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Build an AI business case by starting with a business problem, measuring the workflow before and after a defined pilot, and connecting any observed improvement to business value using a finance-approved method. Count the full cost—including staff time and ongoing oversight—and separate measured results from modeled estimates. That makes the case useful whether the evidence supports scaling, redesigning the workflow, or stopping.
What makes an AI business case credible?
A credible case explains how a specific AI-enabled change in a real workflow could improve an outcome the organization values, and how the organization will tell whether it did. It names the workflow, the accountable owner, the current performance, the intended change, and the evidence that will count as progress.
The sequence matters. Establish the baseline and success criteria before the pilot begins. If the target or measurement method is chosen after results are visible, it becomes harder to distinguish genuine improvement from a convenient interpretation of the data.
Do not treat AI activity as proof of business value. Usage and adoption can show whether people are using a tool; they do not establish that the workflow or business outcome improved. Measure the work and its results, then explain the connection between them.
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Microsoft Adoption frames the aim as establishing “a credible connection between the workflow improvement and the result, supported by evidence,” rather than claiming AI alone caused a business result. That is a useful standard for the whole case: be specific about what was observed, what was inferred, and what remains an assumption.
How do I choose a workflow and pilot?
Choose a workflow where a change is plausible, the business outcome matters, and a baseline can be measured. The best first pilot is not necessarily the most ambitious use of AI; it is one where the organization can learn enough to make a sound investment decision.
Start with candidate workflows
Potential candidates include sales meeting summaries, follow-up drafts, or proposal support; marketing campaign briefs and reporting; operations document summaries and process analysis; customer-support ticket summaries and suggested responses; and leadership report summaries and decision briefs. These are possibilities, not evidence of proven returns. A use case still needs to fit your workflow, data, constraints, and priorities.
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Use the same criteria for each option so that an exciting demonstration does not outweigh weak evidence or an unfavorable cost profile.
- Business importance: How closely does the workflow connect to a business priority, and who owns its outcome?
- Baseline quality: Can current performance be measured consistently from a reliable source?
- Expected workflow impact: What work might change, and can any released time or capacity be redirected to useful work?
- Full cost: What implementation, operating, training, data, governance, and oversight work will be required?
- Risks: What quality, privacy, security, adoption, or workflow problems could affect the result?
- Evidence and scale: How soon could meaningful evidence emerge, and could the approach be repeated beyond the pilot?
A pilot should be small enough to manage but sufficiently defined to test a business claim. Set out the participating users, the AI-enabled workflow change, the review period, and the comparison method. There is no single pilot size or duration established by the cited guidance; choose them based on the workflow and the evidence needed to make the decision.
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What should an AI business case include?
Use a written outline that connects the problem, the test, the measurements, and the investment decision. The following is a practical synthesis of guidance from Microsoft Adoption, Microsoft Learn, Microsoft’s agent measurement guidance, and the Australian National AI Centre—not a mandatory published standard.
- Business problem and owner: Name the workflow, identify who is accountable for its outcome, and explain why it matters to a business priority.
- Expected change: State what should change, for whom, and over what review period. Describe the workflow change rather than simply naming the AI tool.
- Baseline and target: Record current performance and the desired result using a consistent source and method. Define the success criteria before the pilot.
- Pilot design: Describe the AI-enabled workflow change, participating users, comparison period or method, and material dependencies.
- Measures: Select operational and business measures that fit the workflow. Include early indicators for course correction and later indicators to confirm outcomes.
- Value conversion: Explain how measured changes map to financial value. Identify the finance-approved rate or method, and distinguish observed results from modeled value.
- Full cost and payback: Include implementation and ongoing costs, state the payback estimate, and disclose the assumptions behind it.
- Risks and decision rule: Identify material risks and mitigations. Agree in advance on what evidence would mean scale, revise, or stop.
Which metrics should I use to measure AI ROI?
