COOKED
The bulk of the job — consolidating departmental budget requests, building spreadsheet models, running variance and trend analysis, and writing the narrative justifications that accompany budget documents — is exactly the text-and-tables work current AI does at usable quality once it is wired to the ERP and general ledger. What persists is the political and adversarial layer: challenging a department head's headcount ask, defending a number in front of a city council or CFO, and owning the call when revenue assumptions break mid-year. Most budget analysts work in government, where entrenched processes, appropriation law, and civil-service structures will slow the transition considerably even though the tasks themselves are solvable.
This fall is concentrated in 2020 and has not recovered since.
Median pay $76,540 → $91,640 -4.2% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+1% 50,400 → 51,000 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +1% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~3,100 openings a year on average, including replacing people who leave.
Cost AnalystFiscal AgentBudget AnalystBudget OfficerFiscal AnalystFiscal OfficerBudget EngineerBudget ExaminerCost AccountantProgram AnalystBudget SpecialistBudget CoordinatorFiscal Budget AnalystProgram Budget AnalystStaff Services AnalystBudget Planning AnalystBudget and Policy AnalystBudget Management AnalystFinancial Services OfficerFinancial Management AnalystProgram Cost Control AnalystBudget Administrator (Budget Admin)Financial Planning and Analysis AnalystProgram Cost Schedule and Control Analyst
Holding it up: judgment & accountability . Weakest point: embodiment .
Mixed — a routine tier and a judgment tier At 7 the score credits the parts that survive — the closed-door negotiation with a program manager who padded their request, and the interpretation of appropriation language mid-execution — but the consolidation of departmental submissions, the recurring variance report, the funds-availability check against the encumbrance ledger, and the first draft of the budget message are all reproducible from structured GL data, which is most of a fiscal-year cycle.
Fully desk- and screen-based A 2 rather than a 0 reflects that budget hearings, department walkthroughs, and end-of-quarter closing sessions put you in a room rather than at a screen, but nothing about the work requires your hands or your presence at a physical site.
No licence, no signature requirement CGFM or CDFM is resume decoration, not a legal precondition — no statute requires a credentialed budget analyst to certify an appropriation request, and when funds are overspent it is the agency head or the CFO who answers to the auditor and, under the Antideficiency Act, faces the reportable violation.
Meaningful discretion An 11 reflects genuine discretion — deciding which requests are unfundable, recommending a mid-year reduction when sales-tax receipts fall short, choosing a revenue assumption you will have to defend in a public hearing — while acknowledging that you recommend rather than decide, and the appropriation ordinance, budget instructions, and OMB Circular A-11 or equivalent fix the format and much of the substance before you start.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (7/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (3/20) is whether the law requires a licensed human to sign. Trust premium (8/20) is whether buyers specifically pay for a person. Judgment and accountability (11/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 22 of this occupation's 31 points (71%).
Embodiment (2/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
Accountants and Auditors EXPOSED
Financial Managers EXPOSED
Actuaries EXPOSED
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 44/100 — EXPOSED.
Genuine two-tier structure: if consolidation, variance runs, and narrative drafting are absorbed by ERP-embedded copilots (Workday Adaptive, Oracle EPM Planning already ship these), the residual job becomes adversarial negotiation with department heads, council testimony, and mid-year reforecast decisions when revenue breaks. Task_resistance rises mechanically as the routine tier leaves, no law needed — watch for job postings emphasizing 'business partnering' over model-building.
If GFOA or state local-government finance statutes formalize a named 'budget officer of record' who must personally certify revenue assumptions and the balanced-budget calculation — as some states already require for county/municipal budget certification (e.g., NC's Local Government Budget and Fiscal Control Act designating a budget officer) — and that certification is extended to explicitly cover AI-generated forecasts, the role's ownership of consequential calls under ambiguity hardens rather than dissolves.
If GASB or state auditors issue guidance requiring documented human review and attestation of machine-generated budget estimates before adoption — analogous to the AICPA/PCAOB push on auditor use of automated tools — a signature step attaches to a named analyst. Note this is weak: the certifying official is usually the finance director or CFO, not the analyst, so the shield lands one level above.
If federal appropriations law or OMB circular guidance (A-11) is amended to prohibit sole reliance on automated tools for apportionment and anti-deficiency determinations, the Antideficiency Act's personal-liability exposure attaches to human budget staff decisions explicitly.
The limit. No plausible route to a higher trust premium: budget documents are consumed by councils, boards, and OMB reviewers who care about the numbers and the accountable signer, not about whether a human drafted the narrative. Nobody pays extra for a human-authored variance report. Even with every lever above, this stays a low-60s occupation at best — the underlying work product is text and tables, and the surviving value is a political and legal role that a much smaller number of people can hold. Expect headcount compression regardless of score.
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 4,100 | $125,050 +36% |
| New York-Newark-Jersey City, NY-NJ | 1,950 | $96,340 +5% |
| Atlanta-Sandy Springs-Roswell, GA | 1,170 | $99,880 +9% |
| Baltimore-Columbia-Towson, MD | 1,150 | $96,150 +5% |
| Los Angeles-Long Beach-Anaheim, CA | 1,070 | $96,870 +6% |
| Boston-Cambridge-Newton, MA-NH | 930 | $98,330 +7% |
| Seattle-Tacoma-Bellevue, WA | 910 | $104,090 +14% |
| Dallas-Fort Worth-Arlington, TX | 810 | $82,790 -10% |
| Iowa City, IA | 40 | $134,850 +47% |
| San Jose-Sunnyvale-Santa Clara, CA | 340 | $131,730 +44% |
| Ann Arbor, MI | 90 | $127,860 +40% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 31. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.