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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA decision-ready AI business case starts with a bounded use case and a measurable baseline, then compares realistic benefits with the full cost of implementing, operating, and overseeing the system. Treat a pilot as a test of assumptions—not proof of savings—and fund the people and processes needed to monitor quality, manage risk, and change or stop the system.
How do I build a business case for AI?
Build the case around a specific business outcome, not the general promise of AI. Define the workflow the system will affect, how success will be measured, and what would happen without the investment. Then estimate benefits and lifecycle costs using the same time period and operating assumptions.
- Bound the use case: State the task, intended users, workflow boundary, and decisions the system may influence. Be explicit about what remains outside its scope.
- Record the baseline: Measure current volume, time, cost, quality, service level, error rate, or other relevant outcome. Include the cost of the existing process and note where its data comes from.
- Name the outcome owner: Assign a business owner accountable for the result, alongside owners for delivery, data, system quality, and risk.
- Set the counterfactual: Estimate what would happen over the same period if the organization did not adopt the AI system—for example, if it continued the current process or made a non-AI improvement.
- Choose the measurement window and method: Specify when outcomes will be measured, how results will be compared with the baseline, and how the organization will distinguish the system’s contribution from other changes.
Keep the boundary narrow enough to measure, but include the workflow changes needed to make the system useful. OECD enterprise interviews caution that treating AI as plug-and-play can leave organizations unprepared for changes to structure, processes, and workplace culture. A tool that works technically may still depend on new handoffs, staff training, revised procedures, or changed decision rights.
How do you estimate AI benefits and ROI?
List each expected benefit separately, identify the evidence or assumption behind it, and state how confidently it can be attributed to the AI investment. Do not treat all benefits as cash savings: distinguish cost reduction from capacity released, improved service, lower error rates, risk reduction, and potential new products or revenue.
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- Direct savings: Count a reduction in spending only if the organization expects to remove or avoid that spending. Time saved is not automatically a cash saving.
- Efficiency or capacity: Estimate the hours or other capacity released and explain how it will be used, such as handling more work or reducing a backlog.
- Quality and service: Define a measurable outcome such as fewer errors, faster response, or better service, and describe its business consequence.
- Revenue or new offerings: Separate forecast revenue from realized revenue and make adoption, pricing, and demand assumptions visible.
- Risk reduction: State the risk being reduced and the method for valuing that reduction. Avoid presenting avoided losses as certain savings.
One useful summary calculation is ROI = (measured benefits − total costs) ÷ total costs. Define which benefits and costs are included, use the same time horizon for both, and report the underlying amounts as well as the percentage. For investments with material costs or benefits spread over several years, finance teams may also compare cash flows using their usual discounted-cash-flow method. Do not imply precision that the evidence does not support: show key assumptions and plausible low, expected, and high outcomes where estimates are uncertain.
That uncertainty is common. In an OECD enterprise study, 62% of manufacturers and 56% of ICT enterprises in the study sample reported difficulty estimating ROI in advance (OECD, 2023). The figures describe those surveyed groups, not all businesses. OECD also notes that attribution can be difficult even for narrow applications such as predicting machinery failure, and that collecting reliable outcome data can itself cost money. Savings may be easier to estimate than new AI-enabled products, services, or business models.
What costs should be included in an AI business case?
Estimate costs for the system you actually intend to operate. A per-user subscription, a usage-priced model service, and a custom-built system have different cost drivers. Record one-time and recurring costs separately, and include both cash spending and internal staff time. The rows below are a practical planning checklist, not a complete accounting standard.
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| Cost area | One-time or variable setup costs | Recurring or usage-linked costs |
|---|---|---|
| Product or model | Vendor selection, procurement, initial setup, configuration, and customization | Subscription or per-user license; model or API charges based on expected input and output volumes |
| Integration and delivery | Discovery, application integration, deployment, workflow redesign, and pilot administration | Vendor support, contract management, ongoing integration maintenance, and operational support |
| Data | Acquisition, preparation, cleaning, rights review, and access setup | Data maintenance, continued access, quality checks, and any recurring acquisition or rights costs |
| Technology operations | Initial infrastructure configuration and security setup, where needed | Cloud, compute, networking, storage, security, and infrastructure operations; usage may vary with volume |
| People and adoption | Internal staff time, specialist hiring or contractors, training, and change management | Staff time for support, refresher training, process changes, and ongoing adoption |
| Quality, risk, and oversight | Testing, evaluation, risk assessment, documentation, and privacy or security review | Human review, monitoring, incident response, retraining, redeployment, and retirement planning |
| Uncertainty and exit | Migration or exit planning and a contingency for uncertain implementation needs | Contingency for uncertain usage or scaling, plus future migration or exit effort if it arises |
For usage-priced services, show the volume assumptions behind the estimate—including expected requests and input and output usage—and calculate more than one plausible usage level. For licenses, specify the expected number of users and any other pricing basis in the proposal or contract. For custom development, include internal delivery effort, specialist capacity, maintenance, and support rather than treating the initial build as the whole cost.
