We’ve been using AI in our business for two years now. Made every mistake possible. Here are the five that hurt most.

Mistake 1: Trusting AI Output Without Verification

What happened: We used GPT to generate product descriptions. Published 200 of them without checking. Some contained made-up features. One claimed our product had won an award it hadn’t.

The cost: A customer complained publicly. We had to review and fix all 200 descriptions. Probably 40 hours of work.

The lesson: AI hallucinates. Always verify facts, especially anything specific like numbers, awards, or technical specs.

Our rule now: Every AI-generated customer-facing content gets human review. No exceptions.

Mistake 2: Over-engineering the Solution

What happened: We wanted AI to handle customer support. Built an elaborate system with custom fine-tuning, multiple models, and sophisticated routing logic.

The cost: Three months of development. $25,000 in contractor fees. System was too fragile to maintain.

The lesson: Start stupid simple. Our replacement was just Claude with a good system prompt and our FAQ docs. Works better than the complex system ever did. Anthropic has good documentation on building with their API.

Now we build the dumbest possible version first. Only add complexity when we hit specific limitations.

Mistake 3: Ignoring the Humans in the Loop

What happened: We automated our internal reporting entirely with AI. Nobody liked the reports. Engagement dropped. Important insights got missed.

The cost: Six months of worse decision-making before we realized the problem.

The lesson: AI should augment, not replace, human judgment on important decisions. Our reports now are AI-drafted but human-edited. People actually read them.

The best AI implementations feel like having a very fast assistant, not a replacement.

Mistake 4: Not Budgeting for API Costs

What happened: Built a feature that called GPT-4 on every user action. Didn’t rate limit. Didn’t cache responses. Launched it.

The cost: $4,000 in API fees in one weekend. We’d budgeted $500/month.

The lesson: AI costs scale non-linearly. A feature that costs $10 to test might cost $10,000 in production.

Now we obsess over API efficiency. Cache everything. Use cheaper models where possible. Rate limit aggressively. Monitor costs daily during launches.

Mistake 5: Promising Customers Things AI Can’t Deliver

What happened: Our sales team saw competitors claiming AI features. Started promising AI capabilities we didn’t have. “Our AI will learn your preferences and predict what you need.”

The cost: Angry customers expecting magic. A few refund requests. Damaged trust.

The lesson: AI is good at specific, bounded tasks. It’s terrible at vague, ambitious goals. Underpromise what AI can do.

Now our sales team has a list of exactly what our AI does and doesn’t do. No improvisation on AI capabilities allowed.

The Pattern

Looking back, all these mistakes share something: we treated AI as magic instead of as a tool.

AI doesn’t know if it’s right. It doesn’t know your budget. It doesn’t understand your customers’ expectations.

You have to provide all that context and oversight. When we forgot that, things went wrong.

What We Do Differently Now

1. Verify everything. Especially facts, numbers, and claims.
2. Start simple. Complexity is earned, not assumed.
3. Keep humans involved. For editing, decision-making, and oversight.
4. Budget 3x what you expect. For time, API costs, and maintenance.
5. Underpromise. Let AI capabilities speak for themselves.

AI is genuinely useful. But it requires more babysitting than the marketing suggests. Plan for that.