Faster output can move the bottleneck
AI-assisted development can reduce the time needed to explore an unfamiliar codebase, draft tests or produce a first implementation. It can also create more code than a team can safely review. The business result depends less on how quickly code is generated and more on the system that decides whether that code is correct, maintainable and ready to operate.
When implementation becomes faster, review, testing, security and deployment may become the constraint. Pull requests grow. Similar defects appear in several places. Engineers accept plausible suggestions without understanding the surrounding design. Delivery appears faster until rework, incidents or maintenance costs arrive.
Google's 2025 DORA research describes AI adoption as a systems problem. Its central finding is not that AI is automatically good or bad. AI tends to amplify the conditions already present in a team and its delivery environment. Strong feedback loops can turn speed into useful throughput. Weak systems can turn it into instability.
Define approved uses
Start by distinguishing tasks where assistance is welcome from tasks requiring additional review or exclusion. Low-risk uses may include explaining existing code, drafting routine tests or proposing documentation. Higher-risk uses include authentication, authorisation, financial calculations, data migrations, cryptography and changes to safety-critical workflows.
Teams should know which data and code may be sent to which providers. Client code, credentials, personal information and commercially sensitive material need explicit handling rules. Procurement settings and enterprise controls matter, but so do day-to-day developer habits.
Keep engineering accountability human
The person who submits a change remains responsible for it. That means being able to explain the design, failure modes and test coverage. Reviewers should assess the change as software, not as an AI output.
Useful controls include:
- small, understandable changes;
- automated tests at the right level;
- static analysis and dependency scanning;
- protected branches and independent review;
- preview environments for material changes;
- production telemetry and rollback paths; and
- clear ownership after deployment.
AI-generated tests also require review. A test can pass while asserting the wrong behaviour, reproducing the implementation's mistake or ignoring an important boundary.
Measure outcomes, not usage
Licences activated and prompts submitted are not delivery outcomes. Track measures that reveal whether the system is improving: lead time, change failure rate, time to restore service, escaped defects, review time and developer experience. Look at trends by type of work instead of expecting one organisation-wide answer.
Run a bounded pilot with a baseline. Select teams with stable delivery data, agree on permitted use cases and compare outcomes over several delivery cycles. Include qualitative evidence from maintainers and reviewers. A short-term rise in output is not useful if cognitive load and operational risk increase.
Protect the architecture
AI tools are good at producing locally plausible code. They do not automatically preserve system boundaries, data ownership or a long-term migration strategy. Architecture decisions should remain explicit. Keep short decision records for consequential choices and enforce boundaries through repository structure, tests and interfaces.
The goal is not to minimise AI use. It is to make assistance part of a dependable engineering system.
If your teams are adopting AI development tools, Algoza can help define a measured pilot, delivery controls and an architecture-aware operating model.