Budget Analysts
13-2031.00Median wage $91,640/yr47,160 employed (US)Rank #144 of 923 scored · top 16% by substitution
Examine budget estimates for completeness, accuracy, and conformance with procedures and regulations. Analyze budgeting and accounting reports.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution42
Exposure40
Augmentation76
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
13 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
8%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%41
panel mean rating 2.6/5 → substitution pressure 41/100
Technical feasibility todayw 20%37
panel mean rating 2.5/5 → substitution pressure 37/100
Cost vs. human wagew 15%41
panel mean rating 2.6/5 → substitution pressure 41/100
Adoption barriersw 20%inverted — strong barriers lower the score46
panel mean rating 3.2/5 (barrier strength) → substitution pressure 46/100
Sector adoption velocityw 10%46
panel mean rating 2.8/5 → substitution pressure 46/100
Task breakdown (13 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Review operating budgets to analyze trends affecting budget needs.
71CI 55–87 · exposure 70 · augmentation 88 · importance 4.2/5 · click for rater detail
Review operating budgets to analyze trends affecting budget needs.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Finance and accounting sectors have been early adopters of analytics automation; mid-to-large organizations routinely deploy dashboards and automated reporting tools for budget monitoring and trend analysis. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and professional services sectors show moderate-to-fast AI adoption for analytics, but public sector and many budget offices lag in deploying AI-driven trend analysis in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI strongly augments human analysts by automating routine data preparation and pattern detection, allowing them to focus on interpreting implications and recommending strategic adjustments rather than manually reviewing raw figures. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids in identifying spending trends, anomalies, and pattern shifts in operating budgets, enabling analysts to focus on interpretation and recommendations. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems can reliably extract budget data, identify trends via time-series analysis, and flag significant deviations or patterns with high consistency. A system could accomplish this entire workflow—data ingestion, trend detection, anomaly flagging, and report generation—in a fraction of human time at comparable quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can analyze budget data, detect trends, and generate summary insights, but interpreting organizational context and forecasting needs still requires human judgment and validation, so only partial time savings are realistic today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Budget analysis is typically subject to organizational sign-off and oversight requirements rather than legal licensing barriers. The main friction is organizational preference for human judgment in interpreting findings and organizational adoption inertia, not regulatory prohibition. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this specific analytical task, though organizational reliance on accountable human judgment for budget decisions creates moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Cloud-based analytics and AI inference cost pennies per analysis compared to an analyst's fully loaded hourly rate; at scale, the cost differential is more than an order of magnitude in favor of AI. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce time spent on data aggregation and pattern detection, but licensing, integration with financial systems, and required human review keep costs roughly comparable to analyst time for this specific subtask. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | BI and analytics platforms (Tableau, Power BI, modern accounting software) routinely perform budget trend analysis in production. While fully autonomous interpretation requires some human oversight, trend detection and anomaly identification are mature and widely deployed in organizations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Financial analytics and BI tools with AI features (e.g., anomaly detection, forecasting dashboards) are deployed in many finance departments, but full autonomous trend analysis with reliable interpretation is not yet standard practice. |
Compile and analyze accounting records and other data to determine the financial resources required to implement a program.
