PDF is de-facto standard for any invoicing, POs, quotes, etc.
If you solve the problem you can effectively programmatically deal with invoicing/payments/ large parts of ordering/dispensing. It's a no brainer to add it on to almost any financial/procurement software that deals with inter business stuff.
Any small-medium physical business can probably half their financial department if you can dependably solve this issue.
As far as I understand there are at least two standards (I know of in Germany): XRechnung and ZUGFeRD/Factur-X (which is PDF A/3 with embedded XML).
Small-mediums should be looking to consolidate buying through a few good suppliers and working with them directly to automate process, or adopting interchange formats.
Problem for some small-business is the cost (process changes, licencing etc) of adopting interchange formats and working with large vendors is prohibitive at their scale e.g. the airline BSP system.
I agree that solving the problem generally i.e. replacing an accounts payable staff person capable of processing arbitrary invoice documents will be comparable to self-driving in difficulty.
If a company deals with a lot of a single type of PDF, then the approach could be economical. I am actually involved in a project looking at doing this with AWS Textract.
Building machines that understand formats that are understood by humans is exactly what we should be doing. People should read, write, and process information in a format that is comfortable and optimized to them. Machines should bend to us, we should not bend to them.
If businesses only dealt with machine readable formats, everyone's computer would still be using the command line.
And there's real condescension in your post:
> Small-mediums should be looking to consolidate buying through a few good suppliers and working with them directly to automate process
You're saying that businesses need to change their business to accommodate data formats, but it should be the other way around.
The proliferation of computers in business over the last 50 years is precisely because businesses can save money/expand capacity by adapting the business processes to the capabilities of the computers.
Over that time, computers have become more friendly to humans, but businesses have adapted and humans been trained to use what computers can do.
You can go further. Invoices often contain block sections of text with important terms of the invoice, such as shipping time information, insurance, warranties, etc. To build something that works universally, you also need very good natural language processing.
This should be obvious, but the answer is because OCR engines are not terribly accurate. If you have a native PDF, you're far better off parsing the PDF then converting to an image and OCRing. But if OCR ever becomes perfect, then sure.
While Abbyy is likely the best, it's also incredibly expensive. Roughly on the order of $0.01/page or maybe at best a tenth of that in high volume.
For comparison, I run a bunch of OCR servers using the open source tesseract library. The machine-time on one of the major cloud providers works out to roughly $0.01 for 100-1000 pages.
- Better search engine results
- Identifying experts within a company
- Better machine translation
- Finding accounting fraud
- Automating legal processes
For context, the reason why Facebook is the most successful social network is that they're able to turn behavioral residue into content. If you can get better at taking garbage data and repackaging it into something useful, it stands to reason that there are lots of other companies the size of Facebook that can be created.
There's almost no question in my mind that most new data will endure in some form, by virtue of being digital from day 1.
The endgame for such a company, imho, is to become the "source entity" of information management (in abstracted form), whose two major products are one to express this information in the digital space, and the other in the analog/physical space. You may imagine variations of both (e.g. AR/VR for the former).
Kinda like language in the brain is "abstract" (A) (concept = pattern of neurons firing) and then speech "translates" into a given language, like English (B) or French (C) (different sets of neurons). So from A you easily go to either B or C or D... We've observed that Deep Learning actually does that for translation (there's a "new" "hidden" language in the neural net that expresses all human languages in a generic form of sorts, i.e. "A" in the above example).
The similarities of the ontology of language, and the ontology of information in a system (e.g. business) are remarkable — and what you want is really this fundamental object A, this abstract form which then generates all possible expressions of it (among which a little subset of ~1,000 gives you human languages, a mere 300 active iirc; and you might extend that into any formal language fitting the domain, like engineering math/physics, programming code, measurements/KPI, etc.
It's a daunting task for sure but doable because the space is highly finite (nothing like behavior for instance; and you make it finite through formalization, provided your first goal is to translate e.g. business knowledge, not Shakespeare). It's also a one-off thing because then you may just iterate (refine) or fork, if the basis is sound enough.
I know it all sounds sci-fi but having looked at the problem from many angles, I've seen the PoC for every step (notably linguistics software before neural nets was really interesting, producing topological graphs in n dimensions of concepts e.g. by association). I'm pretty sure that's the future paradigm of "information encoding" and subsequent decoding, expression.
It's just really big, like telling people in the 1950's that because of this IBM thing, eventually everybody will have to get up to speed like it's 1990 already. But some people "knew", as in seeing the "possible" and even "likely". These were the ones who went on to make those techs and products.
Legal technology. Pretty much everything a lawyer submits to a court is in PDF, or is physically mailed and then scanned in as PDF. If you want to build any technology that understands the law, you have to understand PDFs.
Changing their process might be more expensive than paying a lot of money for them to carry on as is for a few more years while getting the benefit of modern eyes on their content.
Edit: concrete example would be government publications like budget narrative documents.