The basis for AI to generate added value for users and customers is well-prepared and intelligently networked data. At Quanos, for example, this data serves to simplify manual processes and save users’ time. One example is the intelligent preparation and provision of data from PDF files in digital form.
Service organizations generate a lot of valuable data over time. However, historical data sets are often only available as scanned PDFs. AI-powered tools help to easily digitize the information: The tools can learn to extract specific information selectively and recognize contexts and elements such as tables, paragraphs, or forms. In this way, AI tools offer efficiency advantages over optical character recognition (OCR).
Metadata, which is becoming increasingly important for networking and distributing data, can also be enriched with the help of AI. One of the strengths of AI is its ability to classify content at lightning speed. AI is capable of evaluating large data sets in a short time and recognizing patterns as well as anomalies.
To ensure high data quality, a confidence value is often used for the interpretation of AI results. This value indicates how certain the decision of an AI model is. Companies can set a threshold and manually check results when they fall below this value. This approach requires manual reworking. Nevertheless, users gain efficiency because they do not have to start from scratch.
Technical writers also achieve a gain in efficiency if they have sections of text created by a Large Language Model (LLM) — a generative language model with artificial intelligence — and then correct them as an "expert-in-the-loop" and give them the finishing touches.