AI coding assistants have gone from autocomplete to something closer to a junior collaborator. They write functions, scaffold services, draft tests, explain unfamiliar code and, increasingly, carry out multi-step changes across a repository.
Most engineering teams now use them in some form. The interesting question is no longer whether they help. It is what happens to the rest of the delivery lifecycle when writing code gets cheaper.
The bottleneck moves
When code is faster to produce, other stages feel the pressure.
- Review — more changes, often larger, arrive faster than reviewers can read them carefully
- Testing — generated code needs the same verification as hand-written code, sometimes more
- Design — it is easy to build the wrong thing quickly
- Integration — more parallel changes mean more merge conflicts and drift
Teams that only measure output speed will see throughput rise and quality quietly fall. Teams that rebalance effort toward review, tests and design get the real benefit.
Where assistants help most
From our own delivery work, the clearest gains come from:
- Boilerplate and scaffolding — new services, endpoints, data models, config
- Tests — first drafts of unit tests and edge cases, refined by an engineer
- Migrations and refactors — repetitive changes across many files
- Understanding legacy code — explaining what an unfamiliar module does before changing it
- Documentation — keeping READMEs, API docs and comments in step with code
The gains are smaller, and the risk higher, for core domain logic, security-sensitive code and performance-critical paths. Those still need careful human design.
Review has to get better, not faster
Generated code often looks plausible. That is precisely the problem. Reviewers skim code that reads well and miss the subtle error.
Practical changes that help:
- Keep pull requests small, even if the assistant could produce a large one
- Require the author to explain the change, not just submit it
- Use automated checks — linting, type checking, static analysis, dependency scanning — before human review
- Make reviewers responsible for behaviour, not style
The engineer who submits the change owns it, whoever or whatever wrote the first draft.
Tests become the specification
When assistants write more of the implementation, tests carry more of the intent. A clear, well-maintained test suite tells both people and tools what correct looks like.
This shifts effort toward:
- Writing acceptance criteria before implementation
- Treating test review as seriously as code review
- Watching for tests that simply mirror the implementation instead of checking behaviour
An assistant can write a test that passes against a bug. Only someone who understands the requirement can spot that.
Security and IP
Assistants introduce questions your security and legal teams will ask:
- Where does code and context go when the assistant processes it?
- Is any of it retained or used for training?
- Can generated code include licensed snippets?
- Are secrets kept out of prompts and context?
Settle these with a clear policy and approved tool configurations. Shadow use of unapproved tools is the more serious risk.
Skills and team shape
Assistants change what good engineers spend time on. Reading code, decomposing problems, writing clear specifications and judging trade-offs matter more. Typing speed matters less.
Junior engineers still need to learn fundamentals. Pairing them with assistants without guidance can produce people who ship code they cannot debug. Mentoring and review are how teams avoid that.
Measuring the impact
Avoid vanity metrics such as lines of code or suggestions accepted. Better signals include:
- Lead time from ticket to production
- Change failure rate and time to restore
- Review turnaround and rework
- Defects found after release
If lead time drops and failure rate holds steady, the assistant is helping. If both rise, review and testing need attention.
Where to start
- Agree an approved tool list and data-handling policy with security
- Pick a few well-suited tasks, such as tests and migrations, and start there
- Tighten pull request size and automated checks before usage grows
- Track delivery metrics before and after, not just adoption
- Invest in review skills as deliberately as coding skills
Faster code is only useful if it is also correct code. The lifecycle around the assistant decides which one you get.