Why Slapping AI on Your Site Doesn't Work

You heard AI is the future. You added a chatbot to your landing page. No one used it. You added AI-powered recommendations. 0.2% clicked them. You added AI search. Crickets.

You're confused. AI is supposed to help.

Here's the honest truth: You added AI because it's trendy, not because your users needed it.

Why Random AI Features Fail

You didn't ask users what they needed. You assumed people want a chatbot. They don't; they want answers without extra steps.

You added AI features that assume users want more steps. Chatbots make conversations longer. Recommendations are noise if people came for something specific.

You didn't measure anything. Features should move a metric. If it doesn't move engagement, conversion, support load—it's dead weight.

When AI Actually Works

AI Search: Works if you have 1,000+ products. Adoption: 20–30% of traffic. Conversion lift: 15–25%

AI for Support: Works if you get 100+ requests/day. Labor saved: 4 hours/day = $50K/year

AI Personalization: Works if you track user behavior. Engagement increase: 25–40%

AI Content Generation: Works if you're bottlenecked on content creation. Labor saved: 2 hours/feature

The Right Process for Adding AI

Step 1: Find a real problem (interview 5–10 users) Step 2: Identify where AI helps (research, confirm it's the right tool) Step 3: Build minimally (smallest version, test with 100 users) Step 4: Measure (does 25%+ try it? Does it improve the metric you care about?) Step 5: Expand or kill (if yes to all three, expand; if no, kill it)

The Real Cost

A "simple" AI feature: $3K–$5K dev + $200–$500/month infrastructure

If adoption is 2% and it doesn't move any metric, you've lost $5K upfront.

If adoption is 20% and improves conversion by 5%, the $5K pays for itself in 2 weeks.

The Honest Truth

AI is powerful. But "powerful" doesn't mean "good." A feature is good if users want it and it solves a problem.

Your "failed" AI features probably weren't good features. They were solutions in search of problems. That's backwards.

If you want to add AI that actually works, start with a real user problem—not an idea. Let's talk about finding the right one.