67CI 55–79 · exposure 62 · augmentation 88 · importance 4.1/5 · click for rater detail
Compile and analyze accounting records and other data to determine the financial resources required to implement a program.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Finance and accounting departments—especially in large organizations and public sector—have demonstrated rapid adoption of automated financial analysis and data processing tools. Pilot and production deployment of AI-assisted financial analysis is already common in information-heavy, digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and government sectors are adopting AI-assisted analytics at moderate pace, with pilots for automated reporting and forecasting increasingly common but full deployment for resource determination still limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI strongly augments budget analysts by automating data compilation, surfacing anomalies, and generating draft analyses, freeing analysts to focus on strategic resource allocation decisions and stakeholder communication. This maintains human oversight while dramatically increasing throughput and accuracy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids in aggregating, cleaning, and summarizing accounting records and can generate preliminary analyses, greatly boosting analyst throughput while humans validate final funding conclusions. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI can extract, compile, and analyze structured financial data with high reliability, identifying patterns and preparing summary analyses that would take a human analyst 50%+ of their time. However, determining resource requirements often requires domain judgment about program scope and organizational context that still benefits from human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can compile, summarize, and analyze structured financial data quickly, but determining resource requirements involves judgment, institutional context, and negotiation that still require human oversight and validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations may require human sign-off on budget recommendations for governance/liability reasons, there are no licensing requirements or legal mandates that a human must personally compile and analyze accounting records. Adoption is primarily constrained by organizational inertia rather than hard regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensure is typically required for this task, though organizational approval processes and accountability for budget decisions create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference on structured financial data costs pennies per task, with minimal integration overhead for standard accounting systems. This is at least an order of magnitude cheaper than a budget analyst's fully-loaded labor cost for equivalent analytical output. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can reduce time spent on data compilation and initial analysis significantly, but human review, validation against policy, and integration costs keep overall costs roughly comparable to analyst time for complete task execution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (financial analysis tools, spreadsheet AI, data analytics platforms) reliably perform data compilation and basic financial analysis at scale. Accounting record extraction and numerical analysis are mature; the remaining uncertainty involves interpretation of edge cases in resource requirement determination. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Financial analysis and data-aggregation tools (e.g., AI-enhanced spreadsheet/BI tools) are deployed in production, but fully autonomous determination of program funding needs is not yet reliably automated in real budget offices. |
Analyze monthly department budgeting and accounting reports to maintain expenditure controls.
62CI 50–75 · exposure 62 · augmentation 88 · importance 4.4/5 · click for rater detail
Analyze monthly department budgeting and accounting reports to maintain expenditure controls.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Financial services, large corporations, and accounting departments are among the early adopters of AI analytics tools. Many organizations already deploy AI-assisted expense monitoring and variance reporting, with measured productivity gains and growing displacement in routine analysis roles. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and government back-office functions are adopting AI-assisted analytics steadily, but broad production deployment specifically for budget control monitoring remains at pilot-to-moderate stages. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at rapidly ingesting multi-source financial data, auto-categorizing entries, flagging anomalies, and generating preliminary reports, which materially accelerates human analysts' ability to focus on exception handling, trend interpretation, and strategic recommendations. This is a high-productivity-gain assistive scenario. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up report analysis, flag outliers, and draft variance explanations, meaningfully boosting analyst productivity while they retain oversight and interpretation responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can reliably extract, categorize, and analyze expense data from structured reports, flag variances, and generate variance summaries with high accuracy. While some nuanced judgment about root causes may require human input, the core analytical work of comparing actuals to budget and identifying deviations can achieve >50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can process and flag anomalies in structured financial reports and generate variance summaries, but interpreting context, exceptions, and organizational nuance still requires human judgment, so only partial time savings are achievable off-the-shelf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Finance and accounting roles often carry organizational expectations that a human analyst review and sign off on budget controls; regulatory compliance (SOX, internal controls) may require human accountability and sign-off, creating friction against full automation despite technical capability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this, but organizational accountability for budget sign-off and internal control policies create some institutional friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven financial analytics inference costs are very low per report analyzed, and integration into existing accounting systems is increasingly standard. All-in costs per analysis are typically well below the loaded wage of a budget analyst performing the same work, particularly at scale. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can cheaply generate summary reports and flag variances, but the cost of integration, data cleaning, and required human review keeps overall cost roughly comparable to analyst time for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature accounting software and AI-powered financial analytics platforms (e.g., Alteryx, Workiva, SAP analytics) routinely perform variance analysis and expenditure control monitoring in production environments at scale. Error rates on structured data are low, though edge cases and policy interpretation may need human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Financial analytics and BI tools with AI-assisted anomaly detection and reporting exist in production (e.g., ERP add-ons, Power BI/Tableau with AI features), but fully autonomous expenditure control analysis is not yet standard practice. |
Examine budget estimates for completeness, accuracy, and conformance with procedures and regulations.
