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, 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 that were (about to be) in development five years ago and are now available in leading best of-breed systems. And we’re continuing with Procurement.
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 found, or 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 Procurement that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Procurement success. (This article sort of corresponds with AI in Procurement Tomorrow Part I, AI in Procurement Tomorrow Part II, and AI in Procurement Tomorrow Part III that were published in November, 2018 on Spend Matters.)
TODAY
OVERSPEND PREVENTION
By integrating trend analysis on demand and price, the platform can easily predict the date the budget will be exhausted, and if that’s before the end of the year, it can proactively pause an order for budgetary review EVEN IF it would be automatically approved in a last-gen system because it was still within budget and from an approved supplier.
POLICY IDENTIFICATION and ENFORCEMENT
One reason fake-take (better known as intake) solutions are so popular, besides the fact they make tail spend procurement easy (which we’ll discuss in more detail in our next part), is that they make it easy to identify and follow organizational procurement policies, especially since they will even guide a user through the correct process once the product / service need is identified.
At the end of the day, this is just guided buying with integrated access rules (who can request / buy something), budget rules (what budgets do they have or have access to), approval rules (who needs to approve and when), as summarized in, and extracted from, policy handbooks (which can be done with traditional semantic processing and human verification).
AUTOMATIC INVISIBLE BUYING
In last-gen platforms you had to define items you wanted on auto-reorder, define specific rules for each, and manually maintain this list, and associated rules, on an ongoing basis. But, at the end of the day, for example, MRO is MRO is MRO and commodity stock is commodity stock is commodity stock and there’s no reason that you shouldn’t be able to turn over the entire category to the platform. After all, if you’re ordering the item regularly, as we described in Yesterday’s Smart Automatic Reordering, you have enough data to compute demand trends, price trends, delivery times, and EOQs (economic order quantities) and, as long as everything is within a threshold of predictability, the system should just re-order for you — and if something appears to be going off the rails, pause automatic re-order and alert a buyer to examine the situation and either do a manual re-order (which could include accepting the system suggestion), change the rules or thresholds for automatic reorders, or redefine the category / reassign the product or service.
AUTOMATIC OPPORTUNITY IDENTIFICATION
As noted in “AI In Procurement Tomorrow: Part II“, a high-performing organization tackles at most 1/3 of spend strategically on an annual basis, due to lack of manpower and time. The fact of the matter is that, unless you have a true best-of-breed spend analysis system and the experience to use it efficiently and effectively (as well as sufficiently cleansed and complete data to work on), it’s a significant effort just to do the spend analysis required to identify and fully qualify the market opportunity and shape it into an appropriate market event.
But there’s no reason that the platform couldn’t encode all of the standard analytic workflows used by best-practice consultants, identify the top product/services/categories with the most spend not under contract/management, look at the spend variability, look at current market prices and trends, look at average historical community savings data (from community, consultancy, and GPO intelligence), and evaluate and rank opportunities. And the best platforms do. (Are the rankings 100%? No — no platform has complete market data or complete knowledge of every variance to a market situation, but 90% is more than enough as that will free the buyers up to keep up with market dynamics and do real exploratory analysis that is not easily automated.)
SUMMARY
Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for a few years, have been in developments and discussed as “the future of” Procurement tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand (especially with many of the fake-take and Gen-AI providers marketing these claims, even though they are not entirely realizable within their platforms). And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s November 2018 articles on Spend Matters. The primary purpose of this article, as with the last, was to explain how more sophisticated versions of traditional ML methodologies could be implemented in unison with human intelligence to create smarter Procurement applications that buyers could rely on with confidence.