A Practical Framework for AI Integration in Enterprise Software

Most enterprise AI pilots never reach production — not because the models don't work, but because the use case wasn't chosen with production constraints in mind from day one.

Choosing use cases that ship

The AI use cases that make it to production share three traits: a clear, measurable success metric; a human-in-the-loop fallback for low-confidence outputs; and tolerance for occasional errors in the workflow it touches. Fully-automated, zero-tolerance use cases (e.g. autonomous financial transactions) are far harder to ship responsibly than assisted workflows (e.g. drafting a support response for human review).

Architecture considerations

We typically architect AI features with a clear separation between the retrieval layer (your data, indexed in a vector database), the reasoning layer (the LLM call itself), and the application layer (how results are surfaced and acted on). This separation makes it possible to swap models as the field evolves without rewriting your product.

Measuring success

Before writing any AI feature, define the metric that will tell you it's working — time saved, error rate reduced, tickets deflected. Without this, teams tend to keep iterating on model prompts indefinitely without ever confirming business value.