We’ve covered specific aspects of AI in web development - assisted code generation, security and performance implications of generated code, AI-powered personalization. This is the broader, honest synthesis: where AI actually earns its place in how a modern website gets built and how it functions, versus where it’s still mostly a talking point.
In the build process itself
AI-assisted development genuinely accelerates the mechanical parts of building - scaffolding, boilerplate, exploring unfamiliar code - while requiring the same or greater review rigor for anything security or performance sensitive, which we’ve covered in specific technical depth elsewhere. The honest framing: engineering judgment and review discipline matter more with AI-assisted development, not less, even as raw implementation speed genuinely improves.
In the product itself
Where AI genuinely earns a place in the finished product depends entirely on whether it’s solving a real, specific problem - answering questions from your own current data (retrieval-augmented generation), taking genuine multi-step actions on a user’s behalf (agentic features, built with the permission and audit discipline we’ve written about), or providing genuinely useful personalization backed by real behavioral data at real scale. Where AI features get added because they’re expected rather than because they solve something real, they tend to feel bolted-on and provide little genuine value - we’ve pushed clients away from exactly this pattern more than once.
In how a website is discovered and ranks
AI Overviews and changing search behavior are genuinely reshaping SEO, which we’ve covered in depth elsewhere - the shift toward structured, citable, genuinely authoritative content matters more now, not less, and that’s a real, current effect of AI on how websites need to be built and written, distinct from AI as a development or product feature.
Where the honest limits actually are, today
Full autonomous website generation from a prompt, replacing genuine engineering and design work, remains far short of producing anything a real business should ship - the gap between “AI generated something that looks plausible” and “AI generated something that’s actually secure, performant, accessible, and correctly represents a specific business’s actual needs” is still substantial, and closing that gap is exactly the review and engineering discipline we keep emphasizing across every AI-related topic we’ve covered. Treating AI as a tool that accelerates skilled work, rather than a replacement for the judgment that skilled work requires, is the honest current state.
What we actually recommend as the filter for any AI adoption decision
Does this solve a real, specific problem you actually have? Is there genuine review and quality discipline around it, regardless of how fast or fluent the AI-assisted output looks? Is the underlying data or context it’s grounded in actually good? These three questions, applied consistently, separate the AI adoption decisions that pay off from the ones that become expensive, hollow features nobody actually needed.
We apply this filter across everything we build, whether or not AI is directly involved, as part of our development work. Talk to us about where AI genuinely fits into what you’re building, and where it honestly doesn’t yet.