The U.S. Government Accountability Office's (GAO) May 29 report identifies 54 open recommendations under the Department of Defense (DoD) Chief Information Officer's purview. They span cybersecurity, information-technology acquisition, business-systems modernization, and financial management.
It is tempting to treat that list as legacy administration while attention shifts to artificial intelligence and autonomy. That would be a mistake. The unresolved management system is part of the architecture on which new capability has to run.
Revised and substantially expanded July 17, 2026, with the subsequent change in federal AI policy reflected explicitly.
Executive Order 14110 was remarkable in scope. Issued in October 2023, it assigned artificial-intelligence actions across a wide portion of the federal government: safety and security, privacy, civil rights, consumer protection, workforce, innovation, competition, international leadership, federal use, and technical standards. Many assignments carried deadlines measured in days or months.
By early 2024, progress was naturally reported as a sequence of completed actions. Agencies had issued requests for information, convened experts, begun standards work, created hiring pathways, launched pilots, and prepared guidance. The original version of this post praised that momentum but did little to explain what “progress” should mean.
The later policy record creates a useful natural experiment. In January 2025, a new administration revoked Executive Order 14110 and established a different federal AI policy. OMB subsequently replaced core agency-use and acquisition guidance. Yet many technical problems, statutory obligations, agency missions, and organizational constraints remained.
This reveals the correct unit of progress. It is not the number of executive-order tasks marked complete. It is the amount of durable state capacity created: people, evidence, standards, architectures, acquisition mechanisms, data, evaluation systems, and decision processes that remain useful when the policy language changes.
A label on a political advertisement can tell us that artificial intelligence helped make it. It cannot tell us whether the message is true, who authorized the representation, how materially the content was altered, or whether millions of people saw it before the label appeared.
That is the problem the Artificial Intelligence (AI) Transparency in Elections Act tried to address in 2024. Senate Bill 3875 would have directed the Federal Election Commission (FEC) to require disclosures when covered political communications contained content “substantially generated” by AI. The bipartisan proposal recognized a real gap: voters could encounter a synthetic voice, image, or video without knowing that part of the apparent evidence had never occurred.
The bill advanced out of committee and reached the Senate calendar, but it did not become law before the 118th Congress ended. The later record makes the underlying design question more useful, not less. What would an effective disclosure regime need to accomplish—and what should no one expect a label to solve?
In late 2023, twenty civilian agencies reported roughly 1,200 current and planned uses of artificial intelligence. It was an irresistible number. Read quickly, it sounded like proof that artificial intelligence (AI) was spreading across the federal government at scale.
The U.S. Government Accountability Office (GAO) found something more complicated. Only five of the twenty agencies provided complete information for every reported use case. Two inventories included systems that the agencies later determined were not AI at all. Many entries lacked basic information such as lifecycle stage or whether the use could be publicly released.
The inventory documented attention. It did not establish adoption, readiness, value, or control.
That distinction remains important because federal AI inventories have continued to grow. The number of ideas in a spreadsheet will always rise faster than the number of capabilities that survive acquisition, integration, evaluation, workforce adoption, and sustained operation. Leaders need to know which kind of growth they are looking at.
A good artificial intelligence portfolio begins with permission to say that artificial intelligence (AI) is not the answer.
That sounds obvious. In practice, organizations often begin in the opposite place. A new model becomes available, leaders announce an adoption goal, and teams are asked to find use cases. The search produces a familiar list—summarization, forecasting, chatbots, anomaly detection, document review—before anyone has defined the mission problem, the current baseline, or the decision the system is supposed to improve.
The result may be technically interesting. It is not yet a strategy.
A federal AI use case should be written as a testable claim: for a defined group of people, performing a specific mission task under known conditions, this capability will improve a measurable outcome enough to justify its cost and risk. If the claim cannot be stated, challenged, and evaluated, the agency does not have a use case. It has a technology theme.
Federal artificial intelligence (AI) policy changed substantially between 2024 and 2025. The need to know which systems government uses, who owns them, how they affect people, and what evidence supports them did not.
That is the enduring idea behind the Federal AI Governance and Transparency Act. Introduced as House bill H.R. 7532 in March 2024, the bipartisan proposal would have consolidated several federal AI governance requirements in statute. It directed agencies to create governance charters for certain systems, strengthened the Office of Management and Budget's government-wide role, expanded public visibility, and required contractors to provide information agencies would need for oversight.
Its most useful contribution was not a particular form or office. It was the recognition that accountable AI requires an institutional memory: a durable connection between the system, its public purpose, the decisions made about it, and the evidence available to challenge those decisions.
By the time a Freedom of Information Act request reaches an analyst, the hardest problem may already be years old.
The relevant records may be scattered across email, shared drives, case systems, collaboration tools, contractor environments, and personal filing habits. The request may cross several components with different search practices. Reviewers may need to reconstruct context that was obvious when the work occurred but disappeared when people moved on.
That is the operating reality behind the 2024 NextGen FOIA Tech Showcase. The Chief Freedom of Information Act (FOIA) Officers Council invited vendors to demonstrate artificial intelligence (AI), electronic discovery, search, case-processing, and redaction tools that might improve federal disclosure work.
The technology can help. It cannot repair missing records, unclear ownership, inconsistent retention, or a workflow no one has measured from end to end. If agencies treat FOIA as a document-processing problem that begins when a request arrives, they will automate the visible end of a much larger knowledge-management failure.
More orbital data does not automatically produce a better collision warning.
A useful warning depends on the quality and timing of observations, the model used to estimate an object's orbit, the uncertainty attached to that estimate, the way several sources are combined, and whether a satellite operator can act on the result. Two providers can observe the same object and produce different answers without either behaving irrationally.
That is what made a small 2024 procurement by the Office of Space Commerce (OSC) more interesting than the original announcement suggested. OSC, part of the National Oceanic and Atmospheric Administration (NOAA), hired Kayhan Space and SpaceNav to evaluate the accuracy, consistency, and quality of commercial space situational awareness (SSA) products created by three other companies. The work supported a limited pathfinder for the emerging Traffic Coordination System for Space (TraCSS).
The government was not only buying data. It was buying an independent way to learn which data and services were useful, under which conditions, and according to which evidence.