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Why Government’s AI Revolution Starts with Better Tools, Not Just Better Data

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For decades, public-sector decisions have run on brittle, fragmented systems. In an era of rapid policy change and real-time information, the bottleneck is no longer access to data, it’s the lack of decision-ready tools that put context, provenance, and security in the workflows where officials actually act. Usability now matters more than raw data accumulation, and the question is how governments can adapt, by building or buying AI-enabled tools that make policy execution faster, safer, and more accountable.

The Bottleneck: Usability, Not Data Access

Governments already sit on oceans of information such as legislative trackers, regulatory filings, economic indicators, satellite imagery, open-source media, and internal reports. The core problem is how that information reaches decision makers, which is slowly, piecemeal, and often stripped of the context or provenance needed for action. U.S. oversight bodies have emphasized that without strong governance, integration, and traceability, AI and analytics struggle to translate into operational decisions in mission-critical environments.

Dashboards and data lakes alone rarely fix this. Management research shows that dashboards, useful as they are, can mislead or overwhelm users and do not inherently improve decisions unless they are tightly connected to concrete choices and actions. Studies also suggest that the real value of analytics emerges only when data is reframed around decision-making itself, not simply amassed for its own sake.

Why the Status Quo Is Now Ripe for AI Transformation

Governments everywhere are grappling with outdated systems, thinning institutional memory, and a flood of increasingly complex policy demands. These long-standing structural issues are converging at the very moment AI tools are becoming more capable of addressing them, making the status quo untenable and the case for transformation urgent.

1) Patchwork systems persist. Critical government IT remains a mosaic of legacy applications, email workflows, and siloed databases that don’t interoperate. The U.S. Government Accountability Office (GAO) consistently flags decades old, mission critical systems that are costly to maintain and hard to modernize, with updates as recent as 2025 detailing the most at-risk platforms. Globally, governments are pushing toward platform-level capabilities but progress is uneven; the World Bank’s GovTech Maturity Index is a useful lens on where digital government building blocks are and aren’t yet in place. Meanwhile, the EU’s Interoperable Europe Act makes interoperability (shared solutions, standards, reuse) a legal requirement across the public sector, an approach worth watching beyond Europe.

2) Institutional memory disappears. Attrition and turnover erode context—who decides what, why, and under which constraints. In the U.S., the Partnership for Public Service reports a 5.9% government-wide attrition rate in fiscal 2023, lower than 2022 but still consequential for knowledge continuity. Research on senior-level staffing also shows how churn degrades expertise and relationships that are critical for coordination across the executive branch. 

3) Policy complexity is accelerating. The sheer volume of rulemaking and guidance creates blind spots for organizations without automated change detection. The U.S. Federal Register publishes annual statistics on rules, proposed rules, and total pages, illustrating the scale and variability that agencies (and regulated entities) must track. Text-as-data projects like RegData quantify the growth and distribution of regulatory restrictions over time, offering machine readable evidence that the monitoring burden is real.

From Analysis to Operations: Purpose‑Built AI Agents for Policy

The next wave moves beyond AI that analyzes to AI that operationalizes. Purpose-built agents for the public sector should:

  • Continuously monitor relevant sources across jurisdictions and languages (e.g., media signals can be monitored at scale).
  • Flag changes with context and provenance, surfacing which statute, rule, or guidance moved, and why it matters.
  • Draft first pass briefs and impact notes linked to the authoritative source text and the responsible policy owner.
  • Maintain living stakeholder maps that reflect shifting authority and influence rather than static org charts.
  • Integrate directly into action points such as taskers, comment portals, docketing systems, and clearance chains so insight can become action in the same window.

Public‑sector guidance supports this shift. The NIST AI Risk Management Framework (AI RMF 1.0) lays out practices for making AI valid, reliable, safe, secure and resilient, accountable, transparent, explainable, and privacy‑enhanced. In 2024, the U.S. Office of Management and Budget directed agencies to maintain AI use‑case inventories and implement minimum risk practices for uses that affect the public’s rights or safety.

