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To justify AI spending, start with a measurable business problem—not a model, vendor or impressive demo. Document the current baseline, state the value hypothesis, compare build, buy, reuse and no-change options, price the full lifecycle, and test the assumptions in a bounded pilot. Approve wider funding only when predefined value, quality, safety, adoption and cost thresholds are met. A CFO-ready case therefore combines operational evidence, financial scenarios, controls and explicit scale, change or stop gates.
What decision should an AI business case answer?
The first page should make one decision unavoidable: what funding or permission is being requested, for which workflow, and from whom. A proposal that begins with a large language model or a supplier catalogue forces executives to infer the problem; a proposal that begins with business impact gives them something they can approve, reject or reshape.
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Use a sentence such as: “Approve a 12-week pilot for claims triage in the operations team, with a capped budget and a review at week 10.” Name the accountable business owner, the affected process or customer journey, the amount requested, the period covered and the decision date. Separate the pilot decision from the later production decision so an early experiment does not quietly become a permanent commitment.
Explain why the problem matters now
Describe the cost of delay in operational terms: a backlog, missed service levels, avoidable rework, lost sales capacity, rising fraud exposure or a regulatory deadline. Microsoft guidance advises identifying the business use case before selecting an AI architecture. The “why now” argument should be specific enough that a nontechnical executive can challenge it without debating model terminology.
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Identify who owns the outcome
Give one person authority over the result, even if technology, finance, legal and operations share delivery. The owner must be able to change the process, assign staff for testing and accept or reject the measured outcome. An IT project manager can own delivery milestones, but the business owner owns whether the workflow actually improves.
How do you establish a baseline and value hypothesis?
Measure the current process before introducing AI. A value claim such as “save time” is not investable until the organisation knows how much work exists, what it costs, how quality is measured and what people will do with released capacity.
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Capture the baseline
Use a consistent observation period that represents normal demand, including peak and exception cases where they materially affect risk. Record:
- Transaction or case volume and its seasonality.
- Cycle time, queue time and service-level performance.
- Staff hours, fully loaded labour cost and specialist time.
- Error, rework, escalation and abandonment rates.
- Quality, customer-satisfaction or compliance measures.
- Revenue, retention, loss, fraud or risk metrics connected to the workflow.
- Current software, data, support and process costs.
Write down the data source, owner, extraction date and calculation method. If the baseline is a sample, state its size and selection rule. Do not mix a measured baseline with an aspirational target in the same column.
Turn capability into a testable hypothesis
Express the expected change as a measurable statement: “For representative cases, AI-assisted triage will reduce median handling time by 20% while keeping critical-error rate below 1% and maintaining the existing customer-service score.” Include the population, comparison group, time window and minimum acceptable quality. The Australian National AI Centre recommends defining the problem, expected outcome and signs of progress before investing.
Define attribution before claiming value
Decide what will count as an AI effect. A phased rollout can compare teams or periods; a controlled pilot can compare AI-assisted and current-state work; a matched sample can adjust for case complexity. Record other changes—staffing, pricing, policy, seasonality or automation—so benefits are not credited to AI merely because they appeared after launch.
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Distinguish time released from money saved
Time saved becomes financial value only when capacity is redirected to useful work. The National AI Centre puts this plainly: “Time saved only delivers value if it’s redirected to useful work, such as serving customers, improving quality or growing the business.” Specify the redeployment: additional cases handled, faster response, fewer contractors, avoided overtime or a reduction in planned hiring. If no capacity or cash changes, report the improvement as an operational benefit rather than booking it as savings.
Which alternatives should the business case compare?
A credible case compares the proposed AI workflow with realistic alternatives, including doing nothing. The best technical option is not automatically the best economic option if a process change, conventional analytics or an existing product achieves the outcome with less risk.
| Option | When it may fit | Questions to answer |
|---|---|---|
| No change | The problem is tolerable, temporary or cheaper to absorb. | What does delay cost, and what trigger would reopen the decision? |
| Process redesign | Handoffs, policies or unnecessary work cause most of the loss. | Can standardisation solve the issue without a model? |
| Conventional analytics or rules | The data is structured and decisions are deterministic. | Would rules, search, forecasting or workflow automation meet the target? |
| AI-assisted work | People remain accountable and the task involves language, prediction or pattern recognition. | Where are human review, confidence thresholds and fallbacks required? |
| Buy | A mature product already covers the workflow and required controls. | What are integration, data-use, exit and usage-pricing terms? |
| Build | The workflow or data advantage is distinctive and internal capability is sustainable. | Can the organisation operate, secure and update it for its full life? |
| Reuse an existing platform | Shared identity, data, monitoring and support already exist. | What incremental cost and constraints arise from using the platform? |
UK guidance recommends evaluating build, buy or reuse against uniqueness, product maturity, skills, operations, integration and ethics, and using a small proof of concept to test the hypothesis. Add reversibility and portability to that comparison: a cheaper first year can be expensive if data, prompts, evaluations or users cannot move later.
