[hubraumhugo]

I think it comes down to relatively unexciting use cases that have a high business impact (process automation, RPA, data analysis), not fancy chatbots or generative art.

For example, we focused on the boring and hard task of web data extraction.

Traditional web scraping is labor-intensive, error-prone, and requires constant updates to handle website changes. It's repetitive and tedious, but couldn't be automated due to the high data diversity and many edge cases. This required a combination of rule-based tools, developers, and constant maintenance.

We're now using LLMs to generate web scrapers and data transformation steps on the fly that adapt to website changes, automating the full process end-to-end.