Where does AI actually help a business?
AI creates the most value in work people repeat every day, especially work involving text, documents or images. A useful rule: if a task is frequent, follows a pattern and its errors can be tolerated or reviewed, it is a good candidate for AI.
- First responses to customer questions and routing to the right person
- Classifying incoming emails, forms and requests
- Extracting information from invoices, contracts and documents
- Reports, summaries and first-draft content
- Product descriptions, translations and catalogue clean-up
Chatbot, AI agent and automation: what's the difference?
A chatbot answers a question. An AI agent carries out several steps towards a goal on its own: it gathers data, makes a decision and writes to another system. Automation runs flows whose rules are known in advance, without human input.
For most businesses the right solution combines them: rule-based parts are handled by classic automation, parts that need judgement by AI. Handing everything to AI can be both expensive and unnecessary.
Assistants that work with your own data
A general chat model doesn't know your products, pricing policy or internal procedures. To answer correctly, the assistant is securely connected to your documents, FAQs and product data.
Done well, the assistant can show which source an answer came from, and when it doesn't know, it says so and hands over to a person instead of making something up.
Data privacy and security
The first question in any AI project is which data goes where. For personal and sensitive data these decisions must be clear from the start:
- Which data is sent to the model, and which never is
- Where data is stored and for how long
- Whether the provider uses the data to train its models
- How GDPR and local privacy obligations are met
What drives the cost?
An AI solution has two cost components: one-off development and the per-use model fee. The model fee depends on volume, the model chosen and how much text each request processes.
That's why we measure with real data during the pilot and share the monthly usage cost as a calculation, not a guess. Not every task needs the largest model; choosing the right one lowers cost noticeably.
Start small: the pilot approach
Rather than trying to transform the whole company at once, it's healthiest to start with one clearly measurable task. The pilot runs on real data, results are measured and, if it works, it is rolled out. We cover which approach fits which problem in our post "AI Applications: Which Technology for Which Problem?".