AI for Data Ingestion

Everyone seems to be adding AI to their products right now. Sometimes it’s impressive. Other times it feels a bit forced.

One area where AI has consistently made sense for me is data ingestion.

In education, we used to spend days pulling K–12 standards from different states into a correlation system. Some states published Word documents, others PDFs. The structure was never consistent — numbering would change, tables were embedded in strange ways, and there were always edge cases that required manual cleanup.

That kind of work was tedious, but necessary. It’s also the kind of problem AI is well suited to handle.

I’ve seen the same pattern in finance. Monthly reports from different projects often contain similar information, but arrive in completely different formats. Before any analysis can happen, someone has to normalize the data. AI can remove a lot of that friction.

I’ve even been asked about ingesting PDF job descriptions and converting them into a standardized format in a database. Not long ago, that would have been a fairly involved project. Today, it’s much more approachable.

At Okaya, our work centers on ingesting unstructured inputs — conversations, audio, and video signals — and helping teams discover mental health trends in a way that’s privacy-aware and responsible.

For me, this has been a useful reminder: AI doesn’t always need to generate something new to be valuable. Sometimes the biggest win is simply making existing information usable.

Other than coding & writing - What are the AI use cases that have saved you the most time?

Originally published on LinkedIn — view the original post for comments and reactions.