Choose a small set of measures tied to the workflow and business objective rather than collecting every available number. Where possible, use the business system or report that already records the work, and keep the measurement method consistent between baseline and pilot.
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| Value driver | Possible measures | What the measure can help establish |
|---|---|---|
| Efficiency | Cycle time, hours returned, cost per task or transaction, throughput, or capacity | Whether the work takes less time or uses resources differently. Time saved becomes financial value only when it is redirected to useful work and valued through an agreed method. |
| Quality | Error rate, rework, resolution, or escalation | Whether work is more accurate or needs less correction, and whether that change affects costs or service. |
| Revenue and customer outcomes | Conversion, retention, customer resolution, or satisfaction | Whether the workflow is associated with customer or commercial outcomes. Revenue and customer effects may take longer to connect confidently to AI. |
| Strategic and workforce value | Redesigned workflows or employee sentiment | Whether the change affects how work is organized or how employees experience it; qualitative feedback can help explain why quantitative results occurred or what should change. |
Some measures are leading indicators: they can help you see early whether the pilot is being used as intended or whether the workflow needs adjustment. Others are lagging indicators that take longer to confirm an outcome. Track both when relevant, but do not substitute usage for evidence of improvement.
ROI need not be reduced to a financial figure when the business case also concerns quality, confidence, capacity, satisfaction, or decision-making. The Australian National AI Centre identifies these as possible parts of value. Make clear which outcomes are measured, how they matter to the organization, and whether they have been converted into an approved financial estimate.
How do I turn a workflow improvement into financial value?
Agree on a conversion method with finance before translating a measured operational change into money. A shorter task time, fewer errors, or increased capacity is an operational result; its financial value depends on what the organization can do with that change and how it values the resulting work or savings.
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Separate observed results from modeled value
- Observed result: What the pilot measured, such as a change in cycle time, error rate, or hours spent on a task.
- Conversion assumption: The method or rate used to connect that change to financial value, such as a finance-approved value per productive hour.
- Modeled value: The estimate produced by applying the agreed conversion method. Label it as an estimate and disclose its inputs.
- Attribution: The explanation of how the workflow change relates to the outcome. Do not assign the entire outcome to AI if other changes or factors may have contributed.
For example, time released from a task does not automatically equal payroll savings or additional revenue. If staff use the time for other valuable work, the business case should explain that redeployment and how finance values it. If the time is not put to useful work, a theoretical time saving alone may not represent realized business value. Microsoft Learn calls unsupported claims based on theoretical time savings “the time-savings trap.”
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Use Microsoft’s sales example as a model, not a forecast
Microsoft Adoption illustrates the conversion process with a sales team. The example starts with 40 representatives who average 11.5 selling hours per representative per week, sets a 90-day target of 14 hours, and uses an illustrative actual result of 13.8 hours. The difference between the 11.5-hour baseline and the 13.8-hour example result is 2.3 additional hours per representative per week, or 92 hours per week across the team.
The illustration then applies an assumed finance-approved contribution of $50 per selling hour over 46 working weeks: 92 × $50 × 46 = $211,600 in modeled annual contribution. Against stated pilot costs of $18,000, Microsoft presents a modeled payback of approximately one month. These are example inputs and modeled results—not independently verified outcomes, a typical AI return, or proof that AI caused the increase. The 13.8-hour figure is a result in the illustration, not evidence from a generalizable study. Microsoft also cautions that more measured selling time is not itself revenue.
Which costs belong in the AI business case?
Count the resources required to implement and operate the initiative, not just the visible subscription or usage charge. An apparently low tool cost can obscure the people and process work needed to make a pilot usable, govern it, and sustain it.
- Licenses and usage: Include the relevant licenses and consumption or usage costs.
- Infrastructure and external support: Account for infrastructure and any support required from outside the organization.
- Staff time: Include time spent on implementation, testing, training, and change management.
- Data preparation: Include the work needed to prepare data for the workflow.
- Governance and oversight: Include ongoing governance and the staff effort needed to monitor and oversee the initiative.