What “Good” Looks Like for Government AI Tools

Not every AI solution is fit for the public sector. To maintain trust and reliability, tools need to meet a higher standard, and be built around transparency, security, and interoperability, while ensuring procurement frameworks reinforce accountability.

1) Decision centric by design. Begin with the high-stakes decisions (e.g., whether to issue an emergency waiver, how to comment on a proposed rule, when to trigger an interagency consult). Work backward to the minimum evidence and provenance needed to act. Present options, not just insights, and make the “next action” obvious. This aligns with AI RMF’s emphasis on understanding context, measuring risk, and managing controls across the lifecycle.

2) Explainability and source linking, by default. Every claim should be traceable to a source document with inline citations and timestamps. This is as much a UX requirement as a governance one. GAO’s accountability framework stresses documentation and auditability so AI can be traceable and governable in public missions.

3) Security and compliance baked in. Operational tools must align with zero‑trust architectures and the realities of multi‑cloud and, where applicable, classified networks. In the U.S., that means designing toward FedRAMP authorizations for cloud services and implementing OMB’s Zero Trust strategy alongside CISA’s Zero Trust Maturity Model v2.0.

4) Interoperable from day one. Policy execution crosses agencies, levels of government, and borders. APIs, shared vocabularies, and metadata standards are prerequisites for useful AI tooling. The EU’s Interoperable Europe Act is a forward‑leaning model that promotes reuse and cross‑border interoperability by design; it began applying in July 2024 and phases in further obligations during 2025. World Bank GTMI evidence likewise shows that platform‑level capabilities correlate with better service delivery and resilience.

5) Procurement that rewards outcomes. Agencies consistently report that procurement rules and compliance complexity slow AI adoption. Recent assessments highlight the need to build AI risk requirements into contracts and to use acquisition as a lever for trustworthy AI. GAO’s 2025 review of generative AI use at federal agencies surfaced challenges including compliance with existing policies, technical resource constraints, and keeping appropriate‑use policies current.

The Stakes and the Opportunity

The stakes and the opportunity are clear. In national security and economic policy alike, the window for action is compressing from weeks to days to hours. The National Security Commission on Artificial Intelligence final report warned that governments that fail to adapt AI enabled workflows will cede decision advantage; tools that convert information into options with governance built in can be the difference between timely action and avoidable delay. The true revolution will not be another data warehouse, it will be operational AI tools that embed context, provenance, and accountability at the point of decision. Done right, AI strengthens rather than supplants the human judgment at the heart of democratic governance.

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Joe Scheidler is the Co-Founder and CEO of Helios, an AI-native platform building the operating system for public-private sector interaction, starting with legislative intelligence, regulatory compliance forecasting, and government affairs automation. Before founding Helios, Joe served as a policy and strategy advisor in the Office of the U.S. Secretary of State, where he led Congressional engagement for the U.S. government's coordination on the Partnership for Global Infrastructure and Investment (PGI). Prior to that, he spent two years at the White House, including as a Special Advisor at the Office of the National Cyber Director (ONCD) and as Associate Director for National Security and Foreign Policy Personnel.

Earlier in his career, Joe held roles in the Office of the USAID Administrator, the Senate of Virginia, and across multiple political campaigns at the local, congressional, and presidential levels. He began his career at a nonprofit focused on veterans and military families.

Joe holds a B.A. from the University of New Hampshire, completed graduate coursework at Harvard University, and earned an M.A. from the U.S. Naval War College, where he focused on information operations and military intelligence. He is a member of the Council on Foreign Relations Young Professionals Briefing Series and the Foreign Policy for America Next Gen Initiative. Originally from New Hampshire, Joe now lives in New York City. He enjoys hiking, seafood, basketball, and spending time with his dog, Scout.