To make a defensible business case for generative AI, start with a specific workflow, measure its current cost and performance, estimate the full cost of changing it, and test whether the change produces a business outcome you can verify. Survey results show reported benefits in some functions, but they do not establish that a particular company will earn a positive enterprise-wide return.
What the available evidence says about generative AI returns
Reported value is easier to see in particular business functions than in enterprise-wide earnings. That distinction matters: a team may save time or report lower costs without the company realizing equivalent cash savings, increased revenue, or higher EBIT.
| Evidence | Reported finding | What it does—and does not—show |
|---|---|---|
| McKinsey & Company’s 2025 State of AI article, reporting its 2024 survey | More than 80 percent of respondents said their organizations were not seeing a tangible impact on enterprise-level EBIT from generative AI use. Separately, 17 percent said at least 5 percent of their organization’s EBIT in the previous 12 months was attributable to generative AI. | These are respondent reports, not audited or independently established causal estimates. The survey ran July 16–31, 2024, collecting 1,491 responses from 101 nations. The findings do not mean that more than 80 percent of companies get no value, nor do they predict an individual company’s result. McKinsey & Company |
| Stanford HAI’s 2025 AI Index, summarizing adoption survey evidence | Organizational AI use was reported by 78 percent in 2024, up from 55 percent in 2023. Reported generative AI use in at least one business function rose from 33 percent in 2023 to 71 percent in 2024. | These are adoption figures, not ROI measurements, and the broader AI-use figure is not generative-AI-only. Stanford HAI |
| Stanford HAI’s 2025 AI Index, reporting function-level AI outcomes | Among respondents using AI, 49 percent in service operations, 43 percent in supply chain management, and 41 percent in software engineering reported cost savings. For revenue gains, the corresponding reported shares were 71 percent in marketing and sales, 63 percent in supply chain management, and 57 percent in service operations. | These figures concern AI use as summarized by the Index, not generative AI alone. Most reported savings were less than 10 percent; the most common reported revenue-increase level was less than 5 percent. They describe respondents in functions, not all companies, and do not establish causal impact. Stanford HAI |
McKinsey also reports that respondents whose organizations regularly used generative AI in a function increasingly reported revenue increases and cost reductions in business units compared with earlier 2024 survey results. Those reports cover particular functions and exclude some response categories; they are not an all-company success rate. Stanford HAI’s account of low reported function-level savings and revenue gains summarizes McKinsey survey results, so it should not be treated as a separate experiment or independent replication.
Together, these findings support testing defined use cases—not applying a market-wide productivity figure to a company’s forecast. The cited surveys do not determine your implementation costs, suitability, productivity lift, or net return.
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How to build a business case that can be tested
1. Choose a workflow and establish its baseline
Describe the task precisely: who does it, how often, with what inputs, and what output is required. Record current volume, cycle time, labor or other costs, error and rework rates, service levels, and relevant quality measures. Name the person accountable for the workflow and specify what improvement would matter to the business.
Keep the existing process as a comparison. Without a baseline, a faster demonstration or positive user feedback may be mistaken for a durable operational or financial gain.
2. Estimate the full cost of changing the workflow
Include more than model or software access. Depending on the use case, estimate integration, data preparation, security and privacy controls, evaluation, human review, training, process redesign, ongoing operations, and incident handling. Include internal staff time and any existing system or vendor costs that will remain.
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- Separate one-time implementation costs from recurring costs.
- Estimate the cost of review, correction, escalation, and fallback to the current process.
- Record vendor terms and assumptions behind usage, scale, and support estimates.
- Revisit estimates as usage grows; a pilot’s operating pattern may not represent a scaled deployment.
There is no universal cost benchmark in the cited sources. Use estimates specific to your organization, use case, deployment approach, and vendor terms.
3. State how the workflow creates value
Identify the mechanism before assigning a dollar value. Time saved is released capacity, not automatically a cash saving. It becomes financially meaningful when the organization can use the capacity to increase throughput, avoid hiring or other costs, improve service, or deliver an outcome customers value.
