AI Coding Assistants in the SDLC 
Where They Pay Off and Where They Cost You 

The interesting question about AI coding tools stopped being whether they work some time ago. They work — unevenly, in ways that depend heavily on the task and the codebase, and with second-order effects that show up two months after adoption rather than in the first week's enthusiasm. The useful question for an engineering leader is narrower and more answerable: on which categories of work does this compound, what does it shift downstream rather than eliminate, and what has to be true about your codebase and your process for the gains to be real? Teams that answer those deliberately get a genuine step change. Teams that adopt tools and hope get a higher volume of code and the same delivery rate, which is a worse position than they started in.

Where the gains are real

The pattern is consistent: assistants are strongest where the work is well-specified, verifiable, and tedious. Writing tests against existing behaviour, scaffolding a new endpoint or component that looks like twenty existing ones, mechanical migrations across many files, translating between languages or frameworks, generating the fixtures and factories nobody wants to write, and drafting documentation from code all fall squarely in that band. So does comprehension — asking questions of an unfamiliar codebase is one of the highest-value uses and one of the least discussed, because it compresses the part of onboarding that used to take a new senior engineer weeks of reading. Agentic tooling has extended this into multi-step work: given a failing test or a well-described defect, the tool can locate the cause, propose a change, and iterate against the test suite. Note what that requires — a test suite good enough and fast enough to act as a verifier. The teams getting the most out of these tools are, almost without exception, the teams who already had strong test coverage and fast pipelines. The tooling amplifies whatever engineering discipline is already there.

Where it costs you

• Confident wrongness in unfamiliar territory: plausible code against APIs that don't exist, or that quietly misuses your internal abstractions in ways that pass review because they look idiomatic.
• Review load shifting rather than disappearing. Generation gets faster; understanding does not. Unreviewed volume is the main new risk, and it lands on your senior engineers.
• Security defects at scale — insecure defaults, missing authorization checks, injection-prone query construction, hardcoded credentials — reproduced consistently across many files rather than appearing once.
• Dependency suggestions that are outdated, unmaintained, or occasionally hallucinated entirely, which is a genuine supply chain concern when a name gets registered by someone else.
• Erosion of the struggle that builds judgment in junior engineers, if they're given completion tools before they've built a model of the system.
• Codebase entropy: more code, more abstractions, more near-duplicate helpers, all arriving faster than a team's refactoring cadence can absorb.

The guardrails that matter

Most of what makes AI-assisted development safe is not AI-specific — it's the standard controls, held to properly now that the volume of code has gone up. Human review of every change, with the author accountable for understanding what they submitted, is the non-negotiable one; 'the assistant wrote it' is not a defence in a postmortem. Beyond that: automated security scanning and dependency policy in CI so vulnerable patterns and unknown packages are caught mechanically; secret scanning on every commit; and a rule that generated code touching authentication, authorization, payments, or personal data gets a second reviewer. Give the tools your context deliberately — a project instructions file capturing your conventions, architecture, and the things you don't do measurably improves output quality and is the cheapest lever available. And write down which tools are approved, because the alternative isn't no AI, it's engineers using personal accounts on unreviewed tools.

The governance question your customers will ask

Two things belong in a written policy before a security questionnaire asks for them. First, where your code goes: whether the tool trains on your input, how long it retains it, whether it's processed in an acceptable jurisdiction, and whether you're on an enterprise tier with the terms you think you have — consumer and business tiers of the same product often differ sharply here, which is the same trap covered in keeping customer data safe in AI features. Get a data processing agreement in place and confirm zero-retention or no-training terms in writing. Second, provenance: what your position is on generated code that resembles licensed source, which matters most for anything you distribute or license out. Many enterprise tiers now offer filtering and indemnification; know whether yours does. Neither of these is hard to answer, and both are much easier to answer before a buyer asks than during a deal.

Measure it honestly

Vendor metrics — suggestions accepted, lines generated — measure tool engagement, not value delivered, and optimising for them is straightforwardly harmful. Measure the outcomes you already care about: lead time from commit to production, deployment frequency, change failure rate, time to restore, plus review latency and rework rate. If assistants are working, throughput rises while change failure rate holds steady. If change failure rate rises alongside throughput, you've moved the bottleneck into review and production rather than removed it, which is a fixable problem but only if you're looking. Add a qualitative read as well — ask the team where the tools genuinely help and where they're fighting them, because the answers vary enormously by part of the codebase and are the fastest route to knowing where to invest. Run a real baseline for a few weeks before rollout; without one, every claim about impact afterwards is a vibe.

How Infiniti Tech Partners works with AI-assisted development

We use these tools daily on client code, under the same rules we'd recommend to you: approved tooling with enterprise data terms, every change reviewed and owned by a named engineer, security and dependency scanning in CI, and a hard line around anything touching auth, payments, or personal data. Where we help teams directly is in making adoption produce measurable delivery gains rather than measurable code volume — establishing the baseline first, getting the codebase into a state where the tools are actually effective (fast tests, clear conventions, documented context), writing the policy your security questionnaires will ask for, and putting the review and scanning guardrails in place before the volume arrives rather than after. It's a short engagement with a durable effect, and it pairs naturally with the delivery-metrics work we do around DORA.