Generative AI: definition, value and limits
Generative AI Updated
Definition
Generative AI is a class of models that produces new content, such as text, summaries, reply drafts or code. It differs from classifying AI, which only labels. In process automation it creates the draft that a flow processes further or a human approves. Value appears when the draft runs against checked templates and control points.
Why the Term Matters
In the mid-market, writing replies, summaries and standard texts costs a lot of time. Generative AI can prepare this step instead of replacing it fully. Who understands the term uses it where a draft suffices and a human approves. That keeps provable ROI tied to clear control rather than blind trust.
When It Is Relevant
- A flow needs recurring texts or summaries.
- A draft saves time even if a human still checks.
- Inputs arrive as free text and must be condensed.
- Templates and control points exist or are easy to build.
When Not
When a reply must be legally binding and no check sits behind it, generative AI is risky. For purely structured data you need no generated text but a rule or integration. And when every output is manually reworked, the time saving disappears.
Industries That Use It
- Mid-market customer service: draft replies to standard requests that a human approves.
- Law and tax firms: prepare client letters and summaries with a clear final check.
- Technical sales: assemble quote text from master data and enquiry.
- Marketing and communications: derive recurring text blocks from templates.
Typical Use Cases
- Draft replies to frequent requests and submit for approval.
- Summarise long documents down to the decisive points.
- Turn structured fields into readable text.
- Bring notes and minutes into a consistent form.
- Prepare translations and rewrites inside the flow.
Decision Guide
| Signal | Generative AI fits | Alternative |
|---|---|---|
| Recurring texts needed | draft with approval | text block by rule |
| Binding statement | check first, then send | human writes it |
| Only structured data | no generated text | integration or rule |
| No check in place | build a control point first | no live use |
Example
A mid-market company answers many similar service requests. A flow classifies the request, generative AI drafts a reply from checked templates, and an employee approves or corrects. RPA sends and logs it in the CRM. Value is proven against the baseline of prior handling time. This describes the method, not an asserted client metric.
Service Connection
The entry is usually process automation with AI, the build an RPA solution, and the framing the process automation consulting.
Sources
This framing describes common mid-market practice for using generative AI inside automated flows. Models, templates and data protection depend on the case, the systems and the tools and must be checked case by case.