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How do you build a business case for AI?
Build the case around one decision: whether to fund a defined AI use case, and under what conditions it should proceed. Do not begin with a broad goal such as “use AI to improve productivity.” Specify the task, the people and process affected, the alternatives, the expected outcomes, the full lifecycle costs, and the evidence required to continue.
- Bound the use case: describe the task, intended users, workflow boundary, and what the system will and will not do.
- Establish the baseline: document current volume, quality or service level, time, costs, and relevant failure or error rates.
- Define benefits and uncertainty: identify measurable outcomes, how they will be attributed, and the assumptions behind any forecast.
- Estimate one-time and recurring costs: include implementation, people, data, infrastructure, support, and oversight—not just a license or model fee.
- Compare alternatives: include improving the current workflow as well as buying, customizing, or building an AI-enabled option.
- Set approval gates: release funding in stages and agree in advance what evidence is needed to pilot, deploy, or scale.
This structure matters because AI adoption can require changes to processes, responsibilities, and organizational culture—not simply installation of a tool. OECD enterprise research describes firms running pilots without a clear plan for integration and warns that a plug-and-play assumption can leave organizations unprepared.
Write a use-case statement
Keep the initial scope narrow enough to measure. A useful statement names the workflow, the decision or task being supported, the users, and the boundary of the system. For example: “For the customer-support team, classify incoming requests and suggest a response draft; staff review and send every reply.” This is more assessable than “automate customer support,” because it identifies where human judgment remains and where performance can be measured.
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Record the counterfactual
State what is expected to happen over the same period if the organization does not fund the AI investment. The comparison might be the existing process, a planned process improvement, or another technology. Assign an owner for the business outcome and specify how the baseline and results will be collected. Without a counterfactual, a change in results may be incorrectly attributed to AI when it came from staffing, demand, seasonality, or a different process change.
How do you calculate ROI for an AI project?
Calculate ROI only after defining the measurement window, comparison, and attribution method. A simple financial expression is:
Net benefit over the measurement period = attributable benefits − total costs
ROI = net benefit ÷ total costs
Use the same time horizon and cost boundary in both calculations. If costs include implementation and oversight but benefits include only a short period of projected savings, the result will mislead. If the business case spans multiple years, make clear which costs and benefits recur and which occur once; use the organization’s normal finance method for discounting future cash flows where applicable.
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- Direct cost reduction: lower external spending or avoidable operating expense, measured against the counterfactual.
- Capacity or efficiency: staff time or throughput released. Treat time saved as a financial benefit only if the organization can explain how it will convert that capacity into value, such as handling more work or reducing paid hours.
- Quality and service: fewer errors, faster response, or improved consistency. Define the metric and its business consequence rather than assigning an unsupported dollar value.
- Revenue or new offerings: estimate separately from cost savings. New products, services, and business models may be harder to forecast than reductions in existing costs.
- Risk reduction: identify the specific exposure and the evidence for a change in likelihood or impact. Do not present an unverified avoided-loss estimate as certain savings.
Show uncertainty instead of hiding it
List the assumptions that drive the forecast: expected adoption, task volume, quality, usage, review time, and the share of work that can safely use the system. Where evidence is incomplete, show plausible low, central, and high cases or another clearly defined range. Name the owner and method for checking each important assumption during a pilot.
Uncertainty is common, not a reason to omit measurement. In an OECD enterprise study, 62% of manufacturers and 56% of ICT enterprises in the study sample reported difficulty estimating ROI in advance. The figures describe those surveyed sectors and are not universal rates for all businesses. The OECD also notes that attribution can be difficult even in narrow use cases and that gathering reliable data can add cost.
What costs should be included in an AI business case?
Separate one-time setup from recurring operation, and tailor each line to the proposed architecture and scale. A hosted per-user product, a usage-priced model service, and a custom system have different cost drivers. There is no defensible universal implementation-cost figure in the reviewed evidence: OECD’s cost discussion says available examples vary and does not establish general costs by AI system type.
| Cost area | Include in the estimate | Typical timing |
|---|---|---|
| Product or model access | Subscription or per-user licenses where relevant; usage-priced model or API charges based on forecast input and output volume; contract limits and support terms. | Usually recurring; some procurement or setup charges may be one-time. |
| Discovery and delivery | Use-case assessment, vendor selection, procurement, integration, configuration, customization, deployment, and testing needed to make the system work in the target workflow. | Mostly one-time, with additional work for major changes or expansion. |
| Data | Acquisition, preparation, cleaning, rights, access, storage, and ongoing maintenance of the data needed for the system and its evaluation. | Both one-time and recurring. |
| Technology operations | Cloud or other infrastructure, compute, networking, storage, security, and the staff or services needed to operate them. | Usually recurring and potentially usage-dependent. |
| People and process change | Internal staff time, specialist hiring or contractors, training, workflow redesign, change management, and pilot administration. | Both one-time and recurring. |
| Quality, risk, and oversight | Evaluation, risk assessment, documentation, privacy and security review, human review, monitoring, incident response, retraining, and redeployment. | Ongoing, with additional costs at launch and when changes are made. |
| Supplier and exit management | Vendor support, contract management, contingency for uncertain usage or scaling, and the effort to migrate or exit if the arrangement ends. | Recurring, with potential transition costs. |
This is a practical planning checklist, not a complete accounting standard. Assign an accountable owner to each line, record the assumptions and whether the amount is one-time or recurring, and show how usage or scale affects it. Do not count existing staff time as free merely because it does not appear as a new invoice; it still has an opportunity cost.
