As per our first instalment, it all depends on your point of view and whether you are willing to look beyond the hype, buckle down, and get the real job done.
For instance, just the following five technologies will eliminate 95% or more of your tactical sourcing, procurement, and supplier monitoring work — and all you have to do is find them, properly implement them, and use them. Let’s talk about them briefly.
Real DIY Analytics
The ability to analyze the data you want, when you want, how you want, enriched and augmented using the auxiliary data you want … and not in predefined dash-boards or hidden “AI Agents” which may, or may not, do the analysis you want (and need) … cannot be underestimated! Real value comes from ad-hoc analysis and investigating hunches, abnormalities, and trend changes when you discover them; not days, weeks, or months later when the “cube” has been refreshed, and it might be too late to correct a problem or capture an opportunity!
Remember, this is not 2005, this is 2025, and there are at least half a dozen great DIY (spend) analysis solutions that will do most of what you want, for a price tag that is a fraction of what you might expect, and if you are okay with full DIY, some of these start at a price you can put on a P-Card. For example, Spendata Classic (which can handle data sets up to 5 Million Rows) can be obtained for $699 a year, and Enterprise, which can handle data sets up to 15 Million records, which comes with unlimited use for 5 users (and view licenses for more), and some consulting and setup, starts at an amount that will surprise you. (You can still put it on a P-Card if you pay monthly.) And there is literally nothing you can’t do in it if you’re willing to apply a little elbow grease. It truly is The Power Tool for the Power Analyst.
(Strategic Sourcing Decision / Supply Chain Network) Optimization
Yes, it’s math. But you know what? Math works! And when you use deterministic math, it’s 100% accurate, every time! And it’s one of only two technologies in S2P+ that was been proven (by multiple analyst firms) to repeatedly identify 10%+