60CI 50–70 · exposure 62 · augmentation 88 · importance 4.1/5 · click for rater detail
Examine budget estimates for completeness, accuracy, and conformance with procedures and regulations.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Finance and government sectors are actively deploying compliance and document-review automation; budget offices and CFO organizations show measurable pilot and production adoption of AI-driven audit and validation tools. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and public-sector budgeting are moderately digitized with growing use of AI for anomaly detection and compliance checks, but adoption is uneven and often pilot-stage in government contexts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at surfacing inconsistencies, flagging missing fields, and highlighting deviations from regulatory requirements, dramatically accelerating a budget analyst's review process and reducing human error while the analyst retains final judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up first-pass reviews by highlighting inconsistencies, missing fields, or noncompliant items, letting analysts focus on judgment-intensive verification, meaningfully boosting productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can reliably extract, validate, and cross-reference budget data against regulatory frameworks and procedural checklists with high accuracy, achieving significant time savings on the checking and comparison phases. However, judgment calls on marginal conformance cases or novel regulatory interpretations may still require human review. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can check arithmetic, cross-reference figures against templates, and flag policy/regulatory inconsistencies, but full judgment on completeness and contextual accuracy still requires human review, especially for edge cases and ambiguous line items. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no license is required to operate the AI system itself, organizational oversight requirements, audit accountability for the AI's outputs, and the regulatory expectation that qualified personnel sign off on budget approval create moderate friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no licensing requirement mandates a human budget analyst per se, government and organizational budgets often require sign-off by an authorized official, creating moderate procedural and accountability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Inference and integration costs for automated compliance checking are a small fraction of the loaded cost of a budget analyst performing line-by-line manual review, particularly when amortized across multiple budgets or agencies. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can cheaply flag anomalies and formatting issues, but the need for human verification of substantive accuracy and procedural conformance keeps overall cost savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production-grade document processing and compliance-checking systems are deployed in finance and government contexts today; they handle budget review workflows with mature OCR, data extraction, and rule-matching pipelines. Minor limitations remain in ambiguous edge cases or non-standard document formats. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI-powered spreadsheet auditors and compliance-checking tools exist and are used in finance/accounting, but budget-specific completeness and regulatory conformance checks are less mature and often require custom rules or fine-tuning. |
Perform cost-benefit analyses to compare operating programs, review financial requests, or explore alternative financing methods.
56CI 50–61 · exposure 50 · augmentation 88 · importance 3.7/5 · click for rater detail
Perform cost-benefit analyses to compare operating programs, review financial requests, or explore alternative financing methods.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Government, finance, and large corporate sectors—where budget analysts concentrate—are actively deploying financial analytics and decision-support automation. Adoption is already mid-to-advanced in digitized organizations, with pilots and production use commonplace in federal budgeting and enterprise finance. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and government budgeting functions are adopting AI-assisted analytics and forecasting tools at a moderate pace, with pilots common in agencies and corporations but full production deployment for this specific task still developing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI strongly augments budget analysts by automating data synthesis, stress-testing scenarios, and sensitivity analysis, allowing humans to focus on judgment, stakeholder communication, and strategic recommendation. This is one of the clearest augmentation cases in financial analysis work. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly speeds up data gathering, scenario modeling, and drafting of comparative analyses, letting analysts focus on judgment calls and stakeholder communication, meaningfully raising productivity while humans stay in control. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions of cost-benefit analysis—data collection, quantitative modeling, scenario comparison, and report generation—but typically requires human judgment on assumptions, trade-offs, and strategic interpretation. This meets the ~50% time-saving threshold for routine analyses, though complex multi-stakeholder decisions remain semi-manual. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly compute cost-benefit scenarios and draft comparisons given structured data, but the task requires judgment on program-specific tradeoffs, sourcing assumptions, and stakeholder-specific financing options that need human validation and setup effort. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations have governance or sign-off requirements, there is no universal legal or licensing barrier preventing automation of cost-benefit analysis itself. Customer or stakeholder preference for human-reviewed conclusions creates friction, but not a hard block on substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human budget analyst specifically, but organizational approval processes, accountability for public/organizational funds, and internal governance create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and integration costs (even with data cleanup and scenario modeling) are substantially below the loaded wage of a budget analyst, especially once amortized over multiple analyses. Oversight is minimal for routine comparisons, pushing the ratio strongly in AI's favor. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce analyst time on data compilation and scenario modeling substantially, but human review, data validation, and contextual judgment remain necessary, keeping all-in costs only moderately below fully-loaded analyst wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed tools (financial modeling software, BI platforms, some specialized advisory systems) can perform quantitative cost-benefit comparisons reliably, but they require careful human setup of parameters and validation of inputs. Error rates remain material when assumptions are novel or context-specific, limiting production-scale deployment without oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Financial analysis copilots and spreadsheet AI tools (e.g., Excel Copilot, various FP&A platforms) exist and are used in production, but comprehensive autonomous cost-benefit analysis across alternative financing methods with reliable accuracy is still narrow and heavily supervised. |
Summarize budgets and submit recommendations for the approval or disapproval of funds requests.
42CI 34–50 · exposure 45 · augmentation 75 · importance 4.1/5 · click for rater detail
Summarize budgets and submit recommendations for the approval or disapproval of funds requests.
42| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Financial and government sectors have been slow to deploy autonomous budget AI in production; most use cases remain pilot-stage analytical assistants rather than decision-makers. Legacy systems, risk aversion, and governance complexity limit rapid adoption. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and professional services generally show above-average AI adoption, but budget analysis in government and corporate finance departments tends to move cautiously due to compliance and accountability concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at summarizing large budget documents, flagging outliers, and surfacing patterns in spending, substantially raising analyst productivity in the research and drafting phase. The human analyst remains the final decision-maker but with significantly accelerated input and analysis. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly speed up drafting of budget summaries, highlighting variances, and generating first-pass recommendation text, meaningfully boosting analyst productivity while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist with budget summarization and generate initial recommendations based on financial rules and historical patterns, but final approval decisions require human judgment, institutional knowledge, and accountability. Automation of roughly half the clerical/analysis work is plausible with significant setup, but the full task including recommendation rationale that withstands institutional scrutiny falls short of 50% time saving. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft budget summaries and generate recommendation language from structured financial data, but the final judgment on approval/disapproval requires contextual organizational knowledge and accountability that current systems can't fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Budget approval carries explicit fiduciary and compliance responsibility; many organizations require a licensed or designated human to sign off on fund recommendations. Regulatory requirements, organizational governance, and liability concerns create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not always legally mandated, budget approval recommendations typically require accountable human sign-off within organizational governance and audit structures, creating moderate institutional friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Initial setup costs for AI integration into budget systems are substantial, and human oversight remains necessary to validate recommendations and manage liability. The all-in inference plus integration plus oversight cost approaches or exceeds the human analyst wage for this task. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply generate drafts and summaries, but the human review, validation against policy, and sign-off required keep total task cost roughly comparable to a skilled analyst's time when done properly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (financial analysis software, LLM-based summarization tools) that can extract budget data and flag anomalies, but no mature system reliably generates approvable recommendations end-to-end without substantial human oversight. Scope is often narrow and error rates on complex institutional rules remain material. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some finance software and LLM-based tools can summarize budget data and flag anomalies, but no mature deployed product reliably produces submission-ready funding recommendations across varied organizational contexts without heavy human revision. |
Consult with managers to ensure that budget adjustments are made in accordance with program changes.