Model usage-based charges explicitly
For a service priced by usage, estimate how many tasks or interactions the system will handle and the expected input and output usage for each. Show the source of the volume assumption, how usage may change at scale, and what happens if demand exceeds the forecast. Use the supplier’s current terms for the actual estimate; do not substitute a generic per-query assumption.
Include the cost of keeping performance acceptable
Deployment is not the end of quality work. OECD describes continued assessment, updating or retraining with current data, and redeployment as part of maintaining model performance. Budget for these activities, as well as for monitoring, escalation, and remediation if quality falls or the workflow changes.
How should AI risks and oversight be budgeted?
Budget governance as an operating activity across the system’s lifecycle, not as an unfunded review at the point of launch. The amount and form of oversight should reflect the use context and consequences of error. Name the people responsible for delivery, business outcomes, data, system quality, risk, human review, and escalation; one person may hold more than one role in a small project, but responsibilities should still be explicit.
Define the oversight plan before launch
- What must be tested and documented before use, and who approves release?
- Which outputs require human review, and how will reviewers handle uncertain or incorrect results?
- What quality, safety, or operational signals will be monitored, by whom, and how often?
- Who can pause or restrict use, and how are incidents escalated?
- Who funds investigation, correction, retraining, redeployment, or withdrawal?
- What changes—such as a new data source, model version, user group, or workflow—require reassessment?
NIST’s AI Risk Management Framework 1.0 is voluntary guidance intended to help organizations incorporate trustworthiness throughout AI design, development, use, and evaluation. NIST says the framework is under revision and identifies a separate Generative AI Profile released in 2024. OECD’s Due Diligence Guidance for Responsible AI offers an enterprise-oriented process for embedding responsible conduct and assessing impacts. These are guidance resources, not a universal legal mandate or a price list; applicable legal duties depend on jurisdiction and use case.
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Which alternatives should the business case compare?
Compare plausible ways to address the same business problem, not an AI option against an undefined “do nothing.” The best option depends on fit, data, staff capacity, control, time, and the cost of ongoing operation—not only the headline purchase price.
| Option | What to examine |
|---|---|
| Keep or improve the existing workflow | Whether process changes, better training, or non-AI automation could deliver the outcome with less implementation and oversight effort. |
| Buy a hosted product | Fit to the task, subscription or per-user pricing, customization limits, integration, supplier dependence, data handling, support, and exit terms. |
| Use a usage-priced model in an internal application | Expected usage charges, application and integration work, data controls, monitoring, staff review, and how costs change with volume. |
| Procure a tailored solution | Implementation and customization effort, supplier capability, ownership and access to data, support, governance responsibilities, and future change costs. |
| Build or customize internally | Staffing and specialist skills, development and infrastructure effort, responsibility for quality and security, maintenance, and opportunity cost. |
For each option, compare total lifecycle cost, task fit, data needs, implementation time, staff capacity, controllability, governance effort, supplier dependence, and ability to measure benefits. OECD’s cost discussion distinguishes licensing, volume-based use, custom development, and support, but does not establish one universally preferable approach.
How should funding and approval be staged?
Use decision gates so that later spending depends on evidence rather than optimism. Agree the measures and thresholds before each stage; otherwise, teams can keep extending a pilot without a clear basis for a production decision.
- Discovery and feasibility: validate the use case, baseline, data access, alternative options, cost drivers, and responsible owners. Stop if the problem or measurement plan is not sufficiently clear.
- Limited pilot: test within a defined workflow and user group, preserve a suitable comparison where feasible, and track outcome quality, adoption, usage, staff review effort, and oversight burden.
- Controlled production: proceed only if agreed evidence supports the expected outcome and the organization has funded monitoring, escalation, support, and remediation.
- Scale: expand only when results remain acceptable at the new volume or scope. Revisit usage costs, workflow effects, system quality, risks, and the capacity to oversee it.
At every gate, update the business case with observed costs and outcomes. A pilot can establish whether an approach is promising under specific conditions; it does not by itself prove that broader rollout will have the same economics or performance.
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The available evidence does not support a single reliable price for implementing AI across businesses. Cost depends on the system, usage, scale, data, integration, staffing, infrastructure, and support model. OECD’s 2025 government cost discussion uses varied public-sector examples and says it found no general research estimating development or use costs by system type; those examples are not private-sector price guidance.
For context only, UK DSIT figures cited by OECD in 2025 reported that 8% of UK government AI projects showed measurable benefits and 16% showed forecast costs. These are public-sector findings, not a current rate for private companies. They underscore why a business case should forecast costs as well as define outcome measures.
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