Table of contents
Highlights
- Federal agencies are already using AI in some capacity, though many initiatives remain in early phases.
- Procurement complexity, security uncertainty, and workforce readiness are key barriers slowing government AI adoption.
- FedRAMP-authorized AI tools may help agencies reduce months of independent security vetting during procurement.
- Starting with quick-win use cases in IT service management or document processing can build momentum for broader rollouts.
- Moveworks is FedRAMP Moderate® authorized and designed to help government agencies access a vetted agentic AI platform for employee support and operations.
Government agencies have traditionally moved slower on technology adoption than other sectors. With AI, that's starting to change. Agencies are paying closer attention than they have with previous technologies.
AI is advancing faster than typical adoption cycles, and it's offering more immediate ways to improve how public services get delivered. The U.S. Government Accountability Office reviewed AI inventories across 11 selected agencies and found a significant shift.
The total number of reported AI use cases nearly doubled from 571 in 2023 to 1,110 in 2024. Generative AI use cases grew even faster, increasing from 32 to 282. As government employees show interest in AI's potential, leaders need a practical framework for navigating this new territory.
The challenge is that building a roadmap from evaluation to implementation can feel slow and even overwhelming. But if you understand what AI looks like for your agency and how to evaluate tools, you can build a realistic AI roadmap.
What is AI for government agencies?
AI for government agencies refers to the application of machine learning, natural language processing, and automation technologies for improving public sector operations and citizen services.
These tools are typically required to meet strict security, compliance, and procurement standards unique to government environments. Recent numbers show growing AI usage across federal agencies, which now report over 3,600 AI use cases.
Why government AI adoption stalls before it starts
Many government AI initiatives get stuck in the pilot phase. Procurement complexity, security requirements, and workforce resistance combine to create barriers that can delay adoption.
GenAI tools and AI agents often remain stuck in pilot phases, moving forward slowly or sometimes not at all.
Here's where agencies typically encounter friction:
Procurement and authorization bottlenecks
In April 2025, the Office of Management and Budget (OMB) revised AI procurement policies to encourage a forward-leaning, pro-innovation approach, empower agency leadership, and introduce transparency measures to keep the public informed.
Even with OMB support, agencies face pressure to keep security and compliance front and center. They must navigate an expanding set of AI-specific rules alongside standard procurement requirements. These might include maintaining AI use-case inventories or appointing a chief AI officer.
The GAO identified 94 requirements that apply across government. These administrative demands can slow adoption before implementation even begins.
Security and compliance uncertainty
AI is evolving rapidly, so compliance requirements can feel ambiguous. Adding to that challenge, vendors often document compliance differently, making comparison difficult. Agencies may struggle to determine which security, privacy, or risk requirements apply to a particular AI use case.
That delay can create friction, where legal, IT, and procurement teams need extra time to review how a tool uses, stores, and shares government data. This sequence of policy decisions typically leads to one of two outcomes:
Agencies may hesitate to move forward with a vendor when compliance expectations aren't clearly defined.
Teams may select the safest option rather than the best-performing one.
Either scenario could make it harder for security and compliance teams to keep their AI project moving forward on schedule.
Workforce readiness and cultural resistance
Public skepticism about government AI can make agencies more cautious. Employees may worry that AI will eliminate jobs or result in mistakes they'll be held accountable for.
Underinvestment in technology may also make employees skeptical that AI initiatives will meaningfully impact their workloads. But resistance can slow adoption even after leadership approves the technology.
Ongoing training and internal champions can help employees understand what the AI tool does and how it fits their day-to-day work. Building trust around AI can help your organization move forward with adoption.
How to evaluate AI tools for government use
Selecting the right AI tool for government requires more than a feature checklist. Agencies need to verify security authorizations, map compliance requirements, and confirm integration with legacy systems.
Structure the evaluation process to address all criteria, with particular emphasis on compliance and interoperability for a smoother AI experience.
Start with FedRAMP-authorized solutions
FedRAMP has launched a program to prioritize AI cloud services to help federal employees access conversational AI in their daily work.
FedRAMP's AI Prioritization Initiative shows the federal government's commitment to enterprise-ready AI. For now, agencies can use the FedRAMP Marketplace to check whether a vendor is authorized or moving through the process. This can help narrow shortlists during vendor selection.
Map compliance requirements early
Before issuing an RFP, identify which regulations and security standards apply to your desired AI project. Building a compliance map sets you up for success by clarifying requirements that might affect vendor eligibility (for example, FISMA or NIST 800-53).
This upfront work gives vendors clearer expectations and keeps the selection process moving forward. It also reduces the risk of compliance issues surfacing after selection, which can be especially problematic for government agencies.
Prioritize integration with existing systems
Evaluate API compatibility and look for connectors across ITSM, HRIS, document management, and help desk platforms, especially when departments run on different technology stacks.
Broad integration support is a sign that a platform can work across systems and gives agencies more flexibility to expand AI use cases later without rebuilding the foundation each time. FedRAMP-authorized platforms like Moveworks are designed to streamline the security review process because much of the required vetting is already complete.
Where government agencies are using AI today
With thousands of use cases now in play, here are some key categories of AI applications reshaping how federal and local government functions.
IT service management and employee support
Ticket resolution times have been a steady problem for years. With fewer resources and headcount, the outlook didn't look promising until generative AI gained traction. AI-powered service desks can handle common employee requests and reduce pressure on support teams.
Beyond generative AI, routine tasks like password resets and software provisioning can often be completed end-to-end using agents. By resolving simple requests quickly, AI can help reduce ticket backlogs and give IT teams more time for triage and complex issues.
