Every agency claims to use AI now. Browse enough websites and you’ll see the same buzzwords: “AI-powered development,” “intelligent automation,” “next-generation workflows.” But what does AI-assisted development actually look like day to day? Not in a press release — in practice.

We decided to pull back the curtain. Here’s an honest account of how our team uses AI tools, where they genuinely help, and where we keep our hands firmly on the wheel.

TL;DR

Our team uses AI coding assistants primarily for accelerating routine tasks like boilerplate code, documentation, and refactoring — not for replacing critical developer judgment on architecture or design decisions. The biggest wins come from pairing AI tools with strong code review processes and experienced developers who know when to accept suggestions and when to rewrite them. AI has made us measurably faster at certain tasks, but the quality gains come from how we use the tools, not the tools themselves.

The challenge: hype versus reality

When AI coding assistants gained momentum in 2023, we faced the same question every agency faced: adopt now or wait? The Stack Overflow 2023 Developer Survey showed 70 percent of developers were using or planning to use AI tools. The hype promised tenfold productivity. Some agencies started marketing “AI-built websites” as if the human team was optional.

We saw it differently. As we discussed in our opening article on AI in web development, these tools are power tools — they amplify the skill of the person using them. Our challenge was practical: integrate AI into our workflow in a way that genuinely improves output without introducing new risks.

What we actually use

GitHub Copilot Business is our primary coding assistant. It lives inside our editors and provides real-time suggestions — particularly effective for autocompleting repetitive patterns, generating boilerplate, and suggesting implementations based on function names.

ChatGPT serves as a research and brainstorming tool for exploring unfamiliar problems and drafting documentation. We covered how these tools work in our guide to AI coding assistants.

We deliberately kept the toolkit small. Two well-integrated tools beat five that fragment your workflow.

How our workflow changed

Initial scaffolding is faster. Setting up components, creating API endpoints, or generating database models used to eat hours. Now these tasks take a fraction of the time. The developer still designs the architecture — AI handles the repetitive implementation.

Code review became more important, not less. When developers generate code faster, there’s more to review. AI-suggested code can look correct while hiding subtle issues. We invested more in code review specifically because of AI adoption. Our article on how AI speeds up development explains this dynamic.

Documentation improved dramatically. AI assistants generate solid first drafts of code comments and API documentation. Developers review and refine rather than starting from scratch — which means documentation actually gets written.

Refactoring happens more often. Cleaning up messy code used to get deprioritized. With AI assistance, refactoring takes minutes instead of an hour. Technical debt gets addressed sooner.

Where AI falls short

Architecture decisions require human judgment. AI can’t understand your business requirements or the trade-offs between technical approaches. These decisions require experience that AI doesn’t have.

Client communication is entirely human. Understanding what a client needs requires empathy and business knowledge. We outlined what those conversations look like in our post about the right questions to ask your web agency.

Security requires expert scrutiny. Research shows a significant percentage of AI-generated code contains vulnerabilities. This is why developer experience matters more than the tools.

Design thinking stays human. AI can generate CSS, but understanding how users navigate interfaces is human expertise. We explored this in our article on AI and web design.

The results: measured, not marketed

Routine coding tasks are 30 to 50 percent faster. This aligns with McKinsey’s finding of 35 to 45 percent productivity gains for experienced developers.

Code review time increased by about 15 percent. We treat this as an investment — it’s what maintains quality when velocity increases.

Net productivity gain: roughly 20 to 25 percent overall. Not the tenfold improvement vendors promise, but sustainable and real. GitHub’s research showed a 26 percent increase in completed tasks in real-world conditions, and our experience tracks closely.

These gains only materialized because we paired the tools with the right team. As we explored in our budgeting guide, the tools are the cheapest part of the investment.

Key takeaways

Less is more with tooling. Master two or three tools rather than adopting every new one.

Quality gates matter more, not less. Strong quality assurance isn’t optional — it’s the prerequisite for AI adoption.

The team determines the return. Identical tools produce vastly different results depending on who uses them.

Transparency builds trust. Clients respond well when you explain exactly where AI helps and where human expertise takes over.

Frequently asked questions

Does your team use AI to write all the code?

No. AI assists with specific tasks — autocompleting patterns, generating boilerplate, drafting documentation — but every line is reviewed by experienced developers. AI is a tool in the process, not the process itself.

How do you ensure AI-generated code is secure?

Through rigorous code review and testing, applied with extra scrutiny to AI suggestions. Our developers review all code for security vulnerabilities and edge cases before anything reaches production.

Will AI make my web project cheaper?

AI can reduce time on certain tasks, translating to faster delivery or more features within a budget. The savings come from pairing AI with experienced developers — not replacing them. We break down the numbers in our cost and budgeting guide.

How do I know if my agency is using AI responsibly?

Ask them. A responsible agency will tell you exactly which tools they use and how they maintain quality. If they can’t answer specifically, that’s a red flag. Our agency evaluation guide outlines the key questions.

The bottom line: tools serve the team

AI hasn’t transformed what we do — it’s transformed how efficiently we do certain parts of it. The websites we build are better not because AI writes the code, but because it frees our developers to focus on architecture, user experience, and the business logic that makes your project succeed.

The agencies getting it right invested in the right team and processes before they invested in the tools. Get in touch with our team to see how AI-assisted development could work for your next project.