AI use cases: 3 examples for your own AI

Most companies have already looked into AI and are currently assessing whether tools like ChatGPT are suitable for them or whether they handle sensitive data and therefore need local AI models.

From the discussions we have had with customers, a clear pattern emerges across industries, especially for local AI: three typical AI use cases are emerging that work almost everywhere and usually bring noticeable benefits quickly:

1. Search: How can we find internal knowledge faster?
2. Document creation: How can we efficiently create repeatable documents?
3. Content analysis: How can we quickly extract information from PDFs and emails?

In many companies, knowledge is scattered across email inboxes, presentations, meeting notes, tickets, network drives, and other internal data sources.

When a question needs to be answered quickly or a decision needs to be prepared, the same odyssey often begins: Where is the relevant information already stored internally? How can we reliably find documented answers? The search takes time and often remains incomplete.

For AI-powered search to really work, the following foundations are essential:

• Knowledge sources are digital.
  Content needs to exist in digital form somewhere. Paper-based knowledge can only be included after additional digitization.
• Access and permission structures are clearly defined.
  AI search must strictly respect roles and rights as well. A clean permission framework is mandatory.
• Search intentions are recurring.
  There are typical questions that come up again and again. The clearer these patterns are, the better the automation.

An AI-powered search acts as a kind of an "internal Google".
Instead of searching each source individually, AI indexes various data sources and enables semantic search: not just by keywords, but by meaning.

Local AI models are particularly attractive here: LLMs paired with retrieval-augmented generation (RAG) can search internal data, including confidential information, without anything leaving the company. This means secure access to knowledge with full data sovereignty.

Examples: “Show me similar projects for this customer,” “What risks were described in previous concepts?” and “Find the latest sustainability statements in our documents.”

• Major time savings
• More complete, consistent knowledge sharing
• Faster decisions through instant summaries
• Sources linked directly for verification
• Less reliance on individuals and knowledge silos

Many documents are created according to a familiar pattern. The effort involved is rarely in the thinking, but rather in the preparation: gathering information, structuring it, formulating it, adjusting the layout, managing versions. This takes a lot of time, and when it occurs regularly, it quickly becomes a cost factor.

AI-based document creation works particularly well in the following cases:

• The documents follow a clear structure or template.
  Recurring chapters, building blocks, or formats are key elements.
• The documents are created frequently.
  The more often a document type is created, the higher the payoff from automation.
• The process is stable enough to be standardized.
  Workflows and content don’t change constantly; they’re consistent enough to standardize.

In AI-based document creation, the model extracts content from existing data, structures it according to a template, formulates it consistently, and generates a finished document, such as a Word file or PDF.

Here, too, local AI plays to its strengths: especially when documents are based on internal company data, confidential customer information, or sensitive project details, LLMs in combination with RAG can process the content directly within the company’s own environment. This allows documents to be created automatically without having to transfer data to external AI services.

• Consistent style and quality across teams and locations
• Lower error rate because copy-paste errors and omissions are eliminated
• More documents with the same effort (scalable without proportional additional time requirements)
• Faster response times to customers and internal stakeholders
• Content is easier to update because building blocks can be maintained centrally

In many companies, information arrives in an unstructured format, for example via email, in PDFs, or as free text in tickets and logs. Relevant facts must then be extracted, classified, and prepared for further processing. If this is done manually, it involves a lot of reading, copying, and transferring, which increases effort and the risk of errors.

AI-supported content analysis works well in these cases:

• The target information is clearly defined.
  Which data should be extracted? (e.g., type of concern, product, urgency, contract number, sentiment, deadline, etc.)
• The categories are clear.
  Classification follows defined rules so that the AI can categorize reliably.
• The same kinds of requests recur regularly.
  There are enough similar entries to make automation worthwhile and reliable.

In AI-supported content analysis, documents and emails are automatically read, relevant content is extracted, categorized, and summarized, and then transferred directly to the appropriate follow-up process. Humans then continue to work with the results instead of starting from scratch every time.

Here, too, local AI is a major advantage: although much of the incoming content comes from outside the company, it often contains sensitive information about customers, contracts, or projects. Local AI models can analyze this data directly within the company and transfer it to processes without passing documents or emails on to external AI services.

• Massive time savings, especially with large amounts of input data
• Consistent quality, because AI categorizes according to the same criteria
• Fewer errors due to the elimination of typing and manual transfer
• Faster response times to customer inquiries
• Better analytics and reporting because unstructured data is suddenly available in a structured form

These three AI use cases are universal because they rest on a simple premise: wherever there’s lots of text, information, and routine work, AI acts as an amplifier. Local AI models allow this leverage to be used particularly securely, because internal and confidential data can also be processed directly in your own system without sending content to external services.

In other words, those who start with these use cases usually see measurable impact quickly while building a solid foundation for more advanced AI initiatives.