Short answerTo add AI to an existing process, choose one specific task with unstructured input (reading documents, classifying messages, answering from internal documentation), gather real examples to measure accuracy, compare AI with a simple rule-based alternative and integrate it as one more step in the workflow, with validation, confidence thresholds and human review for uncertain cases. After launch, monitor accuracy and cost.
Start with a specific task
"We want to use AI" isn't a task yet. A task would be: "we want invoices from 200 suppliers to go into the ERP without typing them". The task defines what data is needed, how success is measured and what happens when the system gets it wrong.
Tasks AI does well today
- Extraction: pulling fields out of documents in different formats.
- Classification: deciding what an email, ticket or document is about and where it should go.
- Search: answering questions from your own documents, citing the source.
- Drafting: preparing a first version for a person to edit.
- Summarising: condensing long threads, reports or feedback.
Build a test set before anything else
Gather real examples with their correct answer. A few hundred are usually enough to start. With that set you compare approaches, choose a model and check changes don't make things worse. Without that set there's no way to know whether the system gets things right.
Compare with something simpler
Try the simplest thing first: rules, templates, keywords. If it reaches the accuracy you need, you don't need AI, and you save cost and unpredictability. If it doesn't, you now know how much better AI has to be to be worth it.
Integrate it with safeguards
- Validation: check the output with rules (totals add up, the supplier exists, the date is valid).
- Confidence thresholds: anything below goes to a person.
- Human review: a clear screen to check and correct; those corrections feed the test set.
- Logs: store inputs, outputs and decisions to audit and debug.
Data protection and regulation
In Europe you need to know where data goes and whether providers use it for training. If needed, choose providers hosting in the EU or open models on your own servers. The EU AI Act sets obligations by use case: most internal automations are low risk, but decisions about people (hiring, credit) need more care.
After launch
Track accuracy on reviewed cases, the share that needed a person and the cost per item. Re-run the test set whenever you change model or instructions. AI systems rarely fail all at once; they usually get worse little by little, and only measuring shows it.
Published: · Digrow Technologies