Many companies want AI in their product or internal tools but assume it means starting over. In practice, the most useful AI features are added next to the existing system, one use case at a time.
1. Start with one measurable task
Good first candidates share three traits: the task is repetitive, its input is text or documents, and its output can be checked. Examples:
- Extracting fields from invoices, orders or contracts
- Classifying and routing incoming emails or tickets
- Answering staff questions from internal documents
Define success upfront: “90% of invoices parsed correctly, saving two hours a day” is a target; “use AI for invoices” is not.
2. Test on your own data first
Collect 30–100 real examples with the correct answers. This small evaluation set tells you within days whether the approach works, which model performs best and what it costs per item. It also becomes the regression test for every later change.
3. Integrate it like any other service
The AI step should behave like a well-built backend component:
- Queues and retries for slow or failed calls
- Validation of every output against business rules
- Human review for low-confidence results
- Audit logs that show what the model received and returned
4. Avoid lock-in from day one
Models and prices change every few months. A thin provider-agnostic layer lets you switch between OpenAI, Anthropic, Google Gemini or others — and fall back to a second provider during outages — without touching the rest of the system.
5. Watch cost and quality after launch
Track accuracy on the evaluation set, latency and spend per request. Small changes — shorter prompts, caching, a cheaper model for simple cases — often cut costs significantly without losing quality.
The takeaway
AI integration is mostly good engineering: a clear target, real test data, reliable plumbing and monitoring. Start small, measure, and expand to the next use case once the first one has proven its value.