Score options consistently
Use a weighted scorecard rather than allowing the most persuasive demonstration to decide. Suggested axes are expected value and time to value; total cost and usage sensitivity; data readiness and integration effort; reliability, quality, safety, privacy and regulatory exposure; adoption and change burden; reversibility and vendor dependence; and the strength of evidence from a pilot or comparable deployment. Keep the weights visible, and show how the ranking changes when a high-uncertainty assumption is worsened.
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What belongs in the total cost of ownership?
AI spending is more than a licence or API rate. Price the complete lifecycle from discovery through retirement, and show which costs are one-time, recurring, variable or contingent.
Direct delivery costs
- Software licences, subscriptions, model or inference charges and minimum commitments.
- Cloud infrastructure, storage, networking, accelerators and environments for development, testing and production.
- Data acquisition, cleansing, labelling, migration, retrieval indexes and ongoing data refresh.
- Integration with identity, workflow, records, customer channels and reporting systems.
- External implementation, specialist support, legal review and assurance.
Indirect and operational costs
- Process redesign, employee training, communications and temporary productivity loss during adoption.
- Evaluation datasets, red-team testing, accessibility checks, security reviews and model validation.
- Human review, exception handling, incident response, audit evidence and documentation.
- Monitoring for quality, drift, latency, bias, abuse, data leakage and service availability.
- Product management, platform engineering, help-desk support, upgrades and vendor management.
- Retirement, data export, contract termination, replacement tooling and records retention.
Opportunity and consumption costs
Include the internal time that could have been spent on another initiative, plus the cost of delayed projects. Model usage as a driver, not a footnote: requests per user, tokens or documents per request, peak concurrency, retention period, retries and the share of cases routed to expensive models can all change the bill. McKinsey’s Enterprise AI FinOps survey in 2026 found that 93% of surveyed organisations reported exceeding their AI budgets, while most expected spending to rise by at least 25% over the following 12 months. The practical response is demand shaping: McKinsey says, “The better approach is to focus on shaping demand to create the most value for the business.”
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Show cost uncertainty explicitly
Present a low, expected and high case for volume, unit cost, staffing and implementation duration. Identify the assumptions that can move the result most, then set a monitoring trigger—for example, a monthly cost-per-completed-case limit or a review when usage exceeds the forecast by 15%. A capped pilot budget should include a small contingency, but contingency is not permission to spend without a gate.
How do you calculate ROI and payback for an AI project?
Calculate financial return from incremental benefits minus incremental costs, then show the assumptions and confidence range. A single percentage without a measurement period, attribution method or cash-flow view is not a business case.
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Translate each benefit into an operational driver and a financial rate:
- Capacity: verified hours released × the portion redirected to revenue, service or avoided hiring × the applicable loaded rate.
- Quality: reduction in errors or rework × cost per incident, including downstream handling.
- Revenue or retention: incremental conversions, renewals or capacity × contribution margin, not gross sales.
- Risk: change in expected loss, regulatory exposure or control failure probability, with the confidence level stated.
- Cost avoidance: expenditure that would otherwise occur and has an approved budget or documented commitment.
Do not count the same benefit twice. For example, hours used to process more cases cannot also be counted as a headcount reduction unless the staffing plan supports both claims.
Use transparent formulas
For a one-year view, use net benefit = realised benefits − total incremental cost and ROI = net benefit ÷ total incremental cost. For projects with material timing differences, show payback month and discounted cash flow using the organisation’s approved hurdle or discount rate. Separate cash benefits from non-cash improvements such as faster service or better employee experience.
Report ranges, not false precision
Give a base case, downside case and upside case. Explain which variables change in each scenario: adoption, accuracy, volume, unit consumption, implementation effort or the value assigned to capacity. BCG’s 2025 survey reported median finance AI ROI of 10%, while 20% was a target for many finance leaders. That gap is a reason to test optimistic assumptions, not a universal prediction for every project.
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Set the measurement window
Choose a period long enough to include onboarding and normal demand, but short enough to make a decision. State when benefits start, whether ramp-up is excluded, how seasonality is handled and who signs off the result. Finance should be able to reproduce the calculation from the baseline, usage records, payroll or margin data and the approved attribution rule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you design an AI pilot and its funding gates?
A pilot is a decision instrument, not a miniature production launch. It should be small enough to cap exposure and realistic enough to test the conditions that determine value.
Define the pilot charter
- Choose a bounded workflow, user group, data set and fixed start and review dates.
- Lock the baseline, target metrics, sampling method and comparison group before users see results.
- Name the business owner, technical lead, risk approver and finance measurement owner.
