AI is everywhere, and many businesses feel under pressure to “have AI”. Successful AI projects, however, rarely start with choosing a model. They start with a simple question: which work is time-consuming, repetitive or dependent on decisions drawn from large amounts of data?
Choose the right problem
Good candidates usually have clear inputs and outputs, happen often and can tolerate some margin of error.
- Classifying and routing customer support requests
- Extracting information from invoices, contracts and documents
- Recommending relevant products or content
- Forecasting demand and inventory
- Searching and answering questions over internal documents
Data determines quality
An AI model is only as good as the data it learns from and works with. Before investing, check whether the data is sufficient, clean and permitted for that use. In many cases, getting the data in order is the step that delivers the most value.
- 1Choose the problem
- 2Prepare the data
- 3Run a small trial
- 4Measure results
- 5Put into operation
Keep people in the loop
In the early stages, AI should assist people rather than replace them outright. Having staff review outputs, capturing their feedback and measuring results helps the business manage risk while improving the model over time.
AI works best when it helps people do better at work they are already good at.
Security and privacy
When customer or internal data is fed into an AI system, the business must be clear about where that data is stored, who can access it and whether the model is allowed to learn from it — while complying with personal data protection rules.
Fetek applies AI and machine learning to specific business problems, combining them with its software and data engineering expertise to put models into real-world operation.