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Calculate the total cost of an AI-enabled public service across its full lifecycle—not by multiplying an API price by expected usage. Define the service and planning period, compare credible delivery options on the same assumptions, estimate one-time and recurring costs by line item, and show uncertainty separately. Then reconcile the forecast with actual spending and service outcomes as the deployment runs.
Define what the estimate covers
Before collecting prices, write down what decision the estimate supports and what service it describes. A cost total is meaningful only when its scope, demand assumptions, time period, and boundaries are clear.
- Service and outcome: name the public service, the task AI may support, the intended service outcome, and the population or staff who will use it.
- Boundary: state which systems, teams, suppliers, and activities are included. Note whether the estimate covers a pilot, production service, or both.
- Planning horizon: choose a period long enough to include setup and meaningful operating costs, plus any likely transition or closure work. State the start date and how costs are treated across years.
- Baseline and alternatives: document the current service cost and performance, then identify plausible options such as building, buying, adapting an existing system, or using a shared service.
- Demand and service assumptions: record expected transaction or user volumes, operating hours, service levels, human review requirements, and other factors that affect workload or usage.
- Ownership: assign an owner to each estimate line and record who supplied the underlying rate or quantity.
- Exclusions: list costs deliberately left out and why. Distinguish a sunk cost from existing capacity that will still be consumed.
GAO’s Cost Estimating and Assessment Guide calls for a defined purpose and scope, schedule, technical baseline, work breakdown structure, assumptions, documented methods, and results. These are general cost-estimating practices, not an AI-specific tariff or mandated formula.
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Use a work breakdown structure: break the service into cost categories, then into items small enough to estimate and audit. The checklist below is not a claim that every deployment incurs every cost, nor that every item is new cash spending.
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| Category | Potential cost lines to assess |
|---|---|
| Service definition and procurement | Discovery, options appraisal, procurement, commercial and legal work, and supplier due diligence. |
| Data | Access or acquisition, rights, collection, cleaning, labeling, migration, quality assurance, storage, governance, and ongoing quality monitoring. The UK Balance Sheet Framework offers a non-exhaustive data-cost taxonomy that includes strategy, collection or procurement, legal, technical, operational, and implicit costs. |
| Technology | Model or software licence or development, inference or compute, storage, network, environments, security controls, integration, testing, and resilience. |
| People and change | Service ownership, domain expertise, engineering, procurement, legal, privacy and security input, frontline review, training, change management, and user support. |
| Operations and assurance | Hosting, service desk, monitoring, evaluation, incident handling, audit, model or data updates, maintenance, and supplier support. |
| Transition and closure | Migration, legacy-system overlap, contract exit, data export, decommissioning, and replacement where relevant. |
| Risk and contingency | Material uncertain costs or risk scenarios, presented separately with their basis rather than silently buried in a point estimate. |
For each line, capture the estimated quantity, unit, unit rate, rate source and date, timing, whether it is one-time or recurring, the responsible owner, and the assumption behind it. Include internal labour using an explicit rate and overhead method. The UK government’s AI procurement guidance recommends considering whole-of-life costs, including ongoing support and maintenance, as well as integration, implementation, training, and hosting arrangements.
Calculate costs consistently
For each ledger line and time period, estimate quantity multiplied by unit cost. Separate setup from recurring costs, then sum each period across the declared horizon. Keep the calculation visible so another reviewer can see how assumptions produce the total.
- Estimate one-time work: quantify discovery, data preparation, integration, testing, procurement, implementation, and initial training as applicable.
- Estimate recurring work: quantify usage-based charges and ongoing staffing, hosting, support, monitoring, maintenance, evaluation, and updates.
- Apply consistent demand assumptions: use the same expected service volumes and operating requirements across all options being compared.
- Show the period totals: report setup costs, recurring costs by year or other useful period, and the lifecycle total. Keep cash expenditure distinct from the full resource estimate.
- Test uncertainty: vary uncertain quantities and rates in ranges or scenarios, such as lower, expected, and higher service volume or integration effort. Explain what drives each scenario.
- Document and update: record sources, methods, exclusions, and risks, then replace estimates with actual expenditure as it becomes available and revise future periods.
GAO’s guide recommends documenting the estimate and performing sensitivity and risk analysis, then updating estimates with actual costs. Applying these principles to an AI service is a practical synthesis of general estimating guidance and whole-of-life procurement practice, not a government-prescribed AI formula.