- Scaling: Estimate the additional investment required if results justify extending the approach beyond the pilot.
Compare this full cost with measured value or a finance-approved modeled estimate. State which costs are one-time and which continue, and make the assumptions behind any payback estimate visible. A pilot that appears worthwhile in isolation may require additional investment to scale; include that in the decision rather than assuming pilot economics will carry over unchanged.
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How should I review results and decide whether to scale?
Set the decision rule before the results are in, then review early indicators while there is still time to adjust the pilot and lagging indicators when they can confirm outcomes. The decision should reflect both evidence of value and the costs, assumptions, risks, and repeatability of the approach.
Scale when the evidence is repeatable
Scaling is more defensible when the outcome is repeatable, the workflow change is workable, and the costs and conversion assumptions are acceptable. Consider the extra investment and governance needed beyond the pilot; a positive result in one setting does not by itself establish that the same result will occur everywhere.
Redesign when the workflow blocks value
If the bottleneck, handoff, or way work is organized prevents the expected improvement, redesign the workflow or the pilot rather than treating tool usage as success. Review qualitative feedback alongside the measures when it may help explain where the process is breaking down.
Stop when further investment is not supported
If results do not support further investment, stop rather than stretching assumptions to justify a preferred outcome. An inconclusive result is different from evidence of success or failure: it may indicate that the baseline, instrumentation, pilot duration, or workflow needs attention. State what the evidence can and cannot establish before choosing whether another test is warranted.
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A simple scorecard makes the business case easier to review by keeping the baseline, observed result, cost, and conversion assumptions together. Microsoft’s guidance references an AI Leader ROI Worksheet and scorecard; the useful principle is to capture the evidence and reasoning, not merely a single ROI number.
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| Scorecard field | What to record |
|---|---|
| Workflow and owner | The process being changed and the person accountable for its outcome |
| Problem and expected change | The business problem, what should change, who is affected, and the review period |
| Baseline and target | Current performance, desired result, data source, and measurement method |
| Pilot design | AI-enabled change, participating users, comparison method, and material dependencies |
| Measures and actuals | Selected operational and business measures, early indicators, later indicators, and observed results |
| Value conversion | Finance-approved rate or method, assumptions, and separation of measured results from modeled value |
| Full cost and payback | Implementation, usage, staff, data, governance, oversight, and scaling costs, plus the estimated payback and its assumptions |
| Risks and decision | Relevant risks and mitigations, plus the pre-agreed scale, revise, or stop threshold |
Keep the scorecard honest about uncertainty. A measured change, an estimated financial contribution, and a claim that AI caused a business outcome are different kinds of statements; label them accordingly.
What tools or outside help are required?
The business-case method does not require a particular AI platform. Microsoft Copilot Studio is relevant for organizations building or measuring agents in that ecosystem, but it is an implementation example rather than a prerequisite for defining a business problem, baseline, metrics, costs, and decision rule.
The work may call for input from finance to approve value-conversion assumptions, from workflow owners to define practical measures, and from data, security, privacy, or governance stakeholders to assess relevant risks. The cited guidance does not establish that a particular advisory provider is endorsed or necessary. Choose support based on the gaps the organization actually needs to address.
Frequently Asked Questions
How many users should an AI ROI pilot include?
The cited guidance does not set a standard participant count. Choose a group and review period that let you test the defined workflow and gather decision-quality evidence without assuming that one pilot size fits every organization.
Can an AI pilot have value if it does not reduce costs?
Yes. A case may include quality, capacity, satisfaction, confidence, or decision-making outcomes as well as financial returns. State which outcomes matter to the organization and avoid presenting a non-financial improvement as cash savings unless an agreed conversion method supports that claim.
Do I need a particular AI platform to build the business case?
No platform is required for the measurement method itself. A platform-specific guide may help with implementing or measuring agents in that ecosystem, but it does not replace the need to define the workflow, baseline, costs, and success criteria.
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