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AI is useful for navigating the federal budget, not for certifying what every dollar means. Give a model well-labeled budget tables, spending files, and links to the underlying government documents, and it can organize thousands of pages, summarize programs, produce consistent comparisons, and flag unusual patterns. A person must still choose the right source, check definitions and arithmetic, and decide what the evidence does—or does not—show.
The safest workflow is source-first: define a question that can be checked, select the official record that answers it, preserve the file’s metadata and caveats, use AI for retrieval and analysis assistance, then independently verify every important claim. This matters because a President’s proposal, a congressional appropriation, an agency obligation, and a Treasury-reported outlay are different things.
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What the federal budget measures
The federal fiscal year runs from October 1 through September 30. Annual budget discussions generally divide spending into mandatory programs, discretionary programs, and interest on the debt. USAGov characterizes mandatory spending as typically more than half of total funding, discretionary spending as usually about one-third, and interest as usually under 10 percent. Those are rounded explanations, not a substitute for a dated budget table with a stated measure and denominator.
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|---|---|---|
| President’s budget proposal | The administration’s requested priorities and estimates submitted to Congress. | It is a plan, not enacted authority or proof that money was spent. |
| Appropriation or other enacted funding | Funding authority passed by Congress and signed into law, subject to the terms of the law. | It establishes legal authority, which may differ from the request and from eventual use. |
| Obligation | A legally binding commitment, such as an award or contract, recorded by an agency. | An obligation is not the same as cash paid or a completed project. |
| Outlay | A payment made from the Treasury. | Payment timing can differ substantially from the year in which funds were appropriated or obligated. |
The process starts roughly a year before a fiscal year: agencies send requests to the Office of Management and Budget (OMB), OMB develops the President’s proposal, the President submits it to Congress early in the next calendar year, committees and subcommittees consider funding, and Congress sends funding bills to the President. AI can map that process or compare documents, but it cannot turn a proposal into law or infer policy intent from a number alone.
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Begin with a question a record can answer
Vague prompts such as “Where does the government waste money?” invite the model to invent a category or treat a correlation as a conclusion. Rewrite the question so another analyst could reproduce it from named files.
Good starting questions
- How did an agency’s recorded obligations change between fiscal years 2022 and 2024?
- Which programs appear in a specified spending category in the President’s latest proposal?
- What awards make up a selected agency’s reported outlays during a stated period?
- Which rows in a downloaded table changed unusually after controlling for fiscal year, agency scope, unit, and measure?
Fix the scope before collecting data
- Name the agency, bureau, account, program, award type, or government-wide category.
- State the fiscal years and the date on which data will be downloaded.
- Choose the measure: requested, enacted, obligated, or paid, and specify dollars, thousands, or millions.
- Decide whether nominal dollars are sufficient or whether an inflation adjustment is required.
- Define inclusion rules, such as whether transfers, refunds, de-obligations, and classified or undisclosed items are included.
If you cannot state those boundaries, an AI answer may sound precise while combining incomparable records.
Use the official source that fits the question
Different records answer different questions. A reliable workflow does not ask one website to stand in for every stage of the budget.
| Question | Primary material to use | What to record |
|---|---|---|
| What the administration proposed | The President’s budget and an agency’s budget justification. | Submission year, fiscal-year target, requested account, tables, and assumptions. |
| What Congress authorized | Enacted appropriations and related congressional materials. | Public law, account, limitations, rescissions, and explanatory language. |
| What agencies committed or paid | Treasury’s USAspending.gov transaction and award information. | Agency, award or program, obligation or outlay field, reporting period, and update date. |
| Why a number changed | The underlying agency tables, Treasury records, congressional documents, and explanatory notes. | Definition changes, reclassifications, supplemental appropriations, and reporting corrections. |
Never label a proposed amount as spending. In a prompt and in the final article, use the source’s own term—request, enacted amount, obligation, or outlay—and attach the fiscal year and units to every figure.
Preserve provenance before asking AI anything
Download or archive the source files where permitted, then create a small manifest. It prevents a model from silently mixing versions and makes a later correction possible.
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- Agency and bureau name.
- Fiscal year and reporting period.
- Download or update date.
- Document, table, API endpoint, or dataset name.
- Unit, currency basis, and whether figures are nominal or adjusted.
- Category definitions, filters, and any transformations you made.
- Known omissions, linkage warnings, revisions, and disclosure notes.
Keep the original file separate from cleaned data. Give each row a stable source location—such as a table and row label—so the model can point back to evidence instead of producing an unsupported citation.