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Separate budget impact from resource use
Report new cash spending separately from the cost of using existing staff, platforms, facilities, or shared services. A platform already paid for may have no additional invoice for a particular deployment, yet the service still consumes capacity that has other uses. Identify sunk costs, allocated existing capacity, and opportunity cost distinctly rather than treating them as interchangeable.
Likewise, do not subtract anticipated productivity gains from the cost total as though they were cash savings. A benefit becomes a demonstrated saving for a particular agency only when it is measured against a baseline and attributable to the service. Track cost and outcome measures separately, then assess their relationship using actual results.
Compare options on the same basis
For a build, buy, adapt, or shared-service decision, hold service scope, planning horizon, demand, and service-level assumptions constant. Compare not only initial setup but also recurring costs and factors that affect delivery, ongoing effort, and the ability to change course.
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| Comparison area | Questions to record for each option |
|---|---|
| Setup and lifecycle total | What is one-time setup, what recurs, and what is the total over the same horizon? |
| Data readiness | Are data rights and access established? How much preparation is needed, and what ongoing quality work is expected? |
| Integration | How complex is the connection to existing systems and service processes? |
| Hosting and support | What hosting, resilience, security, and support arrangements are included, and what remains the public body’s responsibility? |
| People and assurance | What human review, training, monitoring, evaluation, governance, and incident-response effort is required? |
| Dependencies and exit | What supplier or platform dependencies arise, and what work or cost would be involved in migration, data export, or contract exit? |
| Uncertainty and outcomes | Which quantities or rates are most uncertain, and what evidence will show whether the option achieves the intended service outcome? |
The UK procurement guidance also recommends assessing integration with existing systems and planning implementation, continuing evaluation, knowledge transfer, and training. Its guidance is aimed at UK central government and may be useful to other public bodies; procurement and legal obligations vary by jurisdiction.
Include governance and accountability work
Governance is part of the operating model, not a free addition to the estimate. Where applicable, cost the staff time and processes needed to assign responsibilities, document data provenance and use, record design and deployment decisions, support review, and evaluate system performance. The UK Data and AI Ethics Framework, updated on 18 December 2025, emphasizes records that support audit and review, including responsibility, data origin and use, and design and deployment decisions.
Assign owners across service delivery, technology, procurement, data, legal, privacy, security, operations, and frontline teams as relevant. UK procurement guidance recommends multidisciplinary teams, data assessment before procurement, and arrangements for knowledge transfer and training. The people and effort needed will depend on the service and jurisdiction.
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Use published figures carefully
Published examples can show why local estimation matters, but they are not interchangeable cost benchmarks.
| Published figure | What it describes—and what it does not |
|---|---|
| 10 of 36 OECD countries (28%) reported any financial or non-financial impact measurement of government AI use cases in the OECD’s 2026 reporting. | A country-level report of measurement practice, not the proportion of individual deployments evaluated. |
| 21 of 36 OECD countries (58%) provided central support for procuring AI goods and services in the OECD’s 2026 reporting. | A country-level finding about procurement support, not a deployment cost or proof that support is available in every jurisdiction. |
| Public Services and Procurement Canada forecast $4,686,281 for AI model training/testing and operations and maintenance in fiscal 2025–2026. | A forecast for one agency initiative, not a unit cost or typical deployment budget. The briefing indicates that later years need ongoing funding. |
| The UK Digital and Data Benefits Framework reports potential annual savings of £6.3 billion: £1.1 billion in potential cost reductions and £5.2 billion in productivity gains. | Government Digital Service analysis of 200,000 Civil Service job descriptions; it is potential across a broad task analysis, not measured cash savings or a project-level forecast. |
The OECD figures come from the OECD Digital Government Outlook 2026. The Canadian forecast is documented in Public Services and Procurement Canada’s 2025 briefing. The UK potential-benefit figures appear in the Digital and Data Benefits Framework.
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Monitor actual cost and service outcomes
At approval, set a baseline for both cost and service performance, with an owner and reporting cadence for each measure. During delivery, reconcile forecast costs with actual expenditure and explain material variances—for example, changes in usage, data quality work, staffing, supplier rates, integration, or support needs. Update the remaining-year estimate rather than leaving the original forecast untouched.
Track benefits against the same baseline and define what evidence would support attribution to the AI-enabled service. The OECD’s 2026 country-level findings show that reported government impact measurement is not universal; they do not establish whether a particular service has achieved a benefit. Use the service’s own evaluation to make that determination.
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