OECD’s government cost analysis describes licensing, volume-based use, custom development, and support as distinct cost forms, with costs varying by system and scale. It also notes that it found no general research estimating development or use costs by AI system type. The analysis includes public-sector examples; those examples are not private-sector price guidance. There is no defensible universal AI implementation cost figure in this evidence, so label any comparison by system, scale, date, geography, and organization type.
How should AI risks and oversight be budgeted?
Put oversight in the operating model and cost plan from the outset. It is not a one-time sign-off: system performance and impacts need attention after deployment, and someone must have the time and authority to act on findings. OECD says maintaining model performance can require continued assessment, retraining with current data, and redeployment.
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For the proposed system, name owners and define what each is responsible for. A compact responsibility plan should identify:
- Who owns the business outcome and who is accountable for system operation.
- Who manages data quality, system evaluation, and monitoring.
- Who performs or supervises human review, and what work must be escalated.
- Who assesses privacy, security, and other relevant risks, and who handles incidents.
- Who can pause or limit use, approve remediation, and fund changes or redeployment.
Specify what will be checked before launch and after deployment, how changes in inputs or performance will be detected, and what conditions trigger investigation, human intervention, a pause, or retirement. Scale the effort to the use context and consequences rather than applying the same controls to every system.
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Which AI options should the business case compare?
Compare plausible ways to address the same workflow, including the option of not adopting AI. Evaluate them against the same baseline, outcome measures, time horizon, and cost categories.
| Option | Questions to test in this case |
|---|---|
| Do nothing or improve the current workflow | Can process changes, existing software, or added capacity address the problem? What outcome and cost would result without AI? |
| Buy a hosted product | Does its capability fit the task and workflow? What are the licensing, integration, customization, data, support, and vendor-dependence implications? |
| Use a usage-priced model in an internal application | Can the organization estimate usage and operating costs? What integration, data, monitoring, and human-review work is required? |
| Procure a tailored solution | What additional fit does tailoring provide, and what are the implications for delivery time, customization, support, change requests, and exit? |
| Build or customize internally | Are the necessary skills, data, infrastructure, and ongoing capacity available? Who will maintain quality and support the system after launch? |
For each option, compare total lifecycle cost, fit to the task, data needs, time to implement, staff capacity, controllability, governance effort, vendor dependence, and ability to measure benefits. The best choice depends on the use case; the cost categories alone do not establish a universally superior option.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should approval and scaling work?
Use staged decisions so that larger commitments depend on evidence gathered at smaller ones. Agree on the measures and decision thresholds before each stage, then revisit expected outcomes, total cost, adoption, oversight workload, and risks at the gate.
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- Discovery and feasibility: Confirm the use-case boundary, baseline, data access, technical fit, process changes, likely cost drivers, and responsible owners.
- Limited pilot: Test against the baseline with a defined user group, measurement window, and comparison method. Track implementation effort and oversight work as well as task performance.
- Controlled production: Expand only with operating procedures, monitoring, escalation, human-review arrangements, and funding for support and remediation in place.
- Scale: Extend use when results meet the pre-agreed threshold and the organization can support the additional volume, cost, and oversight. Re-estimate costs and benefits at the new scale rather than multiplying pilot results without checking assumptions.
A pilot’s license or model bill is only one input to the decision. OECD enterprise research describes firms running pilots without a plan for integration, and its findings on advance ROI estimation underscore why a pilot should test both the operational case and the measurement method.
Public-sector evidence offers another caution, but should not be generalized to private companies: as cited by OECD, UK DSIT reported in 2025 that only 8% of UK government AI projects showed measurable benefits and only 16% showed forecast costs. Those figures concern UK government projects; they illustrate the need to define outcome measures and forecast costs, not a success rate for business AI projects.
What should the final decision document contain?
A concise approval case should make its assumptions and accountability visible enough that decision-makers can challenge them and operators can act on them. Include:
- The bounded use case, intended users, workflow boundary, current baseline, and counterfactual.
- Expected outcomes, measurement method, time horizon, attribution assumptions, and uncertainty ranges.
- One-time and recurring costs, including internal effort, implementation, data, operations, oversight, support, and exit considerations relevant to the chosen design.
- A comparison of plausible alternatives, including the non-AI option, using consistent criteria.
- Named delivery, business, data, quality, risk, human-review, and escalation owners.
- Stage gates, evidence thresholds, funding decisions, and conditions for remediation, pause, or scale.
Approve the next stage only when the case explains what will be learned, how it will be measured, and who will pay for the work required to operate the system responsibly. Treat the expected ROI as a testable estimate, not a guaranteed outcome.
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