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AI is changing the software development lifecycle by helping teams create, inspect, and maintain engineering artifacts faster—from candidate requirements and designs to code, tests, release notes, and incident summaries. It does not make delivery automatically faster or safer: DORA’s 2025 findings describe AI as an amplifier of an organization’s existing strengths and weaknesses. Teams get value when they pair assistance with clear ownership, small changes, robust testing, security controls, and measured delivery outcomes.
How is AI changing the software development lifecycle?
AI is becoming a participant in work across the lifecycle, not a replacement for the lifecycle itself. It can draft or transform artifacts, search and summarize information, and suggest next steps. People still need to decide what problem to solve, establish constraints, review outputs, authorize changes, and take responsibility for what reaches users.
DORA’s 2025 research draws on survey responses from nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative research. Its central finding is a useful corrective to claims of automatic productivity: AI magnifies the way an organization already works. Teams with good engineering practices can use it to extend those practices; teams with unclear requirements, weak tests, or risky release habits can make more changes without making delivery more dependable.
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Where does AI help in software development?
AI can support each SDLC stage, but the suitable role changes with the artifact and the risk. Treat its output as a draft or recommendation until an accountable person has checked it against the relevant requirements and system constraints.
Requirements and discovery
AI can summarize customer feedback and other supplied material, turn prose into candidate requirements, flag ambiguity, and help maintain links between requirements and later work. These capabilities can make a large body of information easier to review, but they do not settle competing priorities or establish what a customer actually needs.
A product or engineering owner should decide scope, priority, acceptance criteria, and who is accountable for each decision. Review candidate requirements for assumptions hidden in a summary, missing edge cases, and wording that cannot be tested. Preserve the source context so reviewers can tell which statements are supported by customer material and which are suggestions that need confirmation.
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AI can propose alternative designs, draft interface concepts and diagrams, and help produce design documentation. These are useful starting points for discussion, not proof that a design fits the system. Reviewers need to check constraints that a draft may not capture, including failure modes, privacy implications, dependencies, and the cost of maintaining the design over time.
Use alternatives to make trade-offs visible rather than asking a model to choose an architecture without context. Engineers who understand the system should verify that diagrams and descriptions match the intended design and identify which assumptions still need validation.
Implementation
Coding assistants can generate boilerplate, explain unfamiliar code, propose refactors, draft tests, and offer code-review suggestions. The generated result remains an untrusted draft: a qualified engineer must verify behavior, dependencies, licensing, and security before accepting it.
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Review a proposed change in the same context as any other change. Check whether it meets the acceptance criteria, fits existing conventions, handles relevant failure cases, and introduces dependencies or behavior that the team did not intend. An explanation from an assistant can help a reviewer navigate a change, but it is not evidence that the code is correct.
Verification and validation
AI can suggest test cases, create fixtures, classify failures, and help maintain regression suites. Those tasks can support test work, but a generated test suite is useful only if it checks the behavior that matters. Review test intent and coverage, including whether the tests would fail when the behavior under test is broken.
DORA connects AI-related delivery risks to large changes and insufficient adherence to small batch sizes and robust testing. Its analysis estimates a 1.5% reduction in delivery throughput for every 25% increase in AI adoption and a 7.2% reduction in delivery stability for every 25% increase in adoption. These are estimates from DORA’s analysis, not universal constants; DORA links the negative effects to larger changes and inadequate small-batch and testing practices. They should be read as evidence that more AI use does not guarantee better delivery, not as a forecast for every team.
Integration and deployment
AI can assist with CI/CD configuration, release notes, operational runbooks, and incident summaries. It can help teams draft and interpret operational material, but release decisions still need safeguards. Keep continuous verification, traceability of changes, rollback capability, and policy gates in the deployment process.
Review generated configuration as carefully as application code: a concise draft can still alter a build, test, or release path in consequential ways. Before deployment, establish that the change passed the checks required for that system and that the team can identify and reverse it if it causes a problem.
Maintenance
AI can explain legacy code, search large repositories, suggest patches, and summarize telemetry or incidents. This can help engineers orient themselves in a codebase or investigate a problem, but a plausible summary may omit context. Confirm findings against the code and relevant operational evidence before changing production behavior.
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Faster patch generation can also increase the volume of changes a team considers. Use change controls and monitoring to keep that volume manageable, and make sure maintenance changes are reviewed and verified according to their risk rather than accepted because they are small or AI-generated.
Can AI-generated code be trusted?
Not by default. AI-generated code can be useful, but it should be treated as untrusted until a qualified engineer verifies its behavior, dependencies, licensing, and security and the change passes the team’s normal checks.
Trust is not a property that can be inferred from how polished a suggestion looks or how confidently it is explained. It is earned through evidence appropriate to the change: review against requirements, tests that exercise intended behavior, and any security or policy checks the project requires. For code that affects sensitive data, authorization, or critical system behavior, reviewers should make the relevant risks explicit rather than relying on a general code review.
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Apply the same discipline to generated tests and documentation. A test can encode an incorrect assumption; a release note can omit a material change; a runbook can describe a step that does not match the deployed system. Review the artifact for its purpose and verify it against the system it represents.
Does AI make developers more productive?
It can accelerate some tasks, but generated output is not the same as improved delivery. DORA’s 2025 analysis estimates that each 25% increase in AI adoption corresponds to a 1.5% reduction in delivery throughput and a 7.2% reduction in delivery stability under the conditions it identifies: larger changes and insufficient small batches and robust testing. The figures are estimates, not universal effects for every organization.
That apparent tension is important. An assistant may help an individual draft code more quickly while the team’s end-to-end delivery becomes less predictable if review, integration, testing, or release practices do not keep pace. Conversely, teams that can absorb and verify changes may get more value from the assistance. The right conclusion is conditional productivity, not that AI invariably helps or harms.
