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AI can reduce the time and cost of work, but a faster or cheaper task is not automatically a net saving. If an organization counts deployment spend and hours saved while leaving out maintenance, security, worker impacts and infrastructure, it may mistake an early productivity gain for a durable return. Whether AI compounds those costs or helps control them depends in part on the practices already in place.
What AI cost savings can leave out
A narrow savings calculation often compares a tool’s price with the time workers report saving. That is a useful starting point, not a full accounting. The real question is whether the workflow delivers its intended outcome at lower total cost over time, without creating offsetting work or unacceptable risk.
| Accounting horizon | What the calculation captures | What may be missed |
|---|---|---|
| At deployment | License or setup costs and initial time saved on a task | Training, integration, review, and changes to existing workflows |
| During operation | Ongoing task speed and operating expenditure | Errors, security review, maintenance, compute, and cooling demands |
| Across the lifecycle | Whether the process remains useful and affordable | Remediation, architecture constraints, workforce changes, and the cost of updating systems |
These are categories to examine, not costs that every AI project will incur. A tool that automates a contained, low-risk task may have a straightforward return. A tool embedded in software development, customer decisions, or core operations can affect systems and people well beyond the task it initially speeds up.
Can AI-generated code create technical debt?
It can contribute to debt when code is hard to understand, maintain, secure, or integrate—but the available evidence does not establish that AI-generated code invariably creates more debt than code written without AI. A 2024 survey of 53 AI practitioners in the Journal of Systems and Software reported substantial perceived severity and impacts on understandability and security. Respondents also described limited support for identifying and managing the debt, often relying on manual identification and ad-hoc refactoring. This is evidence about practitioners’ reported experience, not a measure of prevalence across all companies.
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Software Improvement Group (SIG) also reports benchmark findings that help explain why speed alone is a risky proxy for value. Its 2025 report page says the underlying benchmark research covered more than 18,000 systems. The figures below are SIG’s published headlines; they should not be treated as universal enterprise rates or as proof that AI caused the problems. SIG advises consulting the full report for definitions and methodology before making comparisons.
| SIG 2025 published headline | What it describes |
|---|---|
| 60% of systems | Had a low degree of security controls, according to SIG’s benchmark research. |
| €7 million | Increase in maintenance costs in the largest systems due to poor software quality, as reported by SIG. |
| 40% slower updates | Update pace associated with poor software architecture in SIG’s report. |
| 73% of AI and big-data systems | Had quality issues in SIG’s benchmark findings; this is not a rate for all AI projects or a causal finding about AI-generated code. |
SIG’s State of Software 2025 report page presents these figures as benchmark headlines. Poor quality and architecture can make future changes slower or more expensive; the figures do not show that AI alone produced those conditions.
Why existing engineering practices matter
The same AI capability can accelerate useful work in one organization and amplify weak controls in another. SIG’s 2026 account says AI-assisted coding and agents may speed delivery where software quality and architecture are measured and managed, while also accelerating debt, costs, and security exposure where they are not. DORA’s 2025 research on AI-assisted software development similarly frames AI as an amplifier: returns depend on the organizational system around the tools, not only on the tools themselves.
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This is a practical warning, not a universal causal law. A company with clear ownership, review practices, and maintainable systems is better positioned to see whether faster production translates into useful outcomes. Where those foundations are weak, greater output can increase the volume of work that must later be reviewed, secured, or repaired. Before expanding use, leaders should know what is being generated, who is accountable for it, and how it will be maintained.
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What happens to workers when AI is used to reduce labor costs?
Worker outcomes are not captured by labor-hour savings alone. In its 2024 survey evidence, the OECD reports that four in five workers surveyed said AI improved their performance at work, and three in five said it increased their enjoyment of work. Those are worker responses, not audited company ROI figures. The OECD also identifies concerns including work intensity, how worker data is collected and used, and inequality.
The OECD estimates that occupations at highest risk of automation account for about 27% of employment in OECD countries. “At risk” describes exposure to automation, not a forecast that 27% of jobs will disappear. AI may change tasks, demand for expertise, or the balance between complementary and substitutable work. The National Academies’ 2025 consensus study, Artificial Intelligence and the Future of Work, examines those potential changes in more depth.
For an employer, the relevant question is not only how many hours a system appears to save. It is also whether workloads intensify, roles or tasks change, workers have a meaningful way to raise problems, and the organization is using data about them responsibly. These measures help distinguish productivity gains that improve work from savings that shift costs or risks onto employees.
Why infrastructure belongs in the cost calculation
Generative AI requires computing resources, and data centers also use electricity and water for cooling. The U.S. Government Accountability Office (GAO), in its 2025 assessment of generative AI’s environmental and human effects, reports an International Energy Agency estimate that U.S. data centers used about 4% of U.S. electricity demand in 2022, with a potential rise to 6% in 2026. Those figures cover data centers generally—not AI-only consumption—and the 2026 figure is a potential estimate, not a reported final measurement. They provide context for infrastructure demand, not a way to calculate the energy cost of a particular company’s AI deployment. See the GAO assessment for its scope.
At the project level, leaders can ask providers or internal infrastructure teams what compute is required, how usage changes with adoption, and what cooling or capacity constraints apply. The right accounting depends on the system and deployment; no universal infrastructure figure or AI ROI formula is established by these sources.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to measure AI’s real return
Set the intended outcome before deployment, record a baseline, and revisit the full workflow after use begins. This is a practical measurement approach derived from the documented risk areas, not a validated universal checklist.
- Define the outcome. Specify the task or business result the AI is meant to improve—such as turnaround time, error rate, service quality, or cost per completed workflow. Separate the target from the tool’s activity metrics, such as prompts or generated lines of code.
- Record a baseline. Measure the workflow before rollout, including time, quality, staffing or review effort, operating costs, and any relevant security or service indicators. Note the scope and conditions so later comparisons are meaningful.
- Count total costs over time. Include deployment and integration, training, human review, ongoing operation, security controls, maintenance, and remediation when they apply. Track infrastructure use with the level of detail available rather than treating all data-center demand as AI consumption.
- Monitor quality and risk alongside speed. For software, check maintainability, architecture, security controls, and the effort needed to review or remediate changes. For workforce applications, track experience and changes to tasks or roles. For infrastructure-dependent systems, track compute and cooling demands.
- Compare outcomes and adjust. Review whether the intended result persists as usage grows. If faster output is accompanied by more defects, review effort, worker strain, or operating costs, identify the source before expanding or changing the deployment.
Public-sector adoption illustrates why governance and resources matter as deployment spreads. In a review of 11 selected federal agencies, GAO found inventoried AI use cases rose from 571 in 2023 to 1,110 in 2024; generative AI cases rose from 32 to 282. Agency officials cited policy, technical-resource, and budget challenges. These counts describe GAO’s selected-agency inventory, not private-sector adoption or a direct estimate of savings. They are reported in GAO’s review of federal AI use and management.
What the evidence says—and what it does not
The evidence supports neither dismissing AI productivity gains nor assuming that early time savings equal lasting net savings. OECD respondents reported improvements in performance and enjoyment; SIG’s 2026 report quotation also recognizes productivity potential while cautioning that organizations need to measure and understand the foundations they build on. At the same time, software-quality benchmarks, practitioner reports, workforce concerns, and infrastructure demands identify costs and risks worth tracking.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsNone of these sources establishes a single ROI formula that works for every organization. Nor do they show that AI alone caused a particular company’s technical debt, security incident, job loss, or cost increase. The defensible approach is to evaluate each use case against its own baseline and lifecycle outcomes, keeping benefits, quality, workforce effects, security, and resource use visible in the same decision.
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