Last year, I got a call from a long-time client who was in a total panic. They’d spent six months and a healthy chunk of their budget “AI-powering” their customer onboarding flow. Management was thrilled with the demos, but the reality was a nightmare. The AI was hallucinating shipping dates and suggesting illegal product combinations. It was a technical and brand disaster.
They brought me in to “fix the prompt.” My first thought was the same—just tighten the system instructions, maybe add some few-shot examples, and we’re good. Total mistake. I spent three days fighting with the API before realizing the problem wasn’t the prompt. It was the context. The AI didn’t have a clear journey map to follow, and it was pulling from a messy, unvetted data source. It lacked a cohesive AI UX Strategy.
Why Management Often Gets AI Wrong
Your leadership team isn’t trying to break the product. They’re just reading the same hype cycles everyone else is. They see efficiency, cost-cutting, and “innovation” as checkboxes. But they rarely understand the gap between a flashy demo and a production-ready tool that actually solves a user problem. That’s where you come in. Period.
If you’re a UX professional waiting for a “directive” on how to use AI, you’ve already lost. Someone else—likely someone who doesn’t know a persona from a pizza—will decide how this technology impacts your workflow. You have to lead this conversation because you’re the one who understands user intent and the high-judgment calls that AI simply can’t automate yet. I’ve seen this play out in dozens of projects; the best implementations are always led by the people who care about the “why,” not just the “how.”
Building Guardrails with Code
As a developer, I tell my UX colleagues that their strategy needs technical teeth. You can’t just have “principles”; you need guardrails. For example, in a WordPress or WooCommerce environment, you can hook into the AI’s response stream to validate it against your own business logic before the user ever sees it.
/**
* A simple guardrail to prevent AI from
* hallucinating specific forbidden keywords.
*/
function bbioon_validate_ai_response( $response_text ) {
$forbidden_terms = array( 'unlimited', 'free shipping forever', 'illegal' );
foreach ( $forbidden_terms as $term ) {
if ( stripos( $response_text, $term ) !== false ) {
return 'I am sorry, I cannot provide that information. Please contact support.';
}
}
return $response_text;
}
add_filter( 'bbioon_ai_assistant_output', 'bbioon_validate_ai_response' );This builds on a core concept I saw over at Smashing Magazine about how UX leaders can steer the ship. It’s not about fighting the tech; it’s about framing it. Instead of asking for “more research,” tell management that research is the only way to “de-risk the AI investment.” That language hits differently in a boardroom.
Step Up or Get Stepped On
Trust me on this: your value isn’t disappearing. It’s shifting toward being the person who makes the judgment calls. AI is great at generating 50 layout variations, but it’s terrible at knowing which one won’t frustrate a frustrated user at 2:00 AM. You are the dot-connector. The one who translates business goals into something that doesn’t feel like a robot wrote it.
Look, this stuff gets complicated fast. If you’re tired of debugging someone else’s mess and just want your site to work, drop my team a line. We’ve probably seen it before.
Are you ready to stop waiting and start defining how your organization uses these tools?
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