- Cost reduction: identify a cost that can actually be avoided or removed, net of added review and operating costs.
- Capacity or throughput: show how more work will be completed and whether that work has economic value.
- Revenue: connect the change to a measurable revenue outcome, while distinguishing revenue from profit or contribution margin.
- Quality and customer outcomes: define measures such as errors, rework, response time, or service levels and explain their business relevance.
- Risk: describe a risk reduction only when it can be assessed and monitored; do not treat an assumed avoided loss as guaranteed savings.
Keep the mechanisms separate and avoid counting the same benefit twice—for example, treating saved staff time as both a cash reduction and additional capacity unless both effects are independently realized.
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4. Calculate with explicit assumptions
A useful planning calculation is: net benefit = attributable benefits − total costs. If expressing a return percentage, a common calculation is (attributable benefits − total costs) ÷ total costs. State the time horizon, which costs are included, and how benefits are attributed. This calculation organizes assumptions; it cannot prove causation or make uncertain benefits certain.
Use conservative, expected, and upside cases when key assumptions are uncertain. For each case, identify which assumptions change—such as adoption, review effort, usable time savings, or demand—and show whether the case still meets the organization’s decision threshold.
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Choose the success measures before deployment and compare them with the baseline over a defined period and population. Track adoption alongside workflow outcomes: use alone is not evidence of value. Record human-review time, error and failure rates, exception handling, and any quality or service changes that could offset speed gains.
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- Set the scope: define the workflow, eligible users, volume, exclusions, and accountable owner.
- Set the measures: select baseline and outcome metrics tied to the stated business objective, plus measures for quality, review burden, and failures.
- Run the comparison: use the current process as a reference and document the period, population, and material changes during the pilot.
- Recalculate: replace assumptions with observed results, including recurring costs and the value actually realized.
- Decide in stages: expand only when results and controls meet the agreed criteria; revise or stop when they do not.
McKinsey’s 2025 article identifies defined KPIs, feedback mechanisms, phased rollouts, role-based training, and effective process embedding among practices used by organizations working to scale generative AI. These are reported practices, not guarantees of positive returns.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What organizational and risk work belongs in the case?
Generative AI changes a workflow as well as the technology stack. The business case should identify who will redesign the process, train users, review outputs, handle exceptions, monitor performance, and respond to incidents. It should also account for the effect on roles and responsibilities, not just the time spent using a tool.
Use a lifecycle risk framework to identify and manage risks appropriate to the use case. NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, published July 26, 2024, is a voluntary, cross-sectoral companion to AI RMF 1.0. It describes generative AI risks and suggested actions for governing, mapping, measuring, and managing them. It is not an ROI calculator and does not guarantee commercial success. NIST’s Generative AI Profile
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Translate that risk work into practical decisions: what data the system can access, who can use it, which outputs need human review, what failures require escalation, and how performance will be monitored after launch. The appropriate controls depend on the application, consequences of error, applicable obligations, and organizational risk tolerance; the cited framework does not replace jurisdiction- or sector-specific analysis.
How to compare candidate use cases or deployment approaches
Compare options against the same decision criteria instead of choosing on a compelling demo or a single productivity claim.
- Business objective and value mechanism: what outcome should improve, and how would that create value?
- Baseline and measurability: can current performance and the proposed result be observed reliably?
- Data: are data sufficiently available, accurate, authorized, and appropriate for the task?
- Integration and disruption: what systems or process steps must change, and who absorbs the transition work?
- Human review and failure consequences: when is review needed, and what happens if an output is wrong or unavailable?
- Ongoing cost and governance: what will it take to operate, secure, evaluate, and oversee the solution?
- Scalability and monitoring: can performance, adoption, cost, and risk be tracked as volume and users change?
The cited evidence does not establish a universally best model vendor, product, architecture, or use case. Select an approach based on organizational requirements and results validated in the workflow being considered.
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