Everyone's launching "AI solutions" right now. Chatbots with AI. Customer service powered by AI. AI-driven analytics.
But here's the thing: most of it isn't actually AI. It's smart programming. Machine learning at best. And you know what? That's completely fine — as long as you're honest about it.
Let's clear the air:
✅ The spectrum nobody talks about
There are levels to this:
- Rule-based logic – If customer asks X, show Y. No learning involved.
- Machine Learning – Pattern recognition, classification, clustering. Smart, but predictable.
- Generative AI – LLMs that actually create new responses based on context. This is the real deal.
Each has its place. None are "bad." But calling basic automation "AI-powered" just creates confusion — and often disappointment.
3 questions to ask yourself before building
-
What problem are you actually solving?
Be specific. "Better customer service" isn't a goal. -
Could simpler tech do this?
Sometimes a well-structured FAQ + smart search beats an expensive LLM. -
What's the ROI of complexity?
GPT-4 API calls add up fast. Is the extra "intelligence" worth it?
When you actually need AI (the real stuff)
- Understanding intent, not just keywords ("When can I get hot water?" → product question)
- Generating contextual, personalized responses
- Handling edge cases your rules never anticipated
- Learning from interactions over time
When you probably don't
- 80% of questions are the same 5 FAQs
- Responses need to be legally precise (structured data > hallucinations)
- Budget is tight and uptime matters more than wow-factor
- Your data is too messy to feed an LLM anyway
Why honesty wins
I've turned down "AI projects" because the client's problem was better solved with a spreadsheet and some automations. Cost them 1/10th of what they budgeted.
Guess what? They came back for actual AI work later — because I didn't oversell the first time.
Final take
Build what solves the problem. Call it what it is. If that's a smart if/else tree wrapped in a nice UI, own it. If it's genuine ML or LLMs doing semantic magic, show that off.
Your customers care about results, not buzzwords. And the developers you work with will respect you more for knowing the difference.