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 back-office (fin)tech, 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 measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that are (or soon will be) in development (now) and you will see in leading best of breed platforms over the next few years.
Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently emerging, and will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)
Today we continue with AI-Enhanced Supplier Discovery that is in development “today” (and expected to be in development by now when the first series was penned five years ago) and will soon be a staple in best of breed platforms (and may be found emerging in development beta versions of some platforms). (This article sort of corresponds with AI in Supplier Discovery The Day After Tomorrow that was published in March, 2019 on Spend Matters.)
TOMORROW
Intelligent Supplier Discovery
How is this different than deep capability match that is available today? Because it sure sounds like the ability to match to a very detailed request is “intelligent”. Compared to most search capabilities in most platforms, it is. But there’s more to selecting a supplier, especially one with whom you need a long term relationship, than just tech specs and certification checks. There are also performance considerations, innovation ability (hard to measure), culture, and other, softer factors.
First of all, you need a platform that can predict the ability of a given supplier to innovate and, more importantly, innovate for you based upon your specific needs. To do this, you need to chart the “innovation history” of a supplier (how many innovations per year, typical gap between innovations), compare the “innovation history” to other suppliers in the industry and category, use a predictive curve fitting or other ML algorithm to predict it’s rate (vs. the average). This is a lot of semantic processing to identify innovations and approximate dates, a lot of trend analysis to find the right predictive algorithms, and a lot of calculation. And then you need the ability to refine the innovation rate by category for a multi-category supplier so the trend line matches your need, and not your competitor’s.
Secondly, you need to be able to parse the “reviews” not just for sentiment, but positive or negative interpretations of specific, relevant “soft factors” like communication, working culture, etc. and compute appropriate ratios or bands that can be compared and be considered in super search / match criteria that is relevant to your organization. Next generation targeted sentiment analysis on factors identified on deep semantic analysis. No Gen-AI needed, just domain specific refinements of traditional approaches (trained on highly vetted, validated data sets).
Predictive Smart Search
For a company in direct manufacturing, electronics, pharma, or another industry where advanced innovation at a fairly rapid pace is required not just for growth, but continued market share retention, identifying the right suppliers is critical. This requires a very deep search, and for specific projects, potentially dozens of requirements and validations that need to be done before a supplier can be invited to an event.
So many in fact that, even if a buyer could identify all of these up front, building the search criteria to capture them all could be difficult. Next generation platforms will learn from each search entered into a platform for a product, category, supplier, etc. and extract the typical criteria, the frequency, and the preferences by organization and user.
Based on this data, when a buyer, new product development specialist, etc. starts a search for a new supplier in a category and/or for a product, the platform will predict which factors are relevant to the user, recommend those factors and factors, and intelligently build the right search and tolerances for the user. And then retrieve the best suppliers, ranked with match percentages.
None of this requires Gen-AI. Frequency is just frequency mapping by product, category, and supplier. Matches are matches as per deep search. Auto query creation is rules based automation. Soft factors are identified by semantic and sentiment analysis. And so on. It just requires a lot of Human Intelligence (HI!) to put it all together.
Is That All, Folks?
Probably not. The more data that is collected, the more analysis that can be done, and the more matching and prediction that can be done across people, products, services, and solutions. And the more “intelligence” (which CAN NOT be generated by Gen-AI) that can be put forward beyond your search before you invite a supplier to an event. But it’s the next step, and we’re going to stop here because we are going to refresh our series on Supplier Management as well.
SUMMARY
Now, we realize some of these descriptions are dense, but that’s because our primary goal is to demonstrate that one can use the more advanced ML and AI technologies that already exist, harmonized with corporate, market and community data, to create even smarter Supplier Discovery applications than most people (and last generation suites) realize, without any need (or use) for Gen-AI, that the organization can rely upon to reduce time, tactical data processing, and risk while increasing supplier intelligence and overall organizational performance. It just requires smart vendors who hire very smart people who use their human intelligence (HI!) to full potential to create brilliant Supplier Discovery applications that buyers can rely on with confidence no matter what category or organization size, always knowing that the application will know when a human has to be involved, and why!