A source-first AI workflow
- Write the testable question. Include the agency, years, measure, unit, scope, and comparison rule. Example: “Compare this bureau’s reported obligations in fiscal years 2022 and 2024, using the same account and nominal dollars; list the table rows used.”
- Collect the authoritative records. Use proposal documents for requested plans, enacted congressional material for legal authority, and USAspending.gov for transaction and award reporting. Save the extraction date and filters.
- Inspect the files yourself. Confirm headers, totals, footnotes, fiscal-year labels, and whether a column is an obligation, outlay, or another measure. Remove duplicate rows only when the source documentation supports doing so.
- Give the model bounded material. Upload or paste the relevant tables and definitions. Tell it not to use outside facts, to quote the table or page for each claim, and to write “the supplied material does not answer this” when evidence is missing.
- Ask for structure before interpretation. Have AI extract rows, normalize headings, make a comparison table, and list ambiguities. This is safer than asking for a narrative verdict first.
- Recompute arithmetic independently. Use a spreadsheet or script for sums, percentage changes, and inflation adjustments. Compare the independently calculated result with the model’s result and inspect every mismatch.
- Investigate flagged patterns. An unusually large change can reflect a supplemental appropriation, a new award, a reclassification, a reporting correction, or a missing file. Treat it as a lead, never as proof of fraud, waste, or intent.
- Write the limitations beside the result. State what the data represent, the time period, the scope, known completeness or linkage issues, and exactly what AI did in the workflow.
A constrained prompt pattern
Use a prompt that makes the model’s job auditable:
Using only the attached budget table and its footnotes, (1) list each program and amount, (2) identify whether each amount is proposed, enacted, obligated, or outlayed, (3) cite the table and row for every statement, (4) calculate no totals, and (5) say “not established by these files” when a definition or value is missing. Separate source facts from questions for a human analyst.For a second pass, ask the model to challenge its own extraction: “Find rows whose unit, fiscal-year basis, agency scope, or definition differs from the comparison rule.” This turns AI into a reviewer of structure rather than an oracle.
What USAspending.gov can—and cannot—tell you
USAspending.gov presents information drawn from agency financial systems and external government-wide reporting systems. It is a valuable public way to explore awards and reported spending, but its records are not automatically complete or perfectly comparable.
GAO found that, for the reporting period ending March 31, 2021, 83 of 101 agencies submitted data files on time. Nineteen of the 101 agencies that submitted data omitted a required file linking budget and award information. GAO also found hundreds of billions of dollars in obligations reported with program activity listed as “Unknown/Other.” These are historical findings for that reporting period, not a claim that current submissions have the same defects.
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Treasury has added transparency tools over time: an About the Data disclosure for some external-system data that had not been validated against OMB or Treasury standards was added in 2023; an updated Unlinked Awards section launched in 2024; and Treasury provided evidence in 2025 of an updated key for financial-assistance awards. Check the current site’s disclosures and the status of open recommendations before making a present-tense claim about completeness.
When an AI answer cites USAspending.gov, inspect the underlying agency submission, linkage status, reporting period, and category definition. A polished citation does not prove that the cited record supports the model’s interpretation.
Where AI adds real value
Organizing large document sets
A model can create an index of budget justifications, extract recurring account names, identify tables containing a requested term, and convert narrative footnotes into a checklist. This saves reading time while leaving the underlying pages available for verification.
Summarizing with controlled citations
AI can turn a lengthy program description into a short summary if you require page, table, or row references and preserve qualifiers such as “requested” or “estimated.” Ask for a separate list of unresolved definitions rather than allowing the model to fill gaps.
Making comparable views
After you set identical fiscal-year, agency, program, measure, unit, and inflation rules, AI can place rows side by side and identify which fields are missing. The arithmetic should still be performed or checked with deterministic tools.
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Finding anomalies for investigation
GAO’s April 2025 testimony said, “AI-enabled tools could also help by sifting through large volumes of data to find anomalous patterns or relationships.” An anomaly is a screening result. It becomes evidence only after a person checks source records, definitions, approvals, and context.
GAO’s July 2025 inventory illustrates growing federal interest: 11 selected agencies reported 571 AI use cases in 2023 and 1,110 in 2024. Reported generative-AI use cases increased from 32 to 282, about ninefold. Those counts describe inventories from the selected agencies, not all federal agencies and not successful deployments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The limits that should change your conclusions
Bad or irrelevant inputs
GAO states, “Introducing insufficient, unrelated, or bad data will make an AI model less reliable.” Missing linkage files, “Unknown/Other” classifications, inconsistent account definitions, and revisions can all produce a confident but wrong comparison.
