The Hype Cycle Is Real, and It's Expensive
We've reviewed architectures where teams added a vector database, an embedding pipeline, and a fine-tuned model to solve a problem that a well-indexed Postgres query would have handled in 10 milliseconds. The AI tax — in latency, cost, and operational complexity — was enormous.
The question isn't 'can we use AI here?' It's 'does AI give us something we can't get from a simpler system?' That's a much harder question to answer honestly when everyone around you is shipping AI features.
Where AI Genuinely Adds Leverage
Unstructured data processing is the clearest win. If your system needs to extract meaning from free-text inputs, classify documents, or generate coherent natural language output, AI is the right tool. There's no deterministic alternative.
Personalization at scale is another genuine use case. Collaborative filtering and embedding-based recommendation systems can surface relevance that rule-based systems simply can't match once your catalog grows past a few thousand items.
“Use AI where the problem is fundamentally probabilistic. Use deterministic systems everywhere else.”
The Integration Patterns That Work
Treat AI as a service boundary, not a library. Wrap your model calls behind an interface that can be swapped, mocked in tests, and rate-limited independently. This keeps your core business logic clean and makes model upgrades a deployment concern, not a refactoring concern.
Build fallback paths. AI systems fail in ways that deterministic systems don't — they hallucinate, they degrade under distribution shift, they time out. Every AI integration should have a graceful degradation path that keeps the user experience intact.
