This guide explains AI integration in plain language for business owners and managers: what it really involves, how to decide what you need, the terms you will hear and what a good outcome looks like.
What AI integration really involves
AI integration adds capabilities such as extraction, summarisation and classification to systems you already use.
How to decide what you need
Start with a proof of concept on real data and measure accuracy before committing.
Key terms you will hear
| Term | What it means |
|---|---|
| OCR | Optical character recognition from images. |
| Embedding | Numerical representation of text for search. |
| Fine-tuning | Training a model further on your data. |
| Human in the loop | People confirming uncertain results. |
Typical project sizes
| Level | Typical scope |
|---|---|
| Proof of concept | Two to four weeks on your data |
| Production feature | Integrated with review workflow |
| AI platform | Several AI features and monitoring |
What success looks like
Success is hours saved and errors reduced, with people reviewing the uncertain cases.
Checklist before you start
- Write down the business goal and how you will measure it
- List who will use the result and what they need to do
- Decide who will own the accounts, code and content
- Agree on a budget range and a target launch date
Related reading: AI Integration cost in Pakistan and how to choose a AI integration company. Ready to talk? See our AI integration service.