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 Procurement 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. (This article sort of corresponds with AI in Procurement The Day After Tomorrow that was published in November, 2018 on Spend Matters.)
TOMORROW
AUTOMATIC CATEGORY IDENTIFICATION
Building on the above, there’s no reason it can’t look at common product / service characteristics from BOMs (bills of materials) and descriptions, find commonalities, and suggest new sourcing/procurement categories that would maximize opportunity and leverage. This is just building on last-gen tech with more encoded human intelligence (HI!), RPA, and (gasp!) math. This is especially useful for identifying when tail-spend should go to 3-bids-and-a-buy tactical sourcing and when mid-tier tactical categories are large enough for full blown strategic sourcing with strategy identification, in-depth market research, multi-round bids and negotiations, etc.
AUTOMATIC PROCUREMENT METHOD IDENTIFICATION
When we are talking about mid-tier tactical sourcing, when a category (currently in the tail) goes beyond a simple catalog / e-comm-like site buy, determining whether it should be a 3-bids-and-a-buy RFQ, auction, or negotiation with an incumbent (whom you have a relationship with in another category or who is currently getting most of the business off-contract) can be automated based on an assessment of current market conditions (supply vs. demand, price trends, category risk, etc.) and encoded Human Intelligence (HI!) on best-practice (and the conditions that tilt one method in the favour of another baed on past savings against similar market conditions). While it won’t be perfect, it will better than most buyers in most organizations will be able to do without deep category expertise and/or a lot of experience in strategy selection and implementation — and more than good enough for an average mid-market enterprise for the majority of their mid-tier spend.
ELIMINATION OF UNMANAGED TAIL SPEND
Tail Spend can be 30% to 40% of spend in some organizations, and overspend (as determined by a variance analysis, market prices across marketplaces, and/or average savings from a 3-bids-and-a-buy RFP or even just a bulk discount on standard catalog pricing) in the 15% to 30% range.
(That’s why so many laggards are getting bamboozled by the new generation of fake-take [better known as intake] procurement applications that make it easy to process requisitions and do one-time buys, because they often see a 10% savings on spend out of the gate and think they are doing fantastic, even when they aren’t. First of all, they are only getting market-price [because they aren’t doing real procurement, which requires a basic level of strategy, and definitely not doing strategic sourcing], which means they are leaving money on the table. Secondly, by not identifying items that should be bundled across requisitions from the week OR managed as MRO / commodity inventory [which can be managed automatically], they are wasting time (and thus money) processing essentially the same requisition over and over [and over]. And so on.)
However, given that we have made great advances in trend analysis, community intelligence, market price intelligence, demand management, market dynamics classification, etc., there’s no reason that, for any tail spend item, the system can’t, with high probability, identify the appropriate methodology for any requisition, which, for tail spend, should include:
- fulfill from inventory (and auto manage / order the inventory)
- fulfill from catalog (from contract / preferred suppliers)
- combine requisitions and fulfill via RFQ
- combine requisitions and fulfill via e-Auction
- fulfill as standalone RFQ
- fulfill as standalone e-Auction
- promote to a tactical sourcing / strategic procurement category
PERFORMANCE IMPROVEMENT
Procurement is always overworked and under-resourced from a people, capital, and technological perspective, so performance is critical. A great system will increase performance not just along the “cost savings” dimension (as that’s a given with Procurement, whoever said “I have been tasked to spend more” in Procurement), but also along the time, risk, and sustainability measurements.
A great system will monitor utilization and not only allow itself to be configured to minimize steps and effort for everyday tasks through built in configuration capabilities in the dashboards, workflows, rules, etc., but will suggest to the admin changes to configuration, process, or policy over time as the metrics indicate that changes would reduce process time. Process analysis systems already exist, it’s just a matter of integrating them into procurement systems and integrating the analytics necessary to do the suggestions and linking them to the workflow.
But procurement systems aren’t limited to identifying savings opportunities across money and time, they can also identify opportunity across risk if appropriate risk metrics are incorporated (and suggest strategies, suppliers, or products with lower risk) using trend and comparison analytics.
Similarly, they can integrate carbon models and carbon data and identify the (expected) carbon cost of every product or service being considered (depending on whether the data comes from an industry data base, country database, supplier measurement, or third party auditor, will determine how accurate the carbon value is), and identify suppliers or products that would reduce carbon, as well as the cost decrease and/or risk increase of any carbon improvements.
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 technologies that already exist, harmonized with market and corporate data, to create even smarter Procurement 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, spend, and risk while increasing output 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 Procurement 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!