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What the available evidence says about generative AI’s business value
Adoption is rising faster than clearly reported enterprise-level financial impact. Stanford HAI’s Artificial Intelligence Index Report 2025: Economy says the share of respondents reporting organizational AI use rose from 55% in 2023 to 78% in 2024. The share reporting generative AI use in at least one business function rose from 33% to 71% over those years. These are adoption figures, not measurements of return on investment (ROI). Stanford HAI’s 2025 AI Index also summarizes survey findings that function-level savings and revenue gains were usually reported at low levels.
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In its March 2025 article reporting results from a 2024 global survey, McKinsey & Company said more than 80% of respondents reported that their organizations were not yet seeing a tangible impact from generative AI use on enterprise-level earnings before interest and taxes (EBIT). Separately, 17% said at least 5% of their organization’s EBIT in the previous 12 months was attributable to generative AI. These are respondents’ reports and attributions, not audited causal estimates. The online survey ran July 16–31, 2024, collected 1,491 responses from 101 nations, and reflects respondents’ views rather than a forecast for any particular company. McKinsey’s survey and findings.
The distinction matters: a team may save time or report lower costs without the company realizing cash savings or higher earnings. The financial effect depends on what happens next—whether the freed capacity is redeployed, output increases, an expense is avoided, or a valuable customer or business outcome improves.
What function-level survey results can—and cannot—tell you
Function-level reports can help identify hypotheses worth testing, but they do not establish that a particular deployment caused a gain. Stanford HAI’s 2025 AI Index summarizes survey-reported effects among respondents using AI in the named function. The figures below concern AI generally, not generative AI alone:
| Reported measure | Business function | Respondents reporting the outcome |
|---|---|---|
| Cost savings | Service operations | 49% |
| Cost savings | Supply chain management | 43% |
| Cost savings | Software engineering | 41% |
| Revenue gains | Marketing and sales | 71% |
| Revenue gains | Supply chain management | 63% |
| Revenue gains | Service operations | 57% |
These percentages describe respondents who reported savings or gains, not the size of the average financial effect. Stanford HAI says most reported savings were below 10%; the most common reported level of revenue increase was below 5%. Its summary draws on survey evidence, including McKinsey results, and should not be treated as a separate experiment or independent replication. Stanford HAI’s report explains the survey context.
McKinsey’s 2025 State of AI article also describes respondents increasingly seeing revenue increases and cost reductions in business units using generative AI, compared with earlier 2024 survey results. Its reported revenue findings cover respondents whose organizations regularly used generative AI in the relevant function, and exclude some response categories. The article names strategy and corporate finance, supply chain and inventory management, marketing and sales, service operations, software engineering, and product or service development. Those function-level reports are not an all-company success rate. McKinsey’s article provides the survey details.
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Build the case around a workflow, not a technology purchase
Write down the workflow you intend to change before comparing tools or estimating returns. Define its users, volume, inputs, outputs, handoffs and decision points. Identify the person accountable for the result and the business objective the change should advance.
Record a baseline for the current process over a representative period. Depending on the workflow, useful measures may include cycle time, cost per task, throughput, error or rework rate, service level, customer outcomes and staff time. Note how the measures are collected, which population they cover and any seasonal or workload variation. Without a baseline, a post-launch improvement is difficult to distinguish from normal variation or other process changes.
Set a threshold for a meaningful result before the pilot. For example, specify what improvement would justify further investment and what quality, risk or service levels must not worsen. The threshold should reflect the organization’s objectives and risk tolerance; survey averages do not supply it.
Estimate the full cost of changing the process
Include one-time implementation work and ongoing operating costs over the same period used to estimate benefits. The total will depend on the workflow, organization, deployment choices and vendor terms; the cited surveys do not provide a universal cost benchmark.
- Technology: software or model access, platform usage, hosting where applicable, and any vendor or licensing charges.
- Data and integration: data preparation and access, system connections, workflow configuration, testing, and maintenance.
- Security, privacy and governance: access controls, evaluations, monitoring, documentation, reviews and other controls appropriate to the use case.
- People and process: workflow redesign, staff and manager time, role-based training, human review and change management.
- Ongoing operations: support, quality checks, performance monitoring, incident handling, and the people or services needed to correct failures.
Separate setup costs from recurring costs, and identify who owns each estimate. Include internal staff time even when it does not appear as a new vendor invoice: it is still a resource committed to the change.