- Specify permitted data, retention, access, human-review points and prohibited uses.
- Set a maximum spend, usage ceiling and escalation path for incidents or budget variance.
- Train representative users and record participation, overrides, errors and reasons for rejection.
Test value, quality and safety together
Measure outcome metrics alongside model metrics. Accuracy alone does not prove value if users ignore the output, the workflow slows down or errors become more costly. Include representative edge cases, inaccessible or ambiguous inputs, security and privacy tests, and a way to revert to the current process. Track adoption as active use, completion and appropriate override—not log-ins.
Lock the scale, change and stop gates
Write the gates into the approval before the pilot begins. A scale gate might require the target cycle-time improvement, a critical-error rate below the safety ceiling, minimum adoption, a cost per completed case below the approved limit and no unresolved high-severity control issue. A change gate applies when value is promising but a threshold misses; it should specify the redesign, owner and new review date. A stop gate applies when safety, economics, data rights or adoption fail, and it must include data retention and user communication steps.
Fund in tranches
Release discovery, pilot and scale funding separately. The next tranche should require a signed results sheet, updated forecast, open-risk register and an explicit decision. This prevents sunk-cost pressure from turning a weak experiment into an open-ended programme.
How do you govern AI spending as a portfolio?
After several pilots, manage AI as an investment portfolio rather than a collection of disconnected technology projects. John Marcante, former global CIO of Vanguard and Deloitte US CIO in residence, summarises the principle: “AI shouldn’t be managed as an IT budget or a collection of technology projects.”
Create a value register
Maintain one record for every approved use case with its owner, baseline, hypothesis, expected and realised benefits, total cost, usage, risk rating, controls, evidence links, decision dates and current status. Tag shared platform costs and allocate them by measurable drivers such as users, cases, compute or storage. Use showback first if teams need to learn their consumption; move to chargeback only when the allocation method is stable and behaviour should change.
Set recurring reviews
Review monthly during ramp-up and at least quarterly after stabilisation. Compare forecast with actual spend and value, inspect cost per outcome, review incidents and exceptions, confirm that controls still match the use, and reallocate funding toward evidence-backed work. Retire or redesign uses that no longer meet their gate; an approved project should not become permanent simply because it is familiar.
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Business owners are accountable for outcomes and process decisions. Technology owners are accountable for reliability, security, integration and recoverability. Risk, privacy, legal and compliance owners define control requirements and approve exceptions. Finance owns benefit measurement, cost attribution and the consistency of investment cases. Write these responsibilities into the operating model rather than relying on informal agreement.
What should a one-page AI business case contain?
Use the following layout as the executive summary, with appendices for technical and financial detail.
- Decision: funding amount, period, option selected and approval requested.
- Problem and owner: affected workflow, customer or risk, accountable executive and reason for action now.
- Baseline: volume, time, cost, quality, staffing, revenue or risk measures, data source and observation period.
- Value hypothesis: expected operational change, financial translation, attribution method and measurement window.
- Options: no change, process redesign, analytics or rules, AI-assisted work, build, buy and reuse, scored on value, cost, risk, adoption and reversibility.
- Cost model: one-time, recurring, variable, opportunity, governance, adoption, scaling and retirement costs, with low, base and high scenarios.
- Pilot design: users, data, duration, owner, controls, budget cap, baseline and test method.
- Gates: exact scale, change and stop thresholds, decision date and funding tranche.
- Governance: value-register entry, cost tags, review cadence, control owners and reporting route.
Attach the calculation workbook, data dictionary, evaluation results, risk assessment, procurement assumptions and implementation plan. The one-page view earns attention; the evidence pack makes the approval defensible.
Frequently Asked Questions
Can AI implementation costs be capitalised in the accounts?
Accounting treatment depends on the jurisdiction, contract and stage of development. Ask the controller to separate research, configuration, implementation, subscription and ongoing operating costs, and document the policy before the project starts; a business case should never assume capitalisation to make its return look better.
What discount rate should an AI business case use?
Use the organisation’s approved hurdle or discount rate for comparable investments, then show a risk-adjusted sensitivity rather than inventing a special AI rate. If the project has unusual regulatory, vendor or demand risk, present that risk in the scenarios and approval conditions instead of quietly changing the rate.
How can shared AI platform costs be allocated fairly?
Define a driver that reflects consumption or benefit, such as completed cases, active users, inference volume, storage or reserved capacity. Publish the allocation rule, reconcile it to invoices, and review it when usage patterns change; an arbitrary equal split hides the teams creating the demand.
What evidence should a board see before approving scale?
Show a signed baseline, pilot sample and comparison method, realised value, cost per outcome, adoption, safety and privacy results, unresolved risks, and the downside case. Include what would cause management to pause or reverse the investment, so approval covers both the opportunity and the limits of the evidence.
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