AI features are easy to bolt onto a website and hard to justify once they're there. Before adding one, it's worth being specific about what problem it solves — because 'add AI' is not a feature spec.
The integrations that hold up in production tend to do one of three things well: reduce a genuinely repetitive task (support triage, content tagging, data entry), improve search and discovery inside a large catalog or knowledge base, or accelerate a workflow your team already does manually.
The integrations that don't hold up are usually decorative — a chatbot with no real backing knowledge, or generative content with no editorial review. They ship fast and get quietly disabled a few months later.
Our approach is to scope the use case first, prototype against real data, and only then decide whether an AI feature belongs in the product — the same discipline we'd apply to any other engineering decision.