Key takeaways
- AI tools speed up some tasks a lot and others barely at all; overall gains depend heavily on the work.
- Discovery, design decisions, review, testing and accountability still take human time.
- Ask your team how AI-written code is reviewed, where your code and data are sent, and who owns the result.
- Expect faster prototypes and boilerplate, not a project that costs a fraction of the old price.
If you are hiring a software team in 2026, they almost certainly use AI tools. Code assistants inside the editor, chat models that explain unfamiliar code, and agents that can make changes across several files are now part of normal development work.
Clients reasonably ask what this means for them. Should projects be faster? Cheaper? Is the code worse? Who owns it? This article gives a straight answer from the team side of the table.
What AI tools are genuinely good at
Used well, AI assistants remove a lot of friction from work that is necessary but not novel:
- Scaffolding: setting up a new screen, endpoint or component that follows existing patterns.
- Boilerplate: form validation, data mapping, repetitive configuration.
- Tests: drafting unit tests for existing functions, which a developer then checks and extends.
- Migrations: mechanical changes across many files, such as renaming a field or upgrading an API call.
- Explaining unfamiliar code, error messages and third-party libraries.
- Throwaway prototypes that help everyone see an idea before committing to it.
What does not get faster
The parts of a project that decide whether it succeeds are mostly unchanged:
- Understanding the business problem and agreeing what to build.
- Architecture decisions: data models, integrations, security boundaries, hosting.
- Reviewing code properly, including code an AI wrote.
- Testing real user flows, edge cases and failure modes.
- Deployment, monitoring and support after launch.
- Taking responsibility when something goes wrong.
What the research says about speed
Evidence on productivity is genuinely mixed, and it is worth knowing both sides. A 2022 controlled experiment run by GitHub found that developers using Copilot completed a specific, well-defined task (writing an HTTP server) about 55% faster than those without it. In contrast, a randomised study published by the research group METR in 2025 found that experienced open-source developers working on large codebases they knew well took about 19% longer with AI tools, even though they believed the tools had made them faster.
The honest summary is that AI helps most on small, well-specified, self-contained tasks and least on complex work in large, established systems where context matters. Most real projects contain both.
How this should affect your quote
For a typical web platform or app, AI assistance mainly compresses the build phase for standard features: forms, CRUD screens, admin views and integrations with well-documented APIs. Discovery, design, review, QA and launch take roughly the same effort as before. In some areas, such as code review and security checks, responsible teams now spend more time, not less, because more code is produced more quickly.
So expect faster first prototypes and somewhat shorter build phases on routine work. Be sceptical of anyone promising a full product for a tiny fraction of normal cost because “AI writes the code”. The expensive part of software has always been deciding what to build and making it reliable.
Quality and security
AI-generated code can look clean and still be wrong. Common issues include outdated library usage, missing error handling, subtle logic errors and security gaps such as insufficient input validation or permission checks. None of these are unique to AI, but they can slip through when code is produced faster than it is reviewed.
The safeguards are the same ones good teams already used, applied more strictly: every change reviewed by a person who understands it, automated tests on critical paths, static analysis and dependency scanning in the build pipeline, and staging environments where you can click through changes before release.
Your code and your data
When developers use AI tools, parts of your code, and sometimes your data, are sent to a tool provider. Business and enterprise plans of the major tools generally offer terms where customer code is not used to train models, plus controls for data retention. Free consumer tiers may have different terms. You are entitled to know which tools are used on your project and under which terms.
Who owns AI-assisted code?
Contractually, the answer should be the same as before: your agreement should state that you own the code and work product created for you. Copyright law adds a nuance. In the United States, the Copyright Office has stated that copyright protection requires human authorship, and material generated purely by AI without meaningful human creative input is not protected on its own. In practice, professionally built software involves substantial human design, selection, editing and integration. Make sure your contract covers ownership and assignment clearly, and ask for advice from a lawyer in your jurisdiction if this matters to your business model.
Questions to ask any team you hire
- Which AI tools do you use, and on which plan or licence terms?
- Is any of our code or data used to train third-party models?
- How is AI-written code reviewed before it is merged?
- What automated tests and security checks run on every change?
- Who on the team is accountable for each part of the system?
- Does the contract assign ownership of all code and assets to us?
- How has AI changed your estimates, and where?
| Phase | Typical effect of AI tools |
|---|---|
| Discovery and requirements | Little change; some help summarising research and notes |
| Design and prototyping | Faster clickable prototypes and throwaway experiments |
| Building standard features | Often noticeably faster |
| Complex domain logic | Modest or no gain; still needs careful thought |
| Code review and QA | Same or more effort |
| Launch and support | Some help with diagnosis and documentation |
At Prestigious Software we use AI tools where they help, review every line that goes into your product, and we are happy to show you exactly how. If a team cannot explain its process clearly, that tells you more than any promise about speed.
Sources and further reading
Written by the Prestigious Software team
We design, build and run web platforms, SaaS products and mobile apps. About us
Our team reviews each article. Claims based on standards or research link to primary sources; examples are illustrative unless identified as client work.
