AI raises the value of structured data

AI and three basic information classes

To make better decisions and create stronger digital processes, you need to distinguish between three basic classes of information and understand how today's AI can act as a bridge between them.

1. Analog, unstructured information

This is information that is not yet digital in a meaningful way: audio recordings, images, handwritten documents, and the like. It often contains important insights, but they are difficult to search, analyze, or connect to business processes.

However, modern AI models can “translate” these analog impressions into text or digital descriptions, allowing such information to become part of the digital ecosystem and start working for the business.

2. Digital, unstructured information

Here you will find Word documents, PDFs, Excel files, emails, internal wikis, HTML pages, all the digital artifacts that companies accumulate over time. This category is growing the fastest and often contains enormous amounts of knowledge, but without a clear structure.

This is where AI is making the biggest difference right now. Through techniques like semantic search, Retrieval Augmented Generation (RAG), and large language models, companies can for the first time truly leverage these mountains of information. AI can find connections, answer questions, summarize content, and connect documents that were previously completely isolated from each other.

3. Digital, structured information

Finally, we have information that is already organized according to clear rules: relational databases, database tables (e.g. in SQL) and systems that describe transactions, invoices, customer cases, inventory status and much more.

There is a common misconception that AI will replace this type of structured data. In practice, the opposite is true.

Structured information is needed more than ever and will continue to be critical for the foreseeable future. This is due to three things:

1. Business processes require accuracy:
   Invoices cannot be “approximately” correct. Transactions must follow rules. Cases must be traceable. AI is powerful, but it is probabilistic and guesses. Relational databases are deterministic and guarantee correctness.
2. Regulations and auditing are based on structured data:
   Law, economics and quality systems require data to be traceable, defined and controlled. AI can help interpret, analyse and automate, but it cannot replace the requirements for structure.
3. AI needs structured data to perform at its best:
   LLMs can be impressive in unstructured environments, but they are at their best when connected to clear, consistent data structures. Structured data is like pure building blocks. AI can then build services and intelligent processes on top of them.

AI ties everything together but does not replace the foundation

Together, these three classes of information create a whole that AI can enhance. AI makes it possible to:

• Digitize analog information (class 1 → 2)

• Understanding and navigating unstructured documents (grades 2 → 3)

• Combine structured data with natural language for better decision-making (Class 3 → AI-powered insights)

But AI never replaces the need for structured data. It only makes it even more valuable.

AI ♥ Low-code

We love automation and are continuously making new AI services available in Softadmin®. The effect is a huge potential to both automate and add a whole new layer of functionality.

AI is a natural part of both our work and the customized systems we develop, based on long experience and solid system expertise. With the AI ​​functions and integrations that can now be built in Softadmin®, we take the next step together with our customers.

Softadmin® in combination with the AI ​​services from Microsoft Azure AI Services and Azure OpenAI creates new conditions for robust AI applications that strengthen your processes with stable, scalable and business-oriented automation.