When a business asks us to 'add AI', we start by looking for work that is repetitive, text-heavy and currently done by someone who would rather be doing something else. That is where language models pay for themselves.
Good first candidates
- Extracting structured data from documents, emails and forms
- Search that understands meaning across your own knowledge base
- Drafting replies, summaries and reports for a person to approve
- Classifying and routing incoming requests
Keep a person in the loop
Models are confident even when they are wrong. Design the feature so uncertain results are routed to a human, every automated decision can be traced, and it is easy to correct a mistake. Trust is earned gradually; widen the automation as the numbers justify it.
Measure before you scale
Build a small evaluation set from real examples before writing much code. It tells you whether the idea works, lets you compare models and prompts objectively, and catches regressions when anything changes. Track cost and latency alongside accuracy from day one.