Show how an operational improvement becomes financial value
Do not treat time saved as cash saved by default. Trace each proposed benefit to a value mechanism, and keep different kinds of benefit separate until the assumptions behind them are clear.
- Capacity released: time is freed, but the financial value depends on whether staff can use it for other work the organization values.
- Cash cost avoided: a budgeted expense, paid service or planned hire is actually reduced or avoided.
- Higher throughput: more work is completed, with a credible explanation of whether demand exists and how additional output contributes value.
- Quality or customer outcome: errors, rework, delays or service outcomes change in a way the organization can measure and value.
- Risk effect: a potential reduction or increase in exposure is identified separately, with assumptions that can be reviewed.
- Revenue effect: a change in sales or retention is connected to a measurable outcome rather than attributed to AI simply because it followed deployment.
For a financial comparison, use a consistent time period and make the assumptions visible. One simple framing is (measured benefits minus total costs) divided by total costs. Define which benefits qualify, whether costs include internal labor and ongoing oversight, and how uncertain effects are treated. Present the estimate as a scenario based on those assumptions, not as a guaranteed return. Keep non-financial outcomes visible rather than forcing them into an unsupported dollar value.
Pilot, measure and update the estimate
- Choose the test population and workflow. Define which users and tasks are included, who can use the system, and how the current process will be compared.
- Preselect outcome and adoption measures. Track the business measures from the baseline alongside actual use, human review, errors, rework and failures. Define how each measure will be collected and over what period.
- Set review and stop conditions. Decide in advance what result supports expansion, what requires a change to the workflow or controls, and what should pause or end the pilot.
- Compare results with the baseline. Examine quality and risk as well as speed or volume. Record process changes and other factors that could explain a change; do not claim causation from a simple before-and-after difference alone.
- Revise the business case. Replace assumptions with observed results where possible, include operating and oversight costs, and show remaining uncertainty before approving a broader rollout.
McKinsey’s 2025 article identifies defined key performance indicators, feedback mechanisms, phased rollouts, role-based training and effective embedding in processes among practices used by organizations working to scale generative AI. These are reported organizational practices, not a guarantee of positive ROI. See McKinsey’s discussion of value-capture practices.
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When deciding which workflow to test first, compare candidates on factors that affect both value and delivery effort:
- The business objective and plausible value mechanism.
- How strong and measurable the baseline is.
- Data sensitivity, quality, access and permission requirements.
- Integration effort and disruption to existing work.
- How much human review is needed and what happens when the system fails.
- Recurring model, platform and operating costs.
- Governance, security, privacy and other controls needed for the use case.
- Whether performance can be monitored and the approach scaled responsibly.
A promising use case is not necessarily the one with the largest theoretical time saving. A less dramatic opportunity may be easier to measure, lower risk, or more practical to integrate. The cited evidence does not establish a universally best model, vendor, deployment architecture or use case; those choices depend on organizational requirements and validated results.
Include governance and risk in the investment decision
Generative AI can introduce risks that affect whether a workflow is appropriate to automate or augment, what controls it needs, and how much ongoing oversight will cost. Assess them across the system’s lifecycle, matching safeguards to the task, data, failure consequences and organizational risk tolerance.
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, published July 26, 2024, is a voluntary, cross-sector companion to AI RMF 1.0. It describes generative AI risks and suggested actions for governing, mapping, measuring and managing them. It is a risk-management resource, not a universal ROI calculator or a guarantee of commercial success. Read the NIST Generative AI Profile.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11For the business case, translate risk review into concrete work and decision criteria: who approves use, what data may be used, where human review is required, how performance and incidents are monitored, and who can pause or change the workflow. Account for the resources needed to operate those controls. A deployment that performs well in a narrow test may still be unsuitable if its risks cannot be managed in production.
What to put in the decision document
- Decision requested: the workflow, intended users, scope and business objective.
- Baseline: current performance, measurement period, data source and process owner.
- Value logic: expected operational outcomes and the specific mechanism by which each could create financial or other business value.
- Cost estimate: setup, integration, people, training, governance, operations and incident response, with assumptions and time horizon.
- Evidence quality: which inputs are measured locally, which are assumptions, and which are external survey findings.
- Pilot plan: adoption and outcome measures, review cadence, comparison method, and thresholds for expanding, revising or stopping.
- Risk and accountability: relevant controls, owners, failure consequences and escalation path.
Use external survey evidence to frame a question to test, not to plug a market-wide percentage into a company forecast. Approve a larger investment when local results and the full operating model support it—not merely because a function-level survey reported savings or because a pilot reduced task time.
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