Understanding AI in government: Applications, use cases, and implementation

Artificial intelligence is a set of technologies that enable machines to mimic human intelligence and make efficient and accurate data-driven predictions, recommendations, and decisions. A subset of AI, generative AI, goes a step further. Generative AI features enhanced data processing capabilities and the ability to create new, original content. It enables intuitive, natural language interactions with machines, making technology more accessible. Be it traditional AI or generative AI, at its core, AI can leverage and process data more efficiently than humans can. 

Government agencies run on vast amounts of data — likely even more data than most private organizations. This data is often highly sensitive and subject to strict privacy laws. As a result, the public sector is challenging. It has to serve the needs of every citizen, and different agencies have digitized differently and at various speeds. The result is overwhelming amounts of digital data, distributed across information silos that government agents — and citizen users — don’t always have the skills to navigate. 

Legislation, transactions, records, intelligence, and more form the data pool of government agencies and stakeholders. AI can help conquer big data challenges: breaking down silos, streamlining operations, and enhancing efficiency. By using AI, agencies can reduce costs, improve service delivery, and enhance citizen satisfaction.

In transportation, machine learning algorithms can be used for traffic optimization and predictive maintenance for transportation infrastructure. In the US, organizations are exploring using AI for public transportation applications, reading sensor data, and helping users plan routes and trips and improve roadway safety.

In education, AI can help close education gaps by democratizing access. According to UNESCO (the United Nations Educational, Scientific and Cultural Organization), AI has the potential to address some of the biggest challenges in education today. It can innovate teaching and learning practices and accelerate progress toward the sustainable development goal of ensuring inclusive and equitable quality education for all. AI also has the potential for offering personalized learning platforms and focused help for students. Some government agencies are already rolling out AI training modules within governments, with the possibility to expand to private citizens.

In healthcare, AI-powered tools can improve the overall level of care by accelerating diagnoses and clinical research. AI can support many diagnostic tools and automate administrative processes. It has the potential to improve the collection of clinical data, expediting knowledge-sharing and research efforts across the healthcare sector. Government agencies across the world are also utilizing AI as a predictive analytics tool for disease outbreaks to prevent the next pandemic.

Beyond prediction and processing, government agencies can use AI to better connect citizens with critical services they need. Consumers expect immediate, efficient, and personal service — and government agencies are often notorious for slow and inefficient processing times. AI can improve the quality of customer service and support to match expectations set by the level of service in the private sector.

In the US, the public sector is ranked lowest of 10 industries surveyed for customer satisfaction.¹ By integrating various AI applications like Search AI, chatbots with 24/7 availability, and automated administrative processes, governments can streamline public information dissemination, offer personalized government services, and improve customer service overall.

Read more: Why customer service matters for government — and how AI will help

Governments handle vast amounts of paperwork, data, and administrative tasks. Time wasted manually sorting through documents can have real ramifications for citizens. The US government estimates that $140 billion² in potential benefits go unused each year because of outdated or complicated processes.

Beyond improving knowledge-sharing, access controls, and overall efficiency, going paperless paves the way for automation and AI. AI is particularly useful for retrieving information stored in various formats and locations, making data more accessible and preventing employees from wasting time searching through different files and systems.

Some estimates predict that the US public sector will collectively experience $519 billion in productivity gains from generative AI by 2033.³ By reducing manual workloads, AI helps minimize human error and increases operational efficiency.

In the legal field, AI can find information stored in different places and formats. It can automate processes such as document processing and classification; workflow automation for permits, tax filings, and social benefits; and fraud detection in public assistance programs. AI can also simplify and streamline legal processes, including e-discovery, compliance checks, and contract analysis, enhancing accuracy and efficiency.

AI can also help sift through large amounts of information in higher education, especially research institutions. Generative AI helps researchers locate and use contextual, relevant information from a variety of sources. This is especially crucial for collaborative research projects that span across departments or universities.

Data-driven decision-making is key to optimizing efficiency, services, and outputs. However, recent research showed that only 32% of public sector leaders use data insights for daily decisions.

AI gives its users the unparalleled ability to draw valuable insights from consolidated data that support decision-making. For instance, predictive analytics can forecast trends in crime, public health, and economic shifts, helping governments proactively mitigate and respond to these challenges.

Policy-makers can also rely on predictive analytics to build simulation models that assess the potential impacts of legislation before it is implemented. 

Read more: Solving challenges with data and AI: 5 insights for public sector leaders

Like everything in the public sector, AI use must be carefully regulated to ensure ethical deployment, fairness, and transparency. Government agencies work with sensitive data. Establishing robust governance frameworks helps mitigate risks, uphold legal standards, and maintain public trust in government AI initiatives. The latter is key in meeting consumer expectations and improving government services.

Regulatory bodies have, for the most part, largely struggled to keep up with AI advancement. The speedy adoption of AI in private sectors and an uneven understanding of the technology’s capabilities, benefits, and risks make regulation a challenge.