34CI 28–41 · exposure 28 · augmentation 75 · importance 4.1/5 · click for rater detail
Consult with managers to ensure that budget adjustments are made in accordance with program changes.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Finance and budget management are moderately digitized, with growing adoption of automated reporting and decision support tools. However, adoption remains in the pilot or decision-support phase rather than autonomous replacement, consistent with middling velocity across public and private sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and budgeting functions are adopting AI tools for analysis and forecasting at a moderate pace, but the consultative, interpersonal aspect of this task sees slower uptake. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist budget analysts by automatically identifying program changes, suggesting affected budget categories, and generating adjustment scenarios. This substantially raises analyst productivity in reviewing and consulting on budget changes while the human retains judgment and sign-off authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can help analysts prepare data, model scenarios, and summarize program change impacts prior to and during discussions with managers, meaningfully boosting productivity even though humans remain central. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can potentially automate 40-60% of this task by analyzing program change documents, flagging budget line items needing adjustment, and generating recommended changes. However, the consultative aspect and need for nuanced judgment about program-budget alignment requires significant human oversight, preventing full end-to-end automation at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires interactive consultation, negotiation, and contextual judgment about organizational program changes that current AI cannot conduct end-to-end without heavy human involvement.》 It could assist with data prep but not replace the consultative process.》 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Budget adjustments often require human sign-off from managers and may involve policy compliance or approval hierarchies that prefer human judgment. Organizational processes typically embed human review requirements, though these are procedural rather than strictly legal barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing barrier, but organizational trust, accountability for budget decisions, and the need for interpersonal negotiation with managers create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Document analysis and reporting tools cost less than a budget analyst's loaded wage, but the need for setup, integration with budgeting systems, and human validation of recommendations limits cost advantage. Full savings are not realized since consultation and final approval remain human tasks. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI cannot substitute for the human consultation itself, so cost savings are limited to peripheral analysis support rather than the core task, keeping cost comparable to human-driven work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While document analysis and change-tracking tools exist, no mature product reliably performs the full consultation and decision-making process of ensuring budget adjustments match program changes in production environments. Most deployments remain at the analytical support level rather than autonomous decision-making. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously conducts managerial consultations to align budget adjustments with program changes; this remains a human relationship-driven activity. |
Provide advice and technical assistance with cost analysis, fiscal allocation, and budget preparation.
32CI 28–37 · exposure 30 · augmentation 75 · importance 4.3/5 · click for rater detail
Provide advice and technical assistance with cost analysis, fiscal allocation, and budget preparation.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Finance and accounting sectors are digitizing rapidly, and spreadsheet/BI tools are widely deployed, but AI-driven budget automation in production is still in the pilot-to-early-adoption phase in most organizations rather than standard practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Public sector and corporate finance functions are adopting AI-assisted analytics at a moderate pace, with pilots common but full production deployment for advisory tasks still limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting budget analysts by automating data gathering, generating preliminary cost breakdowns, scenario modeling, and formatting; these assistive capabilities can meaningfully boost analyst productivity while humans remain responsible for judgment and recommendations. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up cost modeling, scenario analysis, and report drafting, giving analysts substantial productivity gains while they retain judgment and oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with cost calculations, data aggregation, and budget formatting, the task requires substantive judgment about fiscal allocation priorities and organizational strategy that remains heavily human-dependent. Current systems cannot reliably replace the full advisory and decision-support role at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This task centers on advisory judgment, stakeholder negotiation, and contextual fiscal knowledge that current AI can support but not independently deliver at equal quality end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Budget advice and allocation decisions often require sign-off from authorized finance officers and may fall under fiduciary or regulatory compliance frameworks; organizational finance governance typically mandates human accountability and review of fiscal recommendations, creating structural adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed like accounting/legal roles, budget decisions carry organizational accountability and approval chains that create friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce certain computational and data-wrangling costs within budgeting workflows, but does not yet deliver the full advisory output of a human budget analyst at materially lower total cost when accounting for validation, correction, and context-setting overhead. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Because human oversight and contextual judgment remain necessary, AI mainly supplements rather than replaces the analyst, keeping cost savings modest rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Budget analysis tools and spreadsheet automation exist in production, and some GenAI systems can draft cost analyses and summaries. However, the advisory and technical-assistance dimensions require domain expertise and context-specific recommendations that current products handle inconsistently or with significant oversight needs. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can assist with spreadsheet analysis and drafting summaries, but no deployed product independently provides organization-specific budget advice and fiscal allocation guidance reliably. |
Seek new ways to improve efficiency and increase profits.
