Adding AI to your product because competitors did it, because investors expect it, or because it's technically interesting are all bad reasons. Here are the situations where you should think twice.
When accuracy requirements are absolute
If your application requires 100% accuracy — financial calculations, medical dosing, legal compliance checks — LLMs are the wrong tool. They are probabilistic systems that can produce confident-sounding incorrect answers.
Use deterministic code for absolute requirements. Use AI for tasks where "good enough with human review" is acceptable.
When you don't have good data
RAG systems are only as good as the documents they retrieve from. If your knowledge base is incomplete, outdated, or poorly structured, AI will confidently retrieve and synthesize wrong information.
Fix your data before adding AI on top of it.
When a simple rule would work
If your workflow follows clear if/then logic — "if invoice amount > $10,000, route to manager" — a rules engine or simple script is more reliable, cheaper, and easier to maintain than an AI agent.
Don't use a LLM to do what a conditional statement can do.
When latency or cost doesn't work at your scale
LLM API calls add latency (often 1-5 seconds) and cost per request. At high volume, costs compound quickly. If your feature needs sub-100ms responses or serves millions of requests daily, evaluate whether the economics work.
When you can't evaluate quality
If you can't define what a "good" output looks like and measure it systematically, you can't improve an AI feature over time. You need evaluation frameworks before you need models.
When the user experience doesn't benefit
Some products add AI chat interfaces where a well-designed form or search would serve users better. AI should reduce friction, not add a conversational layer to simple tasks.
When regulatory or privacy constraints apply
Healthcare, finance, and legal applications often have strict requirements about data handling, model training, and audit trails. Ensure your AI implementation meets these requirements before building.
The decision framework
Ask these questions before adding AI:
- What specific user problem does this solve?
- Can a simpler solution (rules, search, forms) solve it?
- Do we have the data quality to support it?
- Can we define and measure output quality?
- Do the latency and cost economics work at our scale?
- What happens when the AI is wrong?
If you can't answer these clearly, you're not ready to ship AI to production.