Hallucinated facts and citations
A model may invent a page number, merge two similarly named programs, or cite a real document that does not contain the claimed figure. Open the cited record and search for the exact row, footnote, and unit. Treat a citation generated by the model as a pointer to check, not as proof.
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AI can describe that an account rose, but it cannot decide whether the increase was justified, whether a program achieved its objective, or whether a trade-off is acceptable. Those judgments require statutory context, performance evidence, and human values that a spending table does not encode.
Privacy, security, and operational constraints
GAO has reported agency challenges involving policy compliance, technical resources and budget, keeping appropriate-use policies current, privacy-policy obstacles, and rapidly changing technology. Do not paste nonpublic personal information, procurement-sensitive material, or controlled data into a model unless the organization has approved that system and handling method.
Fraud estimates are not waste findings
GAO cited an estimated $233 billion to $521 billion in annual federal losses to fraud, based on data from 2018–2022. That estimate is not a measured amount attributable to AI, and it is not a finding that the same amount represents budget waste. An anomaly model can prioritize a review; only documented investigation can establish misconduct.
How to make comparisons defensible
| Comparison check | Question to answer before publishing |
|---|---|
| Time basis | Are both values for the same fiscal-year definition and was the extraction date recorded? |
| Status | Are you comparing proposals with proposals, enacted amounts with enacted amounts, or recorded spending with recorded spending? |
| Measure and unit | Are both columns obligations, outlays, or another measure, and are both in dollars, thousands, or millions? |
| Scope | Do the agency, bureau, account, program, and award-type boundaries match? |
| Completeness | Were files timely, linked, and subject to any disclosure or validation warning? |
| Definitions | Could a reclassification, reporting-standard change, or new category explain the difference? |
| Inflation | If you describe a real increase, did you state the price index and base year used? |
If any answer is unknown, label the comparison as limited rather than presenting a false precision.
Common failure modes and practical fixes
| Failure | Why it happens | Fix |
|---|---|---|
| “Spending” mixes requests, obligations, and outlays. | The model treats familiar words as interchangeable. | Put the measure in every column heading and sentence; reject mixed-status totals. |
| A year-over-year jump is called waste. | An anomaly is mistaken for a conclusion. | Check appropriations, awards, reclassifications, corrections, and program changes first. |
| A single dashboard is treated as complete. | Convenience hides submission and linkage limitations. | Read current About the Data and submission disclosures and preserve their date. |
| The model supplies a plausible missing value. | Generative systems optimize for a fluent answer. | Require “not established” for absent fields and verify against the original table. |
| Totals do not reconcile. | Units, rounding, duplicate records, or fiscal-year filters differ. | Recompute from downloaded rows, document transformations, and explain rounding. |
| Sensitive files are uploaded casually. | Public and restricted material are handled the same way. | Use approved environments, minimize data, and remove personal or procurement-sensitive details. |
A worked, reproducible analysis pattern
Suppose you want to explain how a bureau’s obligations changed from fiscal year 2022 to fiscal year 2024. First, specify the bureau, account and award scope, nominal-dollar basis, and the date of extraction. Download the two matching USAspending.gov views and record the filters and disclosure notes. Next, obtain the agency’s budget justification and enacted appropriations to explain whether a new authority or account change could affect the result.
Have AI produce a row-level inventory with source locations and a separate list of unmatched categories. Independently sum the downloaded obligation fields. Then investigate the largest changes by opening the award or account records and checking whether the difference comes from a new award, a de-obligation, a supplemental law, a classification change, or incomplete linkage. The finished explanation should say “reported obligations in this filtered view,” name the fiscal years and extraction date, disclose any “Unknown/Other” or unlinked records, and distinguish observed change from an explanation that remains unproven.
How to report an AI-assisted result
Use a short methods note that a reader can understand:
- List the official documents or datasets, their fiscal years, and retrieval dates.
- Define each measure and unit; do not use “spending” as a shortcut for several measures.
- Describe filters, exclusions, inflation treatment, and arithmetic checks.
- Identify known completeness, linkage, validation, or classification limitations.
- State that AI organized, summarized, or flagged records, while a human verified source locations and calculations.
- Separate what the records establish from hypotheses requiring further investigation.
That transparency lets another reader reproduce the analysis and prevents a generated narrative from acquiring more authority than its underlying data.
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Use AI to reduce the mechanical burden of federal-budget research: find the right pages, structure messy tables, summarize documented facts, and surface questions worth investigating. Keep source selection, definitions, arithmetic, privacy decisions, and policy judgments under human control. The result is not an AI-certified answer; it is a traceable analysis whose claims remain tied to the official record and whose uncertainty is visible.
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