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Set an AI budget around a defined workflow and a measurable business or service outcome—not a general technology allowance. Before committing funds, record how the workflow performs today, estimate the full cost of changing it, decide how results will be measured, and set a review point to continue, adjust, scale, or stop.
Start with a specific problem and an accountable owner
Choose a workflow where AI could address a recognizable need: for example, reduce document-processing delays, improve the consistency of customer responses, or help staff find information. Name the people affected, the expected result, and the person responsible for checking whether that result occurs. Tie the proposal to an organizational priority rather than treating AI as a budget category in search of a use.
The Australian National AI Centre recommends defining the problem, desired outcome, and signs of progress before investing. Possible measures include fewer errors, faster turnaround, increased revenue, better decisions, more capacity for higher-value work, or improved satisfaction. Select measures that fit the use case; not every project needs to claim a financial return.
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Measure the current workflow before adoption
A baseline makes it possible to compare the changed workflow with the one it replaces. Record relevant measures before rollout, such as time per task, throughput, error or rework rates, quality, and customer or staff feedback. Use the same definitions after adoption, and collect results for long enough to understand normal variation. The National AI Centre notes that tracking may need to continue for weeks or months.
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Where practical, compare the AI-supported process with a non-AI alternative or a similar workflow that has not changed. This can help distinguish the effect of AI from changes in staffing, demand, or other tools, though no single comparison design fits every organization.
Build a full AI cost estimate
Licences and infrastructure are only part of the budget. The National AI Centre’s business guidance includes direct, indirect, ongoing, and opportunity costs. For forecasting, separate one-time setup costs from recurring expenses and identify costs that rise with usage.
| Cost category | What to include |
|---|---|
| Direct | Licences, subscriptions, infrastructure, and external support. |
| Indirect and ongoing | Staff training, testing, change management, data preparation, governance, and continuing oversight. |
| Opportunity | Work or alternatives displaced by the investment, including the possible effects of delaying or not adopting AI. |
Estimate costs over the same period you will use to assess results. Include the work needed to prepare data, test outputs, train people, and supervise the system—not just the vendor charge. If usage-based costs apply, model how spending could change as adoption or workload grows.
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Choose benefits you can observe and explain
Track financial outcomes such as realized expense reductions, incremental revenue, or costs avoided only when the evidence and accounting treatment support those claims. Pair them with relevant operational or non-financial measures: cycle time, quality, consistency, capacity, confidence, decision quality, staff satisfaction, or customer satisfaction.
For time savings, record what useful activity absorbed the released capacity. The National AI Centre cautions: “Time saved only delivers value if it’s redirected to useful work, such as serving customers, improving quality or growing the business.” Do not label every hour saved as cash savings unless spending actually falls or the hours are otherwise financially realized. If errors decline, compare the cost of errors and rework before and after.
Some benefits may take time to appear, and changes in revenue or retention can be difficult to attribute to AI alone. Avoid claiming that AI caused every improvement when other changes occurred at the same time. There is no universal ROI formula or required payback period established by the official guidance cited here. Organizations can calculate financial returns under their own accounting policies, but should state which benefits, costs, time horizon, and risks are included.
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Compare candidate projects on the same basis
If you are choosing among several AI proposals, compare them over a shared period and with consistent assumptions. OECD guidance for public administrations emphasizes value for money, timely implementation, risk mitigation, and evaluating whether intended benefits materialize. Useful comparison dimensions include:
- Full lifecycle costs and measurable benefits.
- Time and effort required to implement.
- Data and staff-skill readiness.
- Ability to scale beyond the initial workflow.
- Financial and operational risks, including oversight needs.
- Fit with organizational priorities.
In the public-sector context, OECD’s 2025 report cites 2023 Digital Government Index data: 88% of OECD countries had a standardized approach to developing value propositions for digital-government investments, while 41% had a risk-assessment mechanism for those investments. These figures describe government investment practices; they are not AI project success rates, corporate benchmarks, or recommended targets for a company.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Include risks in the business case
Expected benefits can be reduced or reversed by implementation friction and harms. OECD identifies risks in government AI adoption including biased outcomes, rights infringements, weak transparency or accountability, over-reliance, systemic error propagation, skills gaps, data problems, financial costs, and difficulty scaling beyond pilots. Treat these as prompts for assessing your own use case; their relevance and severity vary by system and setting.
NIST’s AI Risk Management Framework (AI RMF) is a voluntary resource for considering trustworthiness through AI design, development, use, and evaluation. Its Playbook organizes suggested actions under Govern, Map, Measure, and Manage. NIST says, “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” The framework can help structure risk review, but it does not supply financial ROI assumptions or replace applicable legal and organizational requirements. NIST’s current AI RMF page says version 1.0 is being revised and notes that its Generative AI Profile was released on July 26, 2024; check the NIST AI Risk Management Framework page for current status.
Review actual results before scaling
Set a review interval suited to the project and compare actual costs and measured outcomes with the original case. Record unintended effects as well as intended benefits. Then make an explicit decision: continue, adjust the workflow or controls, scale, or stop. A pilot’s results should support—not automatically guarantee—the case for a wider rollout.
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Quick Recap
Sources and scope
- Australian National AI Centre business guidance covers use-case definition, costs, and measuring ROI.
- OECD, Governing with Artificial Intelligence (2025) addresses government adoption, investment, monitoring, and risk.
- NIST AI Risk Management Framework and its AI RMF Playbook provide voluntary risk-management resources.
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