How to organize AI work on a web project
A recommended method for discovery, design, implementation and review, using hypothetical examples and deliveries that can be checked.
AI can contribute at several stages of a web project, but each stage needs a question and a way to verify the answer. Generating a screen, editing copy and repairing an integration are different tasks. Treating everything as building the website makes it harder to judge what is actually ready.
The following is a proposed working method with hypothetical examples. It does not report customer results or internal Inkdesign measurements. Its purpose is to help clients and developers organize deliveries that can be discussed and checked throughout a project rather than only at its end.
Discovery: turn information into explicit decisions
Collect the business problem, intended audience and action expected on the page. AI can help organize the material and identify contradictions. The product owner must confirm priorities and undocumented information. A polished summary does not make an uncertain assumption true or remove the need for a decision.
For a hypothetical quotation page, the first question might be which information the business needs to respond. Asking for twenty fields before understanding the service could produce a complete yet unnecessary form. Decide what must be known before designing how to collect it.
Design: examine composition in its use context
A visual proposal should demonstrate hierarchy, content and behavior at relevant screen sizes. Images can explain an offer or establish atmosphere, but they need a purpose. A conceptual mockup should be identified as a demonstration rather than appearing to prove a client project that never happened.
Request alternatives with clear differences and compare them with the page’s objective. One direction might favor reading, another product demonstration. Do not decide only by the image that looks most impressive in isolation. Check contrast, text size, keyboard navigation and states omitted from the initial composition.
Implementation: delegate bounded behavior
Divide work into behaviors such as submitting a form, filtering a list or recovering a password. State the incoming data, expected outcome and response to failure. Discussions of agent structures and tool interfaces offer conceptual references for organizing this work.
In the quotation example, the interface must validate data, send the request and show confirmation consistent with the server response. Displaying success before the operation finishes creates a false promise. The reviewer should be able to follow the whole path rather than only inspect a finished component or accept the agent’s description.
Review: record what was observed
Separate content, presentation and functional review. Copy must explain the offer without invented statistics or testimonials. Presentation needs desktop and mobile checks. The workflow needs valid inputs and relevant error cases. These reviews complement each other; passing one does not automatically clear the others.
At handover, identify changed files, completed checks and unresolved dependencies. If an integration has not been exercised against the real service, make that condition visible. Distinguish code prepared locally from behavior observed after publication. The article on prototypes and products develops that distinction. The AI adoption checklist helps turn the method into a repeatable team practice with owners and stable criteria, without presenting hypothetical benefits as established results.
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