Using AI responsibly

I’m deliberate about where I lean on AI and where I don’t. It’s most useful for the mechanical work: scaffolding components, capturing screenshots, writing tests, drafting pull request descriptions.

It raises the baseline on the hygiene things you should be doing by default, but it's not a substitute for judgement on what to build or how something should feel.

AI workflows I’ve found useful

Realistic placeholder content

AI tools can help mock up real data for UI work really easily. I can stress test a layout and make sure it’s working with the actual data that a customer will see.

Design tools have had plugins for this for years, but mocking it up directly inside the product saves time.

Sketching a mock-up, taking a screenshot, going straight into code

Depending on the complexity of the feature, I find myself sometimes sketching on paper or in Figma to explore the layout of an idea. Then I’m screenshotting or taking a photo and diving into Cursor or Claude.

If I’ve got a clear idea of how I want the feature to work and what it’s trying to achieve, I’m happy to let AI take a first crack.

AI gets me maybe 70% of the way there. Then I can refine the feature, either by further prompting or iterations in the browser.

Finding insights in user research transcripts, support tickets and sales call notes

Usually the more context and the clearer our instructions, the better the results from the AI. That’s why for some bets we’ve been taking, we’ve been feeding the AI with customer interviews, support tickets and sales conversations to pull patterns, group themes and surface representative quotes.

It can help us quickly stress test ideas against a whole knowledge base of insight and feedback and surface things we might’ve missed otherwise.

Marketing illustrations from a defined visual style

For Evidenced, we use a simple, slightly abstracted illustration style to show different features or concepts from our platform. We use these on the marketing website, presentations and sales decks.

I’ve been experimenting with codifying the design language into a repeatable “Skill” that an AI like Claude can take and consistently create on-brand assets for use in these different scenarios. With the goal being able to share the skill with others in the team so they can create assets themselves.

I’d say the output is about 80-90% there and the workflow at the moment still involves taking the SVG asset into Figma to refine the final steps to make it production ready.

Identifying accessibility compliance issues

I’ve been using AI to audit accessibility (contrast, alt text, ARIA, focus order) across components and flagging issues for manual review.

Automated research routines

For example, competitive monitoring like a scheduled scrape of competitor pricing/feature pages, then taking a diff against the previous period and pushing an update in Slack of what’s changed.

This isn’t an exhaustive list, it's just a few of the workflows I've found useful.

Useful resources

Keeping up to date with the latest AI developments can feel overwhelming at times, but these are the podcasts I've been listening to regularly to stay informed.