It’s been just a year since generative AI (gen AI) tools first captured public attention worldwide. But already the economic value of gen AI is estimated to reach trillions of dollars annually—even as its risks begin to worry businesses and governments across the globe. Gen AI offers government leaders unique opportunities to steer national economic development (Exhibit 1). At the same time, they face the heavy burden of monitoring the technology’s downsides and establishing robust guidelines and regulations for its use.

Many government agencies have started investing in transformations made possible by gen AI, but the technology’s rapid evolution means that predicting where it can contribute the most value is difficult. In this article, we discuss three important questions that public sector organizations may need to consider before choosing areas for investment:

We conclude with a suggested eight-step plan for government organizations that are just beginning to implement gen AI use cases.

By now, the risks of gen AI—such as its tendencies toward unpredictability, inaccuracy, and bias—are widely known. Government agencies face different risks than do private sector companies. For example, the technology can be misused to spread political propaganda or compromise national security. Confidential government data can be leaked or stolen if government employees inadvertently introduce that information into foundation models through prompts.

Some outputs from gen AI models might contain inaccurate information—also called “hallucinations”—that could erode public trust in government services that leverage these technologies. Like many private sector organizations, government agencies face challenges with gen AI’s transparency and with the difficulty of explaining the conceptual underpinnings of gen AI, as well as the logic of the models’ decisions and output. Consequences might include low public acceptance of gen-AI-powered government services and unclear liability when unintended effects occur. And like all organizations, government entities run the risk that criminals may misuse gen AI to carry out powerful cybersecurity attacks.

To address those risks, many countries—such as the United States, Australia, and China—have launched initiatives to create frameworks of regulations and policies for AI, and some have expanded their existing AI regulations to explicitly include gen AI, too. The European Union is leading a global effort to build safeguards for any product or service that uses an AI system. Many state government agencies in the US have also released AI-related legislation, executive actions, and policies focused on mitigating the potential risks of AI systems—by highlighting the negative aspects of AI, transparently communicating where AI is used in government, and addressing the ethical aspects of AI usage.

However, those mitigation efforts are still in their early stages in most parts of the world, and gen AI is evolving fast, which means that governments must revise their regulations continually to keep pace. Some government organizations have started ongoing awareness programs among stakeholders—especially end users—about gen AI’s risks and how to address them. For example, the United Kingdom’s Central Digital and Data Office has released a guide for civil servants on safe and informed use of gen AI tools. Similarly, Australia’s Digital Transformation Agency and its Department of Industry, Science and Resources provide interim guidance to government agencies on responsibly using publicly available gen AI platforms, with emphasis on ethical AI usage, security, and human oversight.

As key providers of services to the public, government agencies are likely to prioritize the delivery of those services as a critical area for AI-driven improvements. A good place to start may be our “4Cs” framework, comprising four cross-industry categories: content summarization and synthesis, coding and software, customer engagement, and content generation (Exhibit 2). Most gen AI implementations we have seen fall into one of those four categories, which could apply to both private and public sector enterprises.

Gen AI implementations could streamline a broad range of services that governments typically provide, in areas such as education, healthcare, defense and intelligence, and urban development (see sidebar “Potential applications of gen AI in government functions and services”). Across all of those areas, we have seen government agencies implement gen AI use cases in both external and internal operations that fall within the categories of our framework (see Exhibits 3 and 4). For example, in customer-facing applications, gen AI can help the public navigate government services and get access to real-time language translation. Internally, gen AI can draft creative content such as speeches and official correspondence, simplify complex official documents, and consistently generate financial reports and KPIs on schedule.

Some governments may aspire to develop foundation models—the core models on which gen AI applications are built. But leaders of government agencies must be aware that this endeavor requires considerable investment of time and resources. The many barriers to entry include the availability of talent to build, train, and maintain gen AI models; the necessary computing power; and experience in addressing potential risks inherent in building and serving gen AI foundation models. Almost all current work in these models is led by a few large private sector tech companies (Cohere, Google, Meta, and others) and by open-source initiatives that are quickly becoming popular (such as Hugging Face, Stability AI, and Alpaca).

Unlike global private-sector tech players, government organizations simply lack the capabilities to develop foundation models while managing their risks. For example, violations of intellectual property and copyright laws can expose government agencies that own foundation models to litigation; gen AI’s occasional lack of proper source attribution makes it even harder to detect potential copyright infringement in its responses. Legal implications also apply to manipulated content—including text, images, audio, and video—that malicious actors may use to harass, intimidate, or undermine individuals and organizations. Users could act unscrupulously or illegally by exploiting inherent biases in the data that a specific foundation model was trained on. As a result, some governments—such as those of Iceland and Finland—have chosen to partner with global large language model (LLM) providers to get access to their existing models and augment and customize them to suit their own needs, by adding proprietary data and insights.

For public sector agencies just beginning to venture into gen AI, we suggest this eight-step plan: