Moving fast is not the same as making progress. In 2026, the engineering question is whether teams can review, secure, maintain and change what they ship—not simply how quickly they produce code. Findings published by Software Improvement Group (SIG) and a KPMG survey point to the same management challenge, but they measure different things and do not prove that speed or AI inevitably harms every software organization.
1. AI amplifies the engineering system it enters
SIG’s 2026 report argues that AI speeds delivery when organizations measure and manage code and architectural quality, but can also accelerate technical debt, costs and security exposure when they do not. That is SIG’s interpretation of its own benchmark data, not a universal causal finding.
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The distinction matters: AI does not replace the engineering system around it. If a team has clear standards, capable reviewers and a way to track quality, faster code production can fit into that system. If those controls are weak, producing more code faster may increase the amount of work that is difficult to understand or govern. SIG’s 2026 report describes the benchmark and its findings.
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SIG reports that AI-generated code accounts for 1.9% of enterprise production code in its 2026 findings. In SIG’s testing, AI-generated code had roughly twice as many security risk violations as human-written code. These are SIG benchmark and testing results—not independently established rates for every company, tool or codebase.
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The practical implication is not that teams should reject AI-generated code. It is that faster generation does not remove the need to inspect what enters production. Reviewers need enough time and context to assess security as well as whether code meets the team’s requirements. A rising volume of generated code without matching review capacity can turn apparent speed into a governance bottleneck.
3. Speed and cost trade-offs can constrain later work
In KPMG’s 2026 survey, 69% of surveyed technology executives said their programs make trade-offs in security, scalability or data standardization while trying to move fast and keep costs down. Separately, 63% said the cost of repairing technical debt holds back new initiatives.
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These figures describe executives’ survey responses, not direct measurements proving that a particular trade-off caused a later delay. They nevertheless illustrate a tension teams must manage: a decision that reduces cost or time now can leave less room to build, secure or change systems later. KPMG’s technology survey presents the findings.
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In SIG’s benchmark, 86% of code fell below its recommended maintainability rating, while 50% fell below its recommended architecture rating. SIG also attributes a 30% reduction in issue-resolution time to stronger architecture. These are SIG’s ratings and benchmark findings; they should not be treated as universal estimates for all software organizations.
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Maintainability affects how readily engineers can understand and safely modify code. Architecture affects how the parts of a system fit together and how changes can be made without creating avoidable problems elsewhere. Neither is separate from delivery: when systems are harder to understand or change, teams may spend more effort resolving issues and less on new work. SIG says its benchmark spans more than 30,000 systems and over 400 billion lines of code, with findings based on systems analyzed over the past year.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Measure engineering quality beyond output volume
Code volume and delivery speed can describe activity, but they do not show whether the resulting software is secure, maintainable or ready to change. SIG’s report considers security, architecture, maintainability and technical debt alongside AI adoption. KPMG’s survey highlights reported trade-offs involving security, scalability and data standardization. Together, these dimensions offer a more useful way to discuss engineering capacity than output alone.
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The sources do not define a single universal scorecard, and their figures should not be combined: SIG reports benchmark findings and ratings, while KPMG reports survey responses from technology executives. A team can use the dimensions as prompts for its own measures, chosen to fit its systems and risks:
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- Security: Are security risks considered as part of delivery rather than deferred in the name of speed?
- Maintainability: Can engineers understand and modify the code without disproportionate effort?
- Architecture: Does the system’s structure support changes and issue resolution?
- Technical debt: Is the cost of remediation visible when teams plan new initiatives?
As SIG CEO Luc Brandts put it: “You cannot manage what you cannot measure, and you cannot move fast for long on a foundation you do not understand.” That is a vendor executive’s viewpoint, but it captures the central management issue: speed is useful only when teams can sustain the quality and changeability of what they deliver.
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