Document processing and knowledge management
Government agencies manage enormous amounts of information. Much of it sits in reports, case files, and other unstructured documents that may not even be digitized. Early AI use cases have helped teams search through and summarize this material faster.
The National Institute of Health (NIH) has explored AI-assisted tools for screening research and supporting literature reviews. Environmental agencies are using AI for forecasting events like wildfire behavior and response. Natural language processing helps teams sort incoming FOIA requests and flag sensitive information for human review.
Citizen-facing services and case management
Federal agencies are already using AI-supported chat and voice tools to improve customer service. Chatbots and virtual assistants can typically answer routine questions faster, which often leads to higher satisfaction when live support is needed.
AI can potentially step in to help with case management: basic intake, request routing, and more. These workflows can automate typical processes to reduce backlogs and give employees more time to focus on cases requiring human judgment.
Predictive analytics and decision support
Predictive models can help agencies determine where to allocate resources. For fraud detection, models might flag unusual transactions or claims for further review. Infrastructure teams can use historical maintenance data to anticipate equipment failures.
These tools work best as decision support — they give teams another source of insight. That's often more valuable than asking people to make high-stakes decisions on their own, especially when those decisions involve subjective judgment.
How to build an AI adoption roadmap for your agency
A successful AI adoption roadmap starts with clear outcomes and builds momentum through early wins. Agencies that define measurable goals before selecting tools are more likely to move beyond pilot phases.
There's a lot to think through, especially if you're just getting started. With larger projects, it helps to break them into distinct steps so your team knows exactly what to tackle. The steps are: define, implement, and scale.
The tool you select should follow the outcomes you define, not the other way around.
Define measurable outcomes before selecting tools
Measurable outcomes look different for every agency, but whatever you measure should align with your goals and strategic priorities. If your IT department is overwhelmed by support tickets, success might mean giving the team more time for innovation and higher-value work.
If you want to keep things simple, consider tying AI outcomes to existing agency performance metrics. This approach typically gives your team a clearer picture of whether you're improving on your agency's existing vision for AI, rather than creating standalone metrics.
Start with quick-win use cases that build momentum
When you look at the broader project of incorporating AI into your workflows, it's easy to get overwhelmed and want to tackle every goal at once. The best approach is to begin with low-risk starting points like IT automation or document triage.
By starting with simpler tasks first, you can build visible wins quickly. When your leadership team sees early success, they're more likely to support broader AI initiatives and eventually back the larger rollout.
Scale with change management and training
AI adoption won't succeed without buy-in from the people using your tools every day. In early phases, identify who's championing the tool in each department and make them an ambassador to drive adoption.
Beyond choosing the right champions, schedule ongoing training, encourage feedback loops between employees, and prioritize governance reviews. These formal checks can help confirm whether AI is following your agency's policies as you scale.
What responsible AI looks like in the public sector
Public sector agencies face heightened scrutiny around AI. That increases pressure on leadership to think carefully about the ethics of AI usage itself.
Responsible AI in government requires transparency, accountability, and safeguards against bias. You'll want to include bias testing and explainability checks to help support accurate, fair AI answers.
Given the sensitive nature of government work, workflows should include human-in-the-loop review to confirm the technology is operating within appropriate boundaries.
Responsible AI builds trust internally and with the public. Prioritize documentation through audit trails so you can trace every AI decision step-by-step. Agencies can also conduct algorithmic impact assessments to identify potential risks in advance and evaluate how automated decisions might affect the public before implementation.
Take the next step with AI for your agency
AI adoption in government is becoming less about "why" and more about "how." Agencies need to move forward with clear evaluation criteria, FedRAMP-authorized tools, and phased roadmaps to deliver measurable results.
With agentic AI platforms like Moveworks, you can work within the government's strict governance and security requirements by combining enterprise search with governed workflow activation. Your team can operate like a large enterprise while keeping all AI actions within pre-approved boundaries.
Moveworks helps government agencies move beyond compliance-first tools to intelligent automation that integrates with your existing infrastructure. With our FedRAMP Moderate platform, agencies can reduce the months-long step of independent security vetting and accelerate deployment of a platform built for enterprise-grade governance.
Start small with low-overhead applications that help you evaluate security first and scale with governance as a priority. This approach can help you build support for the efficiencies you're creating.
Take the first step today and learn more about AI solutions for federal government and local government, and how Moveworks can fit into your picture.
Frequently Asked Questions
FedRAMP is a federal program that provides standardized security assessments for cloud services used by government agencies. AI tools with FedRAMP authorization have already undergone rigorous security vetting, which may help agencies shorten procurement timelines and reduce the need for separate security reviews.
Federal agencies reported over 3,600 AI use cases in 2025, spanning IT service management, document processing, citizen-facing services, and predictive analytics. Common applications include automated help desks, literature review acceleration, and fraud detection systems.
Research points to several primary barriers: workforce capacity constraints, risk-averse organizational culture, complex procurement and funding processes, and low public trust in AI systems. Addressing these barriers typically involves a combination of training, phased rollouts, and transparent governance practices.
Agencies can implement PII redaction protocols, maintain comprehensive audit logs, and require human-in-the-loop review for decisions that affect citizen rights or benefits. Selecting AI tools that comply with FISMA, NIST 800-53, and FedRAMP requirements may also help establish a strong security foundation.
Government AI deployments typically need to meet FedRAMP authorization, FISMA requirements, and NIST AI Risk Management Framework guidelines. Each agency may also have additional mandates, so mapping all applicable requirements early in the evaluation process is advisable.
Timelines vary significantly by agency, tool complexity, and authorization status. Choosing FedRAMP-authorized solutions may help reduce procurement cycles by bypassing months of independent security assessments. Starting with quick-win use cases like IT help desk automation can also help agencies demonstrate value faster.
