AI can reduce the cost or time of a task, but that is not the same as proving a lasting net saving. A company that counts faster output while leaving out software upkeep, security work, worker impacts and infrastructure demand may discover that some of the bill was delayed—not avoided. That risk is conditional, not inevitable: AI can also improve work, and the result depends in part on how well an organization manages the systems around it.
Why reported productivity gains do not settle the ROI question
There is evidence that people can find AI useful at work. In its 2024 surveys of employers and workers, the OECD reported that four in five workers surveyed said AI improved their performance at work, while three in five said it increased their enjoyment of work. Those are workers’ reported experiences—not audited company savings, a measure of net return, or a forecast for every job or business. The OECD report discusses both opportunities and risks.
A faster task can create value, but the financial result depends on what happens next. Does the time saved reduce paid hours or outside spending, allow the same team to handle more valuable work, or simply increase output without changing costs? Does the AI-assisted work require extra review, rework, or support? Those distinctions matter when a short-term productivity figure is used to justify a larger investment.
Software Improvement Group (SIG) made the point in its 2026 State of Software account: productivity gains are real, but organizations need to measure what they are doing and understand the foundations on which they are moving faster. Its argument is not to stop using AI; it is to avoid treating speed alone as evidence that the underlying work is healthy. SIG’s 2026 report announcement describes AI as capable of accelerating either good practices or existing weaknesses.
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Where a low upfront cost can leave a later bill
For a finance review, it helps to separate the visible saving from costs that may be deferred, shifted to another team, or difficult to see in a deployment budget. These are areas to measure, not costs that every AI project will necessarily incur.
| What looks cheaper or faster | What can be missed | What to track |
|---|---|---|
| More software changes produced in less time | Review, rework, maintenance, architecture changes, security remediation | Defects, review and remediation effort, maintainability, security controls, time needed for updates |
| Fewer staff hours assigned to a task | Changed workloads, work intensity, task handoffs, job changes and worker experience | Hours and tasks shifted, workload, worker feedback, quality of completed work |
| More AI use or automated processing | Compute, electricity and cooling demand, as well as supporting infrastructure | Usage and operating costs, compute demand and available energy or cooling measures |
Software: output can arrive before maintainability
AI-assisted coding can make it easier to produce changes quickly. But code that is hard to understand or maintain can increase the effort required to review, update, secure, or repair a system later. A 2024 survey of 53 AI practitioners in the Journal of Systems and Software reported that respondents viewed technical-debt issues in AI-enabled systems as serious, including effects on understandability and security. The study also described limited support for identifying and managing those issues beyond manual identification and ad-hoc refactoring. Its small practitioner survey is evidence about reported experience—not proof that AI-generated code universally creates more debt than human-written code. Read the study.
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SIG’s 2025 benchmark report offers a broader software-quality warning, but its figures should be read within its published benchmark scope. SIG says the underlying research covered more than 18,000 systems. Its report page highlights that 60% of systems have a low degree of security controls; that poor software quality is associated with a €7 million increase in maintenance costs in the largest systems; that poor architecture can make updates 40% slower; and that 73% of AI and big-data systems have quality issues. These are SIG’s benchmark headlines, not a universal forecast for an individual company or evidence that AI caused every quality issue in those systems. Consult the report’s definitions and methodology before comparing those figures with a particular portfolio. SIG’s State of Software 2025 report page.
Workers: lower labor spending is not the only outcome
Using AI to reduce labor costs can change the tasks people do, the pace of work, and the way employee data is handled. The OECD’s 2024 account notes worker concerns involving work intensity, data collection and use, and inequality alongside reports of improved performance and enjoyment. It estimates that occupations at highest risk of automation account for about 27% of employment in OECD countries. “At risk” describes exposure to automation; it does not mean 27% of jobs are expected to disappear.
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The relevant financial question is therefore not just how many hours a tool appears to save. A company should also identify whether work is eliminated, shifted, or intensified, and whether new review or oversight tasks emerge. The National Academies’ 2025 consensus study, Artificial Intelligence and the Future of Work, examines AI’s potential to complement or replace labor and change demand for expertise; it provides a wider context for considering task and workforce changes rather than assuming a single outcome. See the National Academies study.
Infrastructure: digital work still uses physical resources
AI-related computing depends on data-center equipment, electricity, and cooling. The U.S. Government Accountability Office’s 2025 assessment describes electricity and water needs associated with powering and cooling AI-related equipment. It reports an International Energy Agency estimate that U.S. data centers used about 4% of electricity demand in 2022, with a potential rise to 6% in 2026. Those figures refer to data centers overall, not AI-only consumption, and the 2026 figure is a potential estimate rather than a measured outcome. GAO’s environmental and human-effects assessment discusses the resource issue.
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Why organizational readiness changes the financial result
AI does not arrive in a vacuum. SIG’s 2026 account and DORA’s 2025 research both frame AI as an amplifier: stronger engineering and management practices can help an organization get more from AI, while weak foundations can make existing problems move faster or become more consequential. This is a useful way to evaluate readiness, not a claim that one causal pattern applies identically to every business. DORA’s 2025 report focuses on AI-assisted software development.
Public-sector adoption illustrates why adoption counts and successful outcomes must not be conflated. In its review of 11 selected U.S. federal agencies, GAO found that inventoried AI use cases rose from 571 in 2023 to 1,110 in 2024, while generative-AI cases rose from 32 to 282. Agency officials also cited policy, technical-resource, and budget challenges. These counts apply only to the agencies and inventory review covered by GAO; they are not a measure of private-sector savings or proof that adoption itself produced a return. GAO’s federal-agency review.
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How to judge whether an AI saving is likely to last
There is no universal AI ROI formula established by the sources cited here. A company can still make a more credible decision by defining what success means before deployment and checking outcomes beyond the launch period. Use the following as a practical measurement approach, not as a validated universal checklist.
- Choose the outcome before choosing the metric. Specify whether the initiative is meant to reduce external spending, shorten cycle time, increase completed work, improve service, or free employees for other tasks. Name the workflow and the people responsible for its costs and quality.
- Record a baseline. Before rollout, capture the current cost and time for the workflow, its output and error or rework levels, relevant security and maintenance measures, and the worker experience where applicable. Record the period and scope so later comparisons use the same boundaries.
- Count the full cost of using AI. Include the tool or service, integration, training and oversight, human review, rework, maintenance, and any security remediation that is attributable to the workflow. Track infrastructure use and operating costs where those data are available. Do not count an estimated avoided cost as cash saved unless the spending actually changes.
- Review quality and risk with speed and expenditure. For software, monitor maintainability, architecture, security controls, defects, and the effort to review or remediate changes. For workforce applications, monitor task shifts, work intensity, worker feedback, and output quality. For infrastructure, record compute and cooling demands where measurable.
- Reassess after the initial deployment. Compare results with the baseline over a defined period and look for costs that appear in another team or later stage. If quality, security, workload, or operating costs worsen, account for the corrective work before expanding the project.
The aim is to distinguish three different results: a task became faster; a team produced more or better work; or the organization actually reduced total cost over time. Those outcomes can overlap, but one does not automatically prove the next.
What decision-makers should take from the evidence
The warning is not that AI savings are imaginary. Worker surveys report perceived benefits, and the sources describe real opportunities for productivity. The financial risk is narrower: treating a deployment-stage saving as a lasting net gain before checking the maintenance, security, people, and infrastructure costs that may accompany it. Make the decision on measured outcomes over time—and on whether the organization can sustain the quality and oversight required to keep those gains.
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