Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.
To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech application, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence (as we don’t really have true appercipient [cognitive] intelligence or autonomous intelligence, and we’d need at least autonomous intelligence to really call a system artificially intelligent — the doctor described the levels in a 2020 Spend Matters article on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?) that have been available for years (if you looked for, and found, the right best-of-breed systems [many of which are the hidden gems in the Mega Map]). And we’re going to continue with Supplier Discovery. (Find our series on Advanced Procurement — No Gen-AI Needed! Yesterday, Today, and Tomorrow and our series on Advanced Sourcing — No Gen-AI Needed! Yesterday, Today, and Tomorrow through the embedded links.)
Unlike prior series, we’re going to mention some of the traditional, sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source-to-Pay technologies that are truly AI-enhanced. (Which, FYI, might not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)
Today we move on to AI-Enhanced Supplier Discovery that was available yesterday (and, in fact, for at least the past 5 years if you go back and read the doctor’s original series, which will provide a lot more detail on each capability we’re discussing). (This article sort of corresponds with AI in Supplier Discovery Today that was published in March, 2019.)
YESTERDAY
Smart Search
As penned in the original, while this is not really AI in any sense of the definition, extremely powerful searching and faceted filtering can really help an organization find the information, or in this case, the suppliers they are looking for. In the early days, searches were super simple — suppliers for product X in this category. If you wanted something like “suppliers in eastern Europe which supply widgets and sprockets with a third party financial risk score of 3 or less that is ISO UVWXY certified with a maximum carbon output per unit of Y”, forget it. You’d get a starting list of all suppliers in all of Europe that supplied widgets or sprockets (and not necessarily both) and have to vet them one by one.
But, thanks to advances in processing and database tech, traditional semantic processing, and tagging, you can now do multi-faceted searches across multiple dimensions on million record plus databases in less than a second, and do regex processing of associated descriptions for key words or phrases for specific requirements not tagged or indexed. And all of the semantic indexing and tagging can be done with traditional semantic analysis and custom trained last gen neural nets (and done with very high accuracy).
Community Intelligence
Like searching, while most of this technically doesn’t require ML/AI, community intelligence that spans ratings, capability verifications, (past) inter/intra organization relationships, and buyer sentiment can be quite useful to a buyer. It’s not just a group of suppliers that seem to meet your requirements of “suppliers in eastern Europe which supply widgets and sprockets with a third party financial risk score of 3 or less that is ISO UVWXY certified with a maximum carbon output per unit of Y”, it’s a group that will actually meet your needs, and the best way to zero in on that group is to use community intelligence from other buyers who have used the supplier and can provide valuable feedback on their capabilities and performance.
Most of this doesn’t require any ML/AI at all as it just requires ratings, feedback on various dimensions, recording of products and services used, etc. Only the sentiment analysis requires the AI domain, and it’s just building on semantic context analysis, which uses semantic processing and customized neural nets to predict sentiment (to detect things like sarcasm, etc.).
That Was It, Folks!
In the early days, Supplier Discovery was overlooked when it came to ML/AI, because it was not seen to be as important as sourcing, procurement or supplier management (because you knew who the suppliers were, you just needed to manage them better). However, as the leaders realized that the best opportunity for innovation was often in the supply chain, focus switched to supplier discovery and real ML/AI worked it’s way in.
SUMMARY
Now, we realize this was very brief, but again, that’s because this is not new tech, that was available long before Gen-AI, which should be native in the majority (if not the entirety) to any true best-of-breed Supplier Discovery platform, that is easy to understand — and that was described in detail in the doctor’s 2019 article for those who wish to dive deeper. The whole point was to explain how traditional ML methods enable all of this, with ease, it just takes human intelligence (HI!) to define and code it.