Governments are poised to be the biggest AI spenders by industry. To prepare, address risks and prioritize use cases.
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AI in Government Promises Automation and Better Decisions
Four key initiatives to get AI-ready in government
Whether your ambition is for AI to augment everyday processes or create something game-changing, government needs a set of foundational capabilities to succeed.
This guide can help government IT leaders ready their organizations to:
Define their “AI ambition” and spot AI opportunities
Prepare AI-based cybersecurity
Make data AI-ready
Adopt AI principles
Embrace AI in government
AI in government helps public-sector organizations improve service levels despite talent shortages and tight budgets.
Government AI Opportunity
Government AI Risk
Top Government AI Uses
Generative AI has raised mainstream awareness about new ways to improve outcomes.
Heightened citizen demand for government services, coupled with the need to do more with fewer resources, is leading everyone across government to embrace automation. That means more AI in government — much more. Government is on track to spend more on AI than any other industry by 2025, notching an estimated 19% CAGR in AI investment between 2022 and 2027.
AI is not a single technology, however. The Gartner Hype Cycle™ for Digital Government Services, 2024, alone highlights four different AI technologies, including influence AI, emotion AI and generative AI (GenAI).
GenAI, in particular, is catalyzing a great deal of excitement among government stakeholders, in part because everyone from the senior executives to the frontline workers see what they can through consumer applications like ChatGPT. Governments are particularly exploring GenAI to add value for both internal and external audiences with:
Text generation. The ability to draft communications in multiple forms to address multiple target audiences.
Text summarization. Summarizing long or complex information sources to support improved decision making, for example by case workers. Similarly, summarizing complex or abstruse documents for laypeople or policymakers.
Question answering. Whether for customer service queries or to provide guidance for employees, for example to ramp them up into new roles.
Text classification. Large language models (LLMs) enable classification and collation of the large volume of unstructured text, improving the quality of the data used to support decision intelligence and policy development.
Sentiment extraction. Analyze transcripts of customer service or social media conversation to assess citizen engagement and communication.
Whether GenAI or any other AI technology, governments are hoping their AI investments will help improve government outcomes, for example through the citizen experience, operational efficiency, reduced risk, mission outcomes and better governance and assurance, among other key opportunities.
Realize the promise of AI in government by addressing its risks.
Government CIOs have to develop new policies, guidelines and regulatory frameworks to proactively address potential cybersecurity and privacy challenges that arise through the use of AI in government.
In the EU, government practices are informed by EU regulatory guidelines like the General Data Protection Regulation (GDPR) and the Digital Services Act, among others. In the U.S., the Executive Order 14110 on the “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence” guides responsible AI development and deployment, at least in federal agencies (but with potential cascading effects to local governments).
Regulations like these reflect areas of high risk for AI in government. Part of that risk is grounded in the need to maintain the trust of the community and of the government workforce. This requires strong governance and risk management around five major sources of risk that apply to all AI, but particularly to GenAI, given its rapid proliferation and frontline adoption:
Accuracy. Particularly as it relates to GenAI and self-service applications, these platforms can be vulnerable to errors, as well as inconsistent answers.
Bias. The data used to train an AI model may be incomplete or poor quality, or contain inherent biases.
Copyright. With GenAI and large language models (LLMs), there is the potential for copyright violations, given the unsettled legal status of the data used to train them.
Privacy. GenAI systems available to the public may use the citizen’s input for further training and as a result are unlikely to meet privacy legislative obligations or community expectations. Employees using similar tools as part of service delivery should similarly only pose questions or input information as they would on a public site — that is, without identifying information.
Sensitive/confidential information. Both the data and the metadata used to train an AI may be sensitive, such as in the case of data related to defense or critical national infrastructure. As such, this is a category of data that needs specific policy attention.
Given these risks, government CIOs must continue to revisit data quality, management and assurance approaches, while also aligning with community expectations by establishing ethical frameworks and using human-centered design (HCD) to ensure AI solves the problems governments hope it will.
Governments are embracing several high-value government AI use cases.
AI in government exists both in stand-alone applications and as embedded capabilities inside of a broader platform. Whether stand-alone or part of a whole, government CIOs are prioritizing AI capabilities that serve the larger government mission. Two high-level capabilities stand out as high potential for AI in government:
Decision intelligence
The need for swifter and more accurate decisions is an evergreen challenge in government. Decision intelligence (DI) is a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and their outcomes evaluated. Applying AI techniques within DI practices supports and automates swift, accurate decisions in order to deal with high backlogs of cases, a fluid environment, and a workforce under stress. Instead, DI helps government organizations to:
Improve consistency and transparency in decision making using AI.
Shift the focus of service delivery from reactive to proactive.
Free up knowledge workers by reducing the effort spent doing repetitive administrative tasks or collecting data available by other means.
Improve the quality and timeliness of both internal- and external-facing services and decision making.
Business process automation
By 2026, more than 60% of government organizations will prioritize investment in business process automation, up from 35% in 2022. The ability of automation platforms to incorporate AI capabilities has elevated them as a CIO priority and improved their time-to-value, especially when aligned with organizational goals of improving operational effectiveness, personalizing government services, and enabling proactive access to government resources. Reaching those goals will depend on collaboration and effective governance among an ecosystem of stakeholders across multiple government agencies and, potentially, tiers of government.
At a more granular level, five use cases aligned with these capabilities are driving AI in government:
Intelligence gathering: Analyzes and synthesizes large amounts of data to generate reports, summaries and visualizations. This augments analysts in pattern recognition, threat assessment and actionable insights.
Decision support: Through the use of historical data, battlefield conditions and real-time information, generates multiple courses of action and evaluates their outcomes.
Contract writing: Generates unique and novel contracts in accordance with regulations and policies that result in cost savings and limit resources and time.
Contact center virtual assistance: Creates personalized government responses to citizen questions.
Predictive maintenance: Analyzes large amounts of historical data on military hardware and combat equipment to recognize patterns and anticipate maintenance requirements.
AI in government will enable these use cases across a range of functional solutions for customer service and support, marketing, human capital management, procurement and others.
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FAQ on AI in government
What is AI in government?
AI in government exists both in stand-alone applications and as embedded capabilities inside of a broader platform. Whether stand-alone or part of a whole, government CIOs are prioritizing AI capabilities that serve the larger government mission. Two high-level capabilities stand out as high potential for AI in government: decision intelligence and business process automation.
What are the potential benefits of using AI in government?
The potential benefits of AI in government are significant and multifaceted, offering the possibility to enhance efficiency, transparency and service delivery. Examples include:
Enhanced decision making
Personalized public services
Fraud detection and prevention
Data-driven policy development
Public safety and security
Health and social services
Environmental management
Transparency and accountability
How can AI improve efficiency and effectiveness in public services?
AI can enhance public service efficiency and effectiveness by:
Automating routine tasks and enabling predictive analytics for better resource planning
Improving decision making with data insights, detecting fraud and optimizing resource use
Personalizing services, providing real-time monitoring for swift emergency responses, and boosting public engagement by analyzing feedback
Drive stronger performance on your mission-critical priorities.