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Set an AI budget around a defined business or public-service problem, not a general technology allowance. Before committing funds, identify the workflow and accountable owner, record how it performs today, estimate the full cost of implementation and ongoing operation, and decide which financial and non-financial results will count as success. Then compare actual results and risks with the original case before deciding whether to continue, adjust, scale, or stop.
Start with a use case and an accountable owner
Choose a specific workflow and explain what is wrong or could improve: for example, slow turnaround, frequent errors, poor access to information, or staff capacity tied up in repetitive work. Define the outcome you want—such as faster service, fewer mistakes, improved decisions, more capacity for higher-value work, or better customer or staff satisfaction—before deciding whether AI is the right investment.
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Name the people affected and the person responsible for realizing and checking the expected benefits. 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’s business guidance on measuring return on investment recommends defining the problem, outcome, and signs of progress before investing.
Measure the current baseline before rollout
Record how the workflow performs without the proposed AI system. Choose measures that fit the task, such as time per task, volume completed, error or rework rate, quality, and relevant customer or staff measures. Make the definitions and measurement period clear so that post-adoption results can be compared fairly.
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After rollout, measure the same task using the same definitions. Allow enough time to see normal variation; the National AI Centre notes that tracking may need to run for weeks or months. Where feasible, compare the AI-enabled workflow with a similar process that has not changed, or with a non-AI alternative. This can help separate AI’s contribution from changes in staffing, demand, or process, but no single comparison design fits every organization.
Value time saved carefully
Estimate the value of time saved by multiplying measured time reduction by the relevant staff-time cost. Treat this as an estimate of capacity value, not automatically as cash savings. The released time creates value only when it is redirected to useful work. As the National AI Centre puts it: “Time saved only delivers value if it’s redirected to useful work, such as serving customers, improving quality or growing the business.”
Budget for the full cost, not just the licence
Build a lifecycle cost estimate that captures both visible charges and the effort required to make the system work safely and reliably. Separate one-time setup from recurring expenditure, and account for usage-sensitive costs where applicable. The categories below follow the National AI Centre’s business guidance.
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| Cost category | Include |
|---|---|
| Direct costs | Licences, subscriptions, infrastructure, and external support. |
| Indirect and ongoing costs | Staff training, testing, change management, data preparation, governance, and ongoing oversight. |
| Opportunity costs | 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 judge benefits. A low initial licence cost can be misleading if data preparation, integration support, training, or oversight makes the total cost materially higher.
Choose measures that reflect realized value
Use a small set of measures that connect the original problem to the decision you need to make. Financial measures may include realized expense reductions, incremental revenue, or costs avoided, but claim these only when the evidence and your accounting treatment support them. Operational and non-financial measures can include cycle time, quality, consistency, capacity, confidence, decision quality, staff satisfaction, and customer satisfaction.
- For time savings: show what useful work absorbs the released capacity; do not label all saved hours as cash savings.
- For fewer errors: compare error and rework costs before and after adoption.
- For revenue or retention: distinguish measured results from changes that may also reflect demand, pricing, staffing, or other tools.
- For quality, confidence, or satisfaction: define how each will be assessed and whose experience counts.
Some benefits take time to appear, and it can be difficult to attribute a change to AI alone. Avoid presenting one ROI formula or hurdle rate as universally authoritative: the appropriate horizon, treatment of benefits, and risk adjustment depend on the organization’s accounting policy and the decision at hand. The National AI Centre offers measurement practices, not a required formula, budget percentage, or guaranteed payback period.
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Compare AI proposals on the same basis
If you are choosing among several projects, compare them using a shared measurement period and the same decision criteria. Include the full lifecycle cost and the benefit you can actually measure and realize—not just the most attractive forecast. A consistent comparison can include:
- Expected measurable benefit and how it will be verified.
- Time and effort to implement.
- Data and staff-skill readiness.
- Ability to scale beyond an initial deployment.
- Operational and financial risks, plus governance and oversight needs.
- Fit with organizational priorities.
OECD guidance for public administrations recommends planning, monitoring, and evaluating AI investments for value for money, timely implementation, risk mitigation, and realized benefits. It also recommends prioritizing high-benefit, low-risk opportunities where possible. These are public-sector recommendations; an organization should assess their applicability to its own context.
The public-sector context matters when interpreting the OECD’s figures. Its 2025 report cites 2023 OECD 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 statistics describe government investment practices, not AI project success rates or corporate AI ROI.
Include risk and implementation friction in the case
Expected gains can shrink or be reversed if a system is difficult to operate, produces harmful errors, or cannot be scaled beyond a pilot. OECD’s government-focused analysis identifies risks and obstacles including biased outcomes, rights infringements, weak transparency and accountability, over-reliance, systemic error propagation, skills gaps, data problems, financial costs, and scaling difficulties. Use these as prompts for an assessment of the specific use case; they do not apply equally to every project.
NIST’s AI Risk Management Framework 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; it says, “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” NIST’s current framework page says AI RMF 1.0 is being revised and records release of its Generative AI Profile on July 26, 2024. Check NIST’s current material when using the framework, rather than treating its status as fixed.
Review actual results before scaling
Set a review interval suited to the workflow and its expected benefit timeline. At each review, compare actual costs and measured outcomes with the original case, record unintended effects, and decide whether to continue, adjust, scale, or stop. Use the same baseline and definitions where possible; if the workflow or measurement changes, document the change so that results remain interpretable.
Scaling should follow evidence that the benefits are being realized and that the costs, risks, and oversight demands remain acceptable. NIST’s voluntary framework can help structure trustworthiness considerations, while OECD’s public-sector guidance emphasizes evaluation and risk mitigation; neither supplies financial assumptions or a universal approval threshold.
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