Most teams do not need a brand-new AI product. They need the software they already run — a CRM, an ERP, an internal portal — to do a few tasks faster and with fewer manual steps. This guide walks through a practical way to get there without rebuilding everything.
Start with the workflow, not the model
The most common mistake is choosing a model first and looking for a problem second. Instead, map one real workflow end to end and mark every step where a person reads, types, copies or decides something repetitive.
- Reading: summarising long emails, tickets or documents
- Typing: drafting replies, descriptions or reports
- Copying: moving data between systems or from PDFs into forms
- Deciding: routing, tagging or prioritising incoming requests
Each of these is a candidate. Pick the one that happens most often and has the clearest definition of a “good” result.
Choose the right level of AI
Rules and classic automation
If the logic can be written as clear if-then rules, you probably do not need a language model at all. Rules are cheaper, faster and easier to audit.
Predictive models
When you have historical, labelled data — for example past tickets and the team that resolved them — a classic classification model can be accurate and inexpensive to run.
Large language models
Language models shine with unstructured text: summarising, extracting fields from free-form documents, or drafting content that a person then reviews.
Design for a human in the loop
Early AI features should assist, not replace. Show suggestions that a user can accept, edit or reject, and record which one they chose. That feedback becomes your quality signal and, later, training data.
A good first AI feature saves time on every use and never surprises the user.
Ship small, then measure
- Define one success metric before you build, such as time per task or edits per suggestion.
- Release to a small group of users first.
- Log inputs, outputs and user corrections (respecting privacy rules).
- Review the results together with the people who use the feature every day.
- Only then expand to more users or more workflows.
Plan for data, privacy and cost
Decide early which data may leave your systems, where it is processed, and how long it is stored. Estimate cost per request and set usage limits so a popular feature does not become an unexpected bill.
Frequently asked questions
Do we need our own model?
Usually not at first. Hosted models plus good prompts and your own data (retrieved at request time) cover most business use cases. Custom training makes sense later, once you have clear evidence of where a hosted model falls short.
How long does a first AI feature take?
It depends on data access and integration work more than on the AI itself. Keeping the scope to one workflow and one user group is the most reliable way to keep the timeline short.
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