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Government can scale AI responsibly by sharing the foundations—governance, data infrastructure, skills, procurement, and risk controls—while designing each use around the work, people, and consequences of a specific public function. Scale should mean reusable capabilities and accountable oversight, not one model or decision rule imposed across every agency.
What “vertical rigor at horizontal scale” means for government
“Vertical” and “horizontal” are useful ways to frame the problem, not established government AI standards. A vertical approach starts with a particular mission or function: for example, helping a tax agency identify potential fraud or helping public-health staff detect outbreak signals. It asks whether AI fits the task, which data and expertise it requires, who could be affected, and how results will be checked.
A horizontal approach supplies capabilities that can be reused across public bodies: sound data practices, secure infrastructure, skilled staff, procurement expertise, governance, and risk-management processes. The aim is to reuse the groundwork without treating distinct missions as interchangeable. A tool that classifies documents is not equivalent to one that informs policy or oversight, where judgments may be contested and consequences harder to reverse.
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The OECD’s 2025 report, Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions, analyzes 200 use cases across 11 government functions. Its framing supports a systems approach; it does not establish a single AI model or deployment pattern for all governments.
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Adoption is growing, but the numbers describe different things
The OECD’s Digital Government Outlook 2026 reports that AI use in internal processes increased from 23 of 33 measured OECD countries (70%) in 2023 to 31 of 36 (86%) in 2025. For public services, the reported share rose from 22 of 33 (67%) to 27 of 36 (75%). These are country-level survey results—not percentages of agencies, deployments, or spending. The cited comparison had no 2025 data for Germany or the United States, and some countries lacked 2023 data.
Use was less widespread in policymaking and accountability: in 2025, 13 of 36 OECD countries (36%) reported AI use supporting policymaking, and 12 of 36 (33%) reported use to strengthen oversight and accountability. These figures point to variation by activity, not a ranking of government performance.
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In the United States, the Government Accountability Office (GAO) reported that AI use cases in inventories from 11 selected federal agencies rose from 571 in 2023 to 1,110 in 2024; generative-AI cases in those inventories increased from 32 to 282. These are agency-submitted inventory counts reviewed by GAO, not a census of the federal government or proof that every listed case was operating in production. The OECD country rates and GAO inventory totals have different subjects and methods and should not be compared as if they shared a denominator. See GAO’s 2025 report on federal agencies’ generative-AI use.
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The OECD’s framework for trustworthy government AI groups the work into enablers, guardrails, and engagement. Its chapter on enablers, guardrails and engagement says: “Governments should take a systems approach and seek to anticipate future changes.” That is a call to build for adaptation, not to make every system identical.
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| Shared across government | Designed for the specific mission |
|---|---|
| Governance roles and escalation routes | The public problem and the process AI is meant to improve |
| Data management, infrastructure, and security practices | Whether the available data are relevant, representative, and fit for the task |
| Workforce skills, procurement expertise, and investment capacity | Acceptable error, human review, and remedies for people affected |
| Common expectations for transparency, accountability, and risk management | How performance, consequences, and failure modes are monitored in context |
| Routes for partnerships and engagement | Which citizens, civil servants, and domain experts must be involved |
OECD recommends considering each government’s context and maturity rather than trying to apply every measure at once. Shared infrastructure can reduce duplicated effort, but it does not replace mission owners’ responsibility for data quality, service outcomes, or the effects of a decision.
How to judge whether an AI use case is ready to scale
- Start with the public problem. Describe the service or administrative process, the people it affects, and the outcome to improve. Check whether the process itself needs redesign before automating it; automation can reproduce an inefficient workflow.
- Classify the task and its stakes. Distinguish structured tasks, such as sorting or extracting information, from decisions involving contested judgments, eligibility, policy, or accountability. The latter generally call for more demanding governance, data scrutiny, and human oversight.
- Test mission fit and data readiness. Identify what data the task depends on, whether those data are available and suitable, and how privacy, transparency, and representation concerns will be handled. A shared platform does not make an unsuitable dataset appropriate.
- Set controls proportionate to consequences. Define who is accountable, how performance will be measured, when a person must review or override an output, and how problems can be reported and corrected. GAO’s AI accountability framework organizes practices around governance, data, performance, and monitoring.
- Involve users and staff. Engage affected citizens and civil servants during design and use. Their experience can reveal service barriers, operational constraints, and harms that a technical evaluation alone may miss.
- Scale the capability, not an unexamined decision rule. Reuse infrastructure, procurement knowledge, and oversight practices where appropriate, but reassess the model, workflow, and controls for each new mission. Continue monitoring as technology and context change.
Examples show range, not proven outcomes
The OECD describes government applications including chatbots for citizen questions and form filling, AI to anticipate and respond to disasters, and tax fraud detection. GAO’s selected federal examples include a Veterans Affairs medical-imaging automation effort and a Department of Health and Human Services effort to extract information from publications to identify possible poliovirus outbreaks. These reports establish that such use cases have been described; they do not, by themselves, establish measured benefits or successful production outcomes.
Why implementation is difficult
Scaling requires more than choosing a model. OECD identifies skills shortages, legacy systems, data availability, financial constraints, and demanding privacy, transparency, and representation requirements as barriers. GAO also reports agencies’ difficulty keeping pace with rapid technological change while complying with policy, along with concerns about technical resources, budgets, and up-to-date use policies.
Oversight is itself a coordination challenge. GAO’s September 2025 report identified 94 AI-related requirements with government-wide scope or implications, counted as of July 2025, and 10 executive-branch groups with an AI oversight or advisory role. Requirements and arrangements can change; the counts describe what GAO identified at that time, not a permanent total. See GAO’s report on federal AI requirements and oversight.
These constraints make a shared foundation valuable, but they also explain why a blanket rollout is unlikely to be responsible. Agencies need enough common capacity to avoid rebuilding basic controls, while retaining the expertise and authority to decide whether a use fits their mission and what safeguards it requires.
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