To respond, governments are establishing ethical standards, legal considerations, and frameworks to ensure responsible AI usage. These regulations aim to promote fairness, accountability, and transparency, ensuring AI applications align with democratic values and human rights. However, compliance with these frameworks varies across regions, influencing how AI is implemented in government agencies.

Overall, data privacy laws, fairness mandates, and transparency guidelines are at the heart of compliance requirements, which aim to ensure trustworthy and safe AI services.

In the US, no current federal, overarching AI regulations exist,⁴ and efforts to adopt or reject them are intrinsically tied to the two-party system. Administrations will sway between imposing regulations or removing them altogether in favor of fast-paced innovation. Instead, states create regulations, resulting in a patchwork of legislation and a complicated compliance landscape that government agencies and stakeholders are left to navigate on their own.

On the other hand, the European Union introduced the AI Act, the first-ever legal framework to guarantee safety, fundamental rights, and human-centric AI and strengthen uptake, investment, and innovation in AI across the EU.

The greatest security concern in the use of generative AI in government is how to handle sensitive data with public large language models (LLMs), foundational pieces of AI systems that use natural language. Improper use of public LLMs can pose risks such as data leaks, unintended exposure of classified information, and vulnerabilities to adversarial manipulation.

AI systems often function as black boxes, making security assurance particularly challenging. Their lack of transparency complicates risk assessment and mitigation efforts, increasing vulnerabilities to data breaches and adversarial attacks. Consequently, deploying AI in government presents complex security challenges, particularly in protecting national security interests. Implementing stringent safeguards, such as robust encryption, access controls, and continuous monitoring is essential to mitigate these risks effectively.

To ensure that generative AI is grounded with proper context, organizations can implement retrieval augmented generation, or RAG, a group of techniques that enable the safe use of proprietary data. This approach helps mitigate risks by ensuring AI models rely on authoritative, up-to-date data rather than solely on potentially biased or outdated training data. By integrating RAG, government agencies can maintain greater control over sensitive information, and rely on more context-aware responses. 

The biggest challenges in AI adoption aren’t just technical. They include a lack of specialized talent and often unclear regulations.⁵ Many agencies face resistance due to concerns over data security, job displacement, and the complexity of implementation. Successfully integrating AI into government must be a multi-step, strategic process that overcomes these obstacles and fosters a culture of innovation.

• Ensuring real-time data visibility: Effective AI implementation depends wholly on agencies having complete, real-time access to all relevant data. Without full visibility, AI-driven insights and automation may be incomplete or inaccurate. After all, AI is only as good as the data it uses.

• Planning and governance: While the lack of regulation may foster innovation, it may hinder government-level adoption of AI. Establishing clear policies, ethical guidelines, and regulatory compliance measures can help ensure fast — and responsible — AI deployment.

• Identifying specific use cases: Agencies should assess where AI can drive the most impact, whether in public services, automation, or decision-making. Governments should slowly scale these services to impact sector and economy levels. 

• Scaling and security: Beyond scaling use cases, agencies should ensure that the technology has the capacity to meet scale and evolving government needs while remaining secure.

• Integration with existing systems: Though government agencies are at varying levels of technological maturity, ensuring that AI systems seamlessly integrate into existing systems is key to successful implementation.

Around the world, government agencies are developing programs to upskill the workforce of government stakeholders and public users on using AI to unlock even more benefits (while mitigating the risks).

The US Department of State AI Inventory 2024 outlines various AI applications in diplomacy, cybersecurity, and administrative functions, aimed at improving public services, efficiency, and decision-making.⁶ The U.S. Department of State is using AI to modernize its diplomatic statecraft. The Office of the Under Secretary for Management uses AI technologies within the Department of State to advance traditional diplomatic activities, applying machine learning to internal information technology and management consultant functions.

Some other examples of initiatives include:

• Translation of consular content: AI translation models work alongside teams to provide consular content on government websites to customers in their preferred language. AI reduces the time and resources typically needed while the human touch ensures accuracy and understanding (AI still struggles with legal jargon!).

• Violence Against Civilians Model: A machine learning model that uses open source political, social, and economic datasets to forecast mass civilian killings for the upcoming quarter and year for each country globally in order to inform conflict prevention.

• Senturion Alpha: A stakeholder/influence-driven model that identifies where key decision-makers fall on an issue spectrum and who influences whom. The simulation analyzes the political dynamics within contexts and estimates how the policy positions of competing interests will evolve over time.

• Storyzy: Improves detected use of synthetic content, which refers to computer-generated data that mimics real-world data.

The Elastic Search AI Platform offers full search capabilities for building AI apps and RAG workflows, document-level security, a production-ready vector database, our ELSER pretrained retrieval model for more relevant natural language search results, and E5 (multi-language) support. Elastic’s open approach allows your team to integrate your data, securely, with your own or third-party transformer models. 

• Advanced data analytics: Real-time insights from structured and unstructured data

• Enhanced search capabilities: Improved data retrieval for intelligence and public records

• Scalable AI systems: Flexible infrastructure that adapts to evolving demands

By leveraging Elasticsearch, government agencies can enhance transparency, efficiency, and citizen engagement, driving digital transformation at scale.