The LLM hype is pushing many teams to slap AI on everything. Most of these integrations wear out within six months. Three questions to ask first.
Is the task ambiguous?
AI shines on ill-formed problems: summarisation, fuzzy classification, generation. If the task is strictly deterministic, a plain function will be faster, cheaper, and more reliable.
Is the cost of error acceptable?
LLMs get things wrong — sometimes confidently. If a wrong answer has a high business impact (medical, legal, financial), you need a human in the loop, guardrails, or another tool entirely.
Is the ROI measurable?
Before any development, define the metric that will prove (or disprove) the value: time saved per case, adoption rate, user satisfaction. No metric, no AI project.
The best AI integrations we've shipped share one trait: we could have justified them without ever uttering the word "AI". The business need was obvious, the tool was just one of several options.