries consistent with democratic governance and the rule of law.
What are the best practices of AI for governments?
By investing in AI capabilities, fostering public-private partnerships, and prioritizing AI workforce development, government agencies can responsibly harness the full potential of AI:
1. The Stack Model
The stack model8 describes the foundations that enable governments to use AI reliably and accountably. It explains how digital systems in the public sector depend on the interaction of three elements: infrastructure, data, and governance.
- Infrastructure provides the technical base. It includes identity systems, secure networks, cloud platforms, and shared digital components. These systems allow public organisations to exchange information and run digital services at scale.
- Data forms the analytical layer. It covers how governments gather, link, store, and process information. High-quality data allows the creation of statistical models, monitoring systems, and AI tools that support policy development and service delivery.
- Governance sets the rules that ensure these systems operate safely and in line with public values. It covers oversight of algorithms, transparency requirements, risk management processes, and mechanisms that protect rights.
Together, the three layers work as an integrated structure. Infrastructure enables data flows, data allows analysis and automation, and governance ensures that these capabilities are used responsibly.
Governments that strengthen all three layers are better positioned to deploy AI effectively, trustworthily, and aligned with democratic principles.
2. Public-private partnerships: Driving AI innovation
Governments should collaborate with AI vendors, research institutions, and private-sector organizations to accelerate discovery and enhance AI capabilities.
For example, federal agencies have engaged with universities and the National Institute of Standards and Technology (NIST) to advance fundamental AI research and establish AI governance frameworks. Such collaborations can fuel AI investments and improve services by leveraging expertise from subject-matter experts in data science, computer science, and machine learning.
3. AI sandboxes for testing and ethical evaluation
AI regulatory sandboxes provide controlled environments where government agencies can test AI tools before full-scale deployment.
These environments can also incorporate public input, enabling citizens to express concerns and help shape AI policies that affect their communities. By integrating this feedback loop, governments can refine AI algorithms while ensuring compliance with ethical and legal standards.
For example, the UK’s Information Commissioner’s Office (ICO) introduced AI regulatory sandboxes to evaluate ethical AI use, providing insights into AI applications in fraud detection and public service delivery.
4. Modernizing technology infrastructure for AI integration
The successful implementation of AI technologies in government requires upgrading legacy IT systems. Modern cloud computing solutions and edge AI enhance scalability, enabling real-time data processing and AI-driven decision-making.
Federal and local governments investing in AI infrastructure can leverage machine learning techniques and deep learning models to optimize government operations.
AI adoption in public-sector IT systems also helps predict outcomes and automate service delivery, reducing the burden on government employees.
5. AI workforce development and talent recruitment
Federal and state agencies must prioritize AI talent recruitment and offer AI training programs to upskill government personnel. AI task forces should be established to oversee AI system development and implementation, ensuring agencies are equipped with AI talent to handle complex AI applications.
As the government expands its use of AI, specialized expertise in computer vision, data science, and machine learning is required. AI training initiatives can bridge the talent gap, ensuring public agencies have the necessary skills to deploy AI tools responsibly.
Additionally, partnerships with universities can provide structured AI development programs to strengthen personnel management in AI-driven roles.
6. Ensuring AI fairness, accountability, and compliance
Governments must implement strong oversight mechanisms to mitigate biased results and ensure AI is used ethically. AI ethics boards, in coordination with subject-matter experts and mechanisms for public input, can help establish guidelines for AI research, investments, and system governance.
One emerging framework that aligns with these efforts is constitutional AI, which focuses on aligning AI behavior with constitutional principles and societal values such as fairness, accountability, and non-discrimination.
Regulatory frameworks such as the EU AI Act and the U.S. Executive Order on AI emphasize the protection of privacy, the safeguarding of human rights, and the prevention of the misuse of AI technologies.
Transparency laws require AI algorithms to be explainable, reducing the risk of discrimination against marginalized and underserved communities. AI-powered systems in government use should align with principles of responsible AI, ensuring that decision-making processes remain transparent and equitable.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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