u/NickBaca-Storni

I was reading an interesting article about how a lot of AI use cases create value, but not all of them are “materially” visible in the numbers. There seems to be a gap between the types of projects companies invest in and the ones that actually show a "material" return.

Part of that gap, I think, has to do with the fact that a lot of the investment today is going into improving individual productivity. Copilots, assistants, tools that help people move faster.

And let’s say that works. Maybe something that used to take a week now takes a day. But then what happens with the rest of the time?

Is it actually being used in something more valuable, or does it just get absorbed into other low-impact tasks? or even nothing at all?

When AI is applied at the process level, things change. You’re redefining how the work gets done. Tasks get reorganized, and that time saved is usually reassigned in a more intentional way.

Which brings me back to the original point: which AI initiatives are actually getting executive or board-level backing?

Hey all,

A few months ago I asked here if anyone was using SAP Document AI.

At the time, most of what I saw (and what came back in the comments) felt pretty experimental. We recently had the chance to implement it with a client, so I figured I’d share the approach in case it helps anyone working on something similar.

I can’t go into full detail for obvious reasons, but here’s the general idea.

One thing we keep seeing: most GenAI use cases today are still at the individual level. Copilots, prompts, assistants. Useful, but they don’t really change how processes run, so ROI is hard to pin down.

In this case, we tried to push it a bit further and integrate it directly into the process, so it doesn’t rely on people actively using it.

Context (manufacturing):

Client manufactures seamless pipes, with heavy machinery that requires frequent maintenance and part replacements.

A big part of the process involves handling purchase orders for spare parts. Different suppliers, different formats (PDFs, scans, emails), and a lot of manual work to extract data and input it into SAP.

What we did (high level):

• Document AI on SAP BTP (AI services)
• Classification + data extraction from incoming POs
• Field mapping into SAP (vendor, materials, quantities, pricing)
• Validation rules + confidence thresholds
• Simple exception flow (only edge cases go to a person)

Before this, everything relied on structured input or manual entry. Now it works directly with unstructured documents.

Results (rough numbers):

40–70 hours/month saved depending on volume

In annual terms, this lands somewhere in the low six-figure range, mainly from reduced manual work and faster processing.

Nothing crazy, but meaningful enough to justify the implementation and keep exploring other areas where AI can be embedded directly into processes. Even small tasks like this can add up and save time and money.

Curious if anyone else here is working on something similar with Document AI.

P.S. I’ll be at SAP Sapphire 2026 in Orlando. Let me know if you’ll be there, happy to chat!