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Measure effects across outcomes that matter to the team: throughput, stability, quality, and developer experience. Compare changes over a defined period and take account of how work is delivered, not just how much code or how many suggestions were produced. If changes grow larger or checks are bypassed, a rise in apparent coding speed is not a reason to declare success.
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Use a software security framework across the whole workflow, including the AI and software supply chains. NIST’s Secure Software Development Framework (SSDF) organizes practices into four groups: Prepare the Organization; Protect the Software; Produce Well-Secured Software; and Respond to Vulnerabilities.
For AI-assisted work, apply those practices to the systems and processes involved, not only to the final code. Consider prompts and data, generated code, evaluation, deployment, monitoring, and vulnerability response. Define who can use AI systems for which work, what information may be supplied, which outputs require review, and how the team will respond when a vulnerability is found.
NIST SP 800-218A is an AI-specific community profile that augments SSDF 1.1 for generative AI and dual-use foundation-model development. Its stated audience includes producers of models, producers of AI systems, and acquirers. That makes it relevant to a broader supply chain than a team using an assistant in an IDE: organizations may need to consider both systems they build and the AI systems they acquire.
Governance should be practical enough to follow in daily engineering work. Assign owners to approval points, retain the information needed to trace how changes were made, and keep verification and release policies in force when AI tools are involved. Where a team cannot establish the origin or suitability of an artifact, it should not treat the artifact as trusted simply because it came through an approved tool.
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Compare tools and operating models against the work your team needs to do, the controls you require, and the effects you can measure. A feature list or an impressive code-generation demonstration does not show whether a tool fits your repositories, delivery process, or risk profile.
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| Evaluation area | Questions to ask |
|---|---|
| Lifecycle coverage | Which stages does it support: discovery, design, implementation, verification, delivery, or maintenance? |
| Repository and IDE integration | Can the workflow use the relevant code and context, and are the boundaries of that access clear? |
| CI/CD and deployment integration | Does it fit the team’s build, verification, and release process without weakening existing gates? |
| Testing and verification | Can the team assess the quality of generated tests and verify that they check intended behavior? |
| Security, privacy, and provenance | What controls cover sensitive data, generated artifacts, dependencies, and traceability? |
| Human approvals and governance | Where must a person review or authorize work, and can the team audit those decisions? |
| Model and data isolation | What model and data boundaries does the workflow provide, and do they meet organizational requirements? |
| Cost | What does the tool cost for the intended use, and how does that compare with the value and operational burden it creates? |
| Measured effects | How does use affect throughput, stability, quality, and developer experience in the team’s actual delivery system? |
Run evaluation against representative work rather than a showcase task alone. Include work that depends on repository context, verification, and the team’s actual approval process. Record what the tool helped with, what required correction, and whether the overall change was safely delivered. The comparison should include the cost of reviewing and integrating outputs, not merely the speed of producing them.
DORA names GitHub as an AI-powered developer platform and GitLab as an intelligent DevSecOps platform. Those categories illustrate that AI-assisted development can sit in different parts of the workflow; they are not proof that any one product suits a particular team. Evaluate each candidate independently against the same lifecycle, integration, governance, and outcome criteria.
What does a responsible AI-assisted SDLC look like?
A responsible approach keeps engineering ownership intact while placing AI assistance where it can be checked. IEEE P26044 organizes relevant capabilities across governance, project, technical, and organizational processes, spanning requirements engineering, architecture and design, implementation, verification and validation, integration, and maintenance. That lifecycle view helps teams avoid treating coding as the only place where AI changes work.
In practice, a team can use the following principles to make assistance observable and manageable:
- Keep a human owner for decisions. Assign people to scope, acceptance criteria, architecture choices, reviews, and release authorization.
- Make changes reviewable. Keep work small enough to inspect and test, and avoid allowing faster drafting to turn into oversized batches.
- Verify the behavior, not just the artifact. Use tests and other checks that establish whether the change does what the requirements call for.
- Apply security controls throughout the chain. Include data and prompts, generated artifacts, dependencies, deployment, monitoring, and vulnerability response in the security model.
- Track delivery outcomes. Evaluate throughput, stability, quality, and developer experience rather than treating code volume as the result.
- Preserve accountability and traceability. Make approval points and responsibility clear enough that the team can understand and investigate a change later.
The key shift is not that software can now be produced without engineering judgment. It is that teams can use AI to create and inspect more artifacts, making the quality of their surrounding process more consequential. A system that can absorb, verify, and govern those artifacts is better positioned to benefit than one that equates faster generation with successful delivery.
Frequently Asked Questions
What does SDLC stand for?
SDLC means software development lifecycle: the connected work involved in defining, designing, building, verifying, delivering, and maintaining software. The exact names and boundaries of stages differ by organization, but the lifecycle framing helps teams consider the whole delivery system rather than coding alone.
What is the difference between NIST SSDF and NIST SP 800-218A?
SSDF is NIST’s secure software development framework, grouped into four practice areas. SP 800-218A is an AI-specific community profile that augments SSDF 1.1 for generative AI and dual-use foundation-model development.
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Who should use NIST SP 800-218A?
NIST identifies producers of models, producers of AI systems, and acquirers as intended audiences. That scope is useful when an organization needs to consider responsibilities on both sides of an AI-enabled software supply chain.
What is IEEE P26044 about?
IEEE P26044 organizes AI-related software lifecycle capabilities across governance, project, technical, and organizational processes. Its coverage includes requirements engineering, architecture and design, implementation, verification and validation, integration, and maintenance.
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