AI isn’t a sticker you slap on a landing page. It’s most valuable at the friction points: where people abandon flows, where teams copy-paste between tools, or where “just checking something” takes ten clicks. Get those moments right and the product feels sharper — without shouting “powered by AI.”
Marketing sites: answer before the form
On the public site, AI can surface the right proof, plan, or FAQ based on what someone has already seen — or draft a first reply when they’re still deciding whether to enquire. The goal isn’t to replace your team; it’s to qualify intent earlier and cut time-to-response once they do reach out.
Inside the app: plain language, real actions
In dashboards and internal tools, users shouldn’t have to memorise every filter. A well-scoped assistant turns questions like “unpaid invoices this month?” or “which campaign drove signups?” into queries, charts, or next steps — with your permissions and data model enforced server-side.
Treat models like any other service
Technically, I treat AI like an API: clear inputs, validated outputs, and fallbacks when confidence is low. Prompts live next to monitoring; human review stays in the loop for anything customer-facing or compliance-sensitive. That’s how you ship fast without gambling on black-box behaviour.
Start with one high-leverage workflow
You rarely need a rewrite. Pick one assistant, one automation, or one “remove this step” button, measure it, then expand. The agencies and founders I work with get the most ROI when AI is tied to a metric they already care about: speed to first reply, completion rate, or hours saved per week.
My process stays consistent: map the journey, identify where intelligence removes drag, then design UI and prompts around that outcome. The product should feel like it understands the job — not like it’s showing off a model name.