31CI 25–38 · exposure 25 · augmentation 75 · importance 3.4/5 · click for rater detail
Seek new ways to improve efficiency and increase profits.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While organizations use AI for financial analysis and reporting, adoption of AI for generating novel strategic recommendations remains limited; most firms treat this as a human-led process with AI support rather than AI-driven. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and budgeting functions are adopting AI analytics tools at a moderate pace, with pilots for forecasting and cost analysis common but full strategic automation rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly augment budget analysts by rapidly analyzing datasets, identifying patterns, generating benchmarking comparisons, and surfacing optimization opportunities that analysts then evaluate and refine, substantially raising their ability to propose novel efficiency improvements. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can analyze large datasets, model scenarios, and suggest efficiency opportunities, meaningfully speeding up the ideation and analysis phase for analysts who retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires strategic creativity and judgment about novel approaches to business optimization. While AI can analyze data and suggest incremental improvements, identifying genuinely new ways to improve efficiency and profits involves business insight, organizational knowledge, and innovation that requires human judgment. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires open-ended strategic thinking, organizational context, and judgment about tradeoffs that current AI cannot autonomously originate or validate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strategic decisions about efficiency and profitability typically require organizational sign-off and human judgment on risk and implementation feasibility. Budget analysts' recommendations are usually reviewed by management, creating a natural governance barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but organizational trust, accountability for financial recommendations, and need for contextual buy-in create moderate friction against full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of deploying AI tools for this task, including setup, integration, and human oversight of strategic decisions, likely exceeds or approximates the cost of a budget analyst performing it, since meaningful novelty still requires human direction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Analysis support from AI is cheap, but the human judgment, stakeholder buy-in, and contextual validation needed still require substantial analyst time, keeping costs comparable to a full human process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this open-ended strategic task end-to-end. AI can assist with data analysis and benchmarking, but current systems struggle with identifying and evaluating novel business strategies at the level required for independent execution. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can surface data patterns or suggest cost-saving ideas, but no deployed product independently generates and validates efficiency/profit strategies reliably in production. |
Direct the preparation of regular and special budget reports.
29CI 25–32 · exposure 25 · augmentation 75 · importance 4.0/5 · click for rater detail
Direct the preparation of regular and special budget reports.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Finance and government sectors use BI dashboards and data tools, but human-directed budget planning remains high-touch; most organizations have not shifted to AI-directed budget report production, and adoption remains at the pilot or limited-automation stage. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and budgeting functions are adopting AI for drafting and analysis at a moderate pace, though the supervisory/directive aspect lags behind more transactional automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist budget analysts by automating data pulls, generating preliminary report sections, flagging variances, and formatting—enabling humans to focus on interpretation, strategy, and sign-off. This is an area of active and productive augmentation in financial planning software. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly assist by drafting report templates, aggregating data, and flagging anomalies, boosting the analyst's productivity while they retain directive control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data aggregation, formatting, and routine sections of budget reports, directing the preparation—which involves strategic planning, prioritization, and decision-making about what to report and how—requires human judgment and accountability that current systems cannot fully replicate end-to-end without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Directing report preparation involves managerial oversight, coordination, and judgment calls about content and priorities that current AI cannot autonomously perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Budget oversight carries legal, fiduciary, and audit responsibility; financial regulations (SOX, COSO, etc.) typically require human sign-off and accountability for budget direction and integrity. A licensed or designated budget official must legally own the quality and assumptions in prepared reports. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but organizational hierarchy, accountability for budget accuracy, and need for human judgment in directing staff create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted report generation can reduce manual effort on clerical parts of the task, but the cost of oversight, validation, and correcting strategic misalignments often remains comparable to or higher than the savings from automating routine sections, especially given the accountability requirements in budgeting. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply generate draft text or data summaries, the managerial direction component still requires human time, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably directs budget report preparation autonomously; existing BI and reporting tools require human managers to set direction, validate outputs, and make substantive decisions about content and emphasis. Most automation is limited to data pipeline and formatting tasks. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can draft report sections or summarize data, but no deployed product manages the directive/supervisory function of overseeing report preparation across a team. |
Match appropriations for specific programs with appropriations for broader programs, including items for emergency funds.
28CI 25–30 · exposure 25 · augmentation 63 · importance 3.5/5 · click for rater detail
Match appropriations for specific programs with appropriations for broader programs, including items for emergency funds.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Budget analysis remains concentrated in specialized finance and government roles with slower IT modernization compared to information-centric sectors. Pilot automation exists but production adoption of end-to-end matching remains limited due to regulatory and organizational inertia. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Government and public-sector budget offices are typically slower adopters of AI tools compared to finance/tech sectors, with pilots more common than production deployment for this kind of specialized task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can assist by suggesting potential matches, highlighting discrepancies, organizing data hierarchically, and flagging items that might qualify for emergency treatment, meaningfully improving analyst productivity without requiring full task automation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by cross-referencing documents, flagging inconsistencies between program-level and broader appropriations, and summarizing complex funding structures, significantly speeding up the analyst's review process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves complex matching and hierarchical relationships between budget items that require understanding context, policy rules, and emergency criteria. While AI could assist in organizing and flagging potential matches, the domain-specific judgment about what constitutes appropriate pairing and emergency fund treatment typically requires human expertise and is not 50% automatable end-to-end today. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires understanding legal/organizational program hierarchies, contextual judgment about emergency fund allocation, and cross-referencing complex appropriations structures that AI can assist with but not fully execute reliably end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Budget appropriations are often governed by legislative requirements, accounting standards, and audit trails. Organizations are typically required to maintain accountability and documentation that creates friction for full automation, and many government/public sector roles have explicit compliance and sign-off requirements. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no explicit licensing is required, budget analysis in government contexts often requires organizational sign-off, adherence to specific appropriations law, and accountability structures that create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing a specialized system to match budget appropriations accurately would require substantial integration, validation, and ongoing human oversight. The all-in cost (data setup, inference, error-checking) remains higher than the direct labor cost of budget analysts performing this work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Given the low reliability and need for significant human verification and domain expertise, AI-assisted approaches would still require substantial analyst oversight, keeping costs closer to human-comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably matches budget appropriations across programs at scale. Budget matching exists in limited forms within enterprise ERP systems, but this specific task—aligning specific to broader programs with emergency considerations—is not reliably automated in production systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product specifically performs appropriations matching against program hierarchies reliably; general-purpose LLMs could assist with document analysis but lack domain-specific production deployment for this niche government/budget task. |
Interpret budget directives and establish policies for carrying out directives.
