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Governments can tell whether AI reduces operating costs only by comparing the full cost of delivering the same work and service outcomes before and after deployment—and by testing whether the change is attributable to AI. A shorter task is not automatically a budget saving: staff may use the released time for other work while payroll stays the same. Report cash savings, redirected staff capacity, avoided costs, revenue and service improvements separately.
Start with a defined baseline
Before procurement or rollout, define the service and the work unit being evaluated: for example, one completed application, inspection or support request. Record the period, population served, workload and case complexity, output volume, service standard, staffing effort, contractor use, existing systems and operating expenditure. Keep those definitions consistent in the follow-up period, or explain adjustments when they cannot be.
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Choose the objective first. An agency may seek to spend less while maintaining service, handle more work with existing resources, improve quality, or reduce future costs. These are different outcomes; an evaluation should not treat increased output or better service as proof of lower spending.
Count the complete cost of AI
Compare the costs of the AI-enabled service with the costs of the comparable baseline service. Include one-time and recurring expenses, not just the model or subscription price. OECD guidance calls for tracking full project costs. In its public example, the CDC’s estimate included usage and task types as well as implementation, infrastructure, platform, training and adoption costs.
#1 Best Overall
- Procurement or development, integration and data preparation
- Compute, hosting, licenses or usage charges
- Security, privacy, oversight and human review
- Training, adoption and changes to existing workflows
- Maintenance, upgrades, and eventual replacement or exit costs
Separate cash expenditure from allocated staff time and broader economic resource costs. If a jurisdiction uses a multi-year calculation, state the time horizon and discounting approach required by its finance rules.
Measure comparable results, not forecasts
Track the same service and work unit at baseline and follow-up. Useful measures include completed work, time per case, backlog, employee and contractor hours, actual expenditure, errors, appeals and rework, waiting time, and quality or satisfaction. Record case complexity and service standards as well as volume: a lower average cost can be misleading if the post-AI period involved simpler cases or a different level of service.
Rank #2
A pre/post comparison is a starting point, not proof that AI caused the difference. Where practical, use a phased rollout or a matched comparison group. Otherwise, document plausible influences such as workload shifts, policy changes, staffing changes or non-AI process improvements. OECD recommends comparing before-and-after metrics and tracking completed-project indicators; GAO’s modernization review illustrates why expected savings need later validation.
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Use distinct categories rather than rolling every claimed benefit into one “savings” total:
- Cashable operating savings: documented reductions in expenditure, such as lower contractor or overtime spending, or a reduced operating budget.
- Redirected capacity: staff hours freed from one task and used for other work. This can be valuable, but it is not a payroll saving unless staffing costs or resource use actually change.
- Cost avoidance: a future expense that the agency can show it did not incur, with the counterfactual and assumptions stated.
- Revenue effects: additional receipts or improved collection, reported separately from operating-cost reductions.
- Service and quality gains: changes such as faster service or fewer errors, reported alongside costs rather than converted into cash savings without a defensible method.
A value calculated by multiplying estimated hours saved by wage rates is an imputed labor value until the agency can show a corresponding budget or resource change. The Congressional Budget Office notes that AI may improve federal efficiency while requiring spending, and that better service could expand activity. The net budget effect is therefore uncertain and depends on the agency’s objective and what happens to the released capacity.
Use a counterfactual and make uncertainty visible
The key question is what would have happened to the same work without AI. A phased rollout can compare early and later adopters; a matched comparison can compare similar units or cases. If neither is feasible, use a transparent pre/post evaluation and describe its limits rather than presenting correlation as causation.
Report a range where inputs are uncertain. Show assumptions about adoption, usage, workload and timing; identify who validated the inputs; and set a date for revisiting the estimate. A forecast should remain labeled as projected until results are observed. GAO found that several Technology Modernization Fund projects did not meet or were not on track to meet expected savings, underscoring the gap that can exist between planned and realized benefits.
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For each baseline and follow-up period, publish or retain an auditable record of:
Best Value
- Service volume, case complexity and unit cost
- Total operating expenditure and AI-related one-time and recurring costs
- Employee and contractor hours, throughput and waiting time
- Error, appeal and rework rates, plus quality or satisfaction measures
- Cash savings, redirected capacity, cost avoidance and revenue effects as separate figures
- Uncertainty range, assumptions, data owners, validation date and audit trail
One useful evaluation structure is: net operating-cost effect = baseline operating cost for comparable output − post-AI operating cost for comparable output − incremental AI lifecycle cost. State whether each cost is cash expenditure, allocated labor or economic resource cost; do not combine unlike measures without labeling them. If a proposed benefit cannot be observed in budget, staffing, procurement or service data, describe it as estimated capacity or potential value, not a realized saving.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available government evidence shows
The figures below illustrate why governments should distinguish measurement practice, estimates and realized outcomes. They do not establish a general AI savings rate.
| Evidence | What it says | How to interpret it |
|---|---|---|
| OECD, 2026; survey data cover 2023–2024 | 10 of 36 OECD countries (28%) reported conducting any prospective or retrospective financial or nonfinancial impact-measurement studies of government AI use cases. Half used evidence of potential efficiency or cost savings when deciding whether to adopt AI. | This is a gap between reported use of potential evidence and measurement activity—not a success or failure rate. OECD report. |
| U.S. GAO, 2026; Technology Modernization Fund projects | 24 projects expected about $1.06 billion in savings; 11 had realized about $13.5 million as of June 2025. Thirteen had not yet begun achieving savings, and 21 projects representing 98.3% of expected savings anticipated them in fiscal year 2027 or later. Of six completed projects expecting savings, two met or were on track within 10% of target; four did not meet or were not on track. | These are IT modernization projects, not an AI-specific or representative AI savings rate. GAO cited removed functionality and higher migration costs among reasons for misses. GAO report. |
| CDC, 2026; internal estimate | CDC estimated more than $3.7 million in labor costs saved to date and 41,460 staff hours redirected. | The public case study says the estimates use an internal, unpublished analysis drawing on tokens, task types, industry time-saving benchmarks, estimated labor rates and implementation, infrastructure, platform, training and adoption costs. The underlying calculation and a causal comparison are not public, so treat the amounts as agency estimates. CDC case study. |
| Austria, as described by OECD, 2025 | Predictive analytics activity by the Austrian Ministry of Finance was associated with approximately EUR 185 million in additional tax revenues in 2023, after analyzing 6.5 million cases across income, corporate and value-added tax, and customs transactions. | This is a reported revenue outcome, not a reduction in operating costs. OECD public-finance chapter. |
Put accountability around the claim
Assign named owners for the baseline, cost inputs, outcome measures and follow-up. GAO’s AI accountability framework organizes practices around governance, data, performance and monitoring; those categories also provide a useful structure for reviewing savings claims. Make clear who approved the assumptions, how data quality was checked and when results will be reassessed. GAO, Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities.
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OECD’s 2025 public-finance guidance says public feasibility and cost studies are rare and impact assessments are often anecdotal. It recommends project frameworks that track full costs and completed-project indicators—including savings, effectiveness, efficiency, error reduction and compliance—alongside before-and-after measures and stakeholder feedback. These practices make a cost claim auditable instead of leaving it as a vendor projection or a productivity estimate.
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