There is a specific face that clients make when we ask about their existing AI features. A slight wince, a pause, and then something like: "Users tried it a few times and stopped." We have seen this face a lot in the last 18 months.
The gap between AI demos and AI products that survive contact with real users is enormous. Demos optimize for impressiveness. Products have to optimize for consistency, speed, cost, and being useful in the exact moment someone needs them — not in the ideal conditions of a sales call.
The latency problem nobody talks about in demos
A GPT-4 API call at a well-provisioned endpoint averages 2–6 seconds for a moderate response. That is fast enough for a demo where the presenter talks while it loads. It is not fast enough for a primary interface action where the user is waiting.
The AI features that retain users are almost universally async or streaming. Streaming responses (using the OpenAI streaming API) change the perceived experience dramatically — users read as the text arrives, which feels like a fast response even if total generation time is the same.
Production Pattern
For our clinic management clients, we implemented AI-generated appointment summaries as a background job that runs after booking confirmation. By the time the doctor opens the file, the summary is there. Zero perceived latency. High retention.
Determinism versus creativity — know what you need
Language models are stochastic by nature. The same prompt can produce different outputs on different runs. For creative applications — copywriting, ideation, brainstorming — this is a feature. For business applications — data extraction, classification, structured output — it is a bug.
Production AI integrations in business software almost always require deterministic or near-deterministic output. This means low temperature settings, structured output formats (JSON mode in OpenAI), validation layers that check outputs before rendering them, and fallback paths when the model produces unexpected results.
Cost control is a product decision
At scale, LLM API costs are not negligible. A feature that costs $0.003 per use sounds trivial until you have 10,000 daily active users triggering it multiple times per session. The teams that build sustainable AI products build cost awareness into product decisions from day one.
- →Cache aggressively — many AI requests in business apps are near-identical and can return cached results
- →Use smaller models for simple tasks — GPT-4o mini handles classification and extraction at 10x lower cost
- →Set hard token limits — most business use cases need less than 500 tokens of output
- →Monitor cost per feature, not just total API spend
The integrations that actually stick
Across all the AI features we have shipped that users actually kept using, a pattern emerges: they all remove a specific, concrete piece of friction from a workflow the user already does. Not "AI assistant." Not "smart search." Something specific — summarize this intake form, suggest the next follow-up date, flag this record as unusual based on these fields.
The more specific the job the AI does, the more reliably it does it, and the more users trust it. That trust is the product. The AI is the mechanism.