20CI 13–28 · exposure 20 · augmentation 63 · importance 4.0/5 · click for rater detail
Interpret budget directives and establish policies for carrying out directives.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Budget policy is a governance function tightly controlled by finance leadership with little incentive or appetite to delegate to AI. Adoption in production remains minimal, and organizational culture strongly favors human expert judgment for policy-level decisions. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and government sectors are adopting AI for analysis and drafting support at a moderate pace, but policy-setting authority remains human-led with limited production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by summarizing budget directives, highlighting contradictions, drafting initial frameworks, and organizing stakeholder input, which accelerates human policy makers' work. However, the core deliberation and decision remain human-led. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by summarizing directives, drafting policy language, and flagging inconsistencies, significantly speeding up the analyst's preparatory work even though final interpretation remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can parse policy documents and summarize directives, establishing policies requires normative judgment, stakeholder input, and alignment with organizational values—tasks that demand human discretion and accountability. AI could assist in organizing and flagging inconsistencies but cannot autonomously create binding policy. |
| Task automatability | claude-sonnet-5 | 2/5 | Interpreting high-level directives and establishing organizational policy requires judgment, stakeholder negotiation, and accountability that current AI cannot reliably replicate end-to-end.To meet the 50% time-saving bar, most of the substantive interpretive and policy-setting work would still need to be done by a human. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Budget policy establishment carries significant legal, fiduciary, and governance weight. Authorized human leaders (CFOs, budget directors) must be accountable for policy decisions; regulators, boards, and stakeholders expect human ownership of binding budget directives. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Budget policy-setting typically requires organizational authority, accountability, and sign-off from designated officials, creating strong institutional and governance barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Even with AI assistance for directive analysis, human policy experts must ultimately interpret, deliberate, and establish frameworks. Integration costs and ongoing expert oversight mean AI does not yet offer a cost advantage over direct human policy work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply summarize documents, but the judgment-heavy policy-setting portion still requires expensive human oversight, keeping overall cost comparable to or only slightly less than human-only performance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs end-to-end policy establishment in production environments. AI systems can support research and drafting, but real-world policy adoption requires human leadership and organizational sign-off that remains firmly human-driven. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously interprets budget directives and sets organizational policy; existing tools assist with drafting summaries or analysis but leave interpretation and policy creation to humans. |
Testify before examining and fund-granting authorities, clarifying and promoting the proposed budgets.
1CI 0–3 · exposure 0 · augmentation 50 · importance 3.8/5 · click for rater detail
Testify before examining and fund-granting authorities, clarifying and promoting the proposed budgets.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Organizations in government and public finance have no incentive or regulatory permission to have AI testify before examining authorities; this remains firmly a human-only function across all sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While budget analysts work in sectors with moderate AI adoption for analysis, the testimony function itself sees essentially no AI adoption due to its formal, personal nature. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist a budget analyst by drafting talking points, summarizing budget documents, or preparing responses to anticipated questions, but the core testimony task must remain human-performed. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can strongly help analysts prepare talking points, anticipate questions, draft clarifying materials, and rehearse responses, improving the quality and efficiency of testimony prep. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Testifying before authorities requires real-time persuasion, credibility judgment, handling adversarial questioning, and building trust—capabilities that current AI systems cannot reliably perform autonomously in a high-stakes public setting. |
| Task automatability | claude-sonnet-5 | 1/5 | Live testimony before authorities requires real-time human presence, credibility, and responsiveness to unpredictable questioning that AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Testifying before authorities is typically a formal proceeding where a human representative must appear, take oath, and bear legal responsibility for statements made; many jurisdictions explicitly require human testimony in such contexts. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Formal governmental and organizational processes typically require a designated, accountable human representative to testify and answer questions under scrutiny. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human budget analyst must physically appear and testify; AI cannot substitute for this requirement, so there is no cost advantage—only added expense if AI were used to assist preparation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI alternative to compare costs against for the actual testimony act, so AI is not a cheaper substitute here. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can independently represent an organization before funding authorities or deliver credible testimony; this requires human presence, legal responsibility, and accountability that organizations will not delegate to AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human testifying before oversight bodies or legislatures; this remains a human-only interpersonal and institutional function. |
Related occupations — Business & Financial Operations
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.