Generative AI: definition, value and limits
Definition
Generative AI creates new content such as text, images, code or audio by extending patterns learned from large training data. Unlike classic software that runs fixed rules, it produces suggestions that read differently every time. The practical value is drafting and routine content faster, while a human reviews and takes responsibility for the result.
Why the Term Matters
Generative AI drives many current project requests, often without a clear goal behind them. As a neutral advisor we separate excitement from value and first ask which concrete process should be relieved. The term matters because it sets the expectation that outputs sound plausible but are not automatically correct. Who understands this builds in review steps instead of blind trust.
When It Is Relevant
- Recurring text work such as offers, summaries or product descriptions eats time.
- Existing knowledge should become searchable and turned into drafts.
- Early prototypes should show whether a use case carries at all.
- A fixed-price entry should clarify which generative cases deserve priority.
When Not
When a task demands exact, traceable results with no leeway, such as booking logic or legally binding calculations, generative AI is the wrong lever. With a missing data base or without an owner for quality review, a short readiness look is more honest than an immediate rollout.
Industries That Use It
- Law and tax advisory: speed up first drafts of filings and client letters before a specialist finalises.
- Machinery and plant engineering: generate technical documentation and offer text from existing templates.
- E-commerce and retail: produce product copy and variants in many languages from master data.
- Education and training: draft learning material and exercises from existing curricula.
Typical Use Cases
- Drafts for offers, emails and reports from bullet points.
- Summaries of long documents with references to the source passages.
- An internal assistant based on your own manuals and policies.
- Translation and tone adaptation of existing content.
- Code drafts and test cases for simple automations.
Decision Guide
| Signal | Generative AI fits | Alternative |
|---|---|---|
| Lots of drafting, human final check possible | generative AI with a review step | manual creation is too slow |
| Exactness with no leeway needed | rather not | rule-based software or a formula |
| Data base unclear | check readiness first | avoid a rollout without a base |
| Only a standard task sought | an off-the-shelf tool suffices | no custom build needed |
Example
A plant engineering firm wants faster offer text. In a fixed-price potential analysis we check which templates and master data exist, test a drafting assistant on ten real offers and define a firm sign-off by sales. This describes the method, not an asserted saving.
Service Connection
The entry is usually the AI potential analysis to prioritise generative cases, or the AI readiness check for the data base. Legal questions are handled by the EU AI Act consulting.
Sources
This framing describes common practice in using generative models. Statements on the EU AI Act and data protection are general orientation and not legal advice; they must be checked case by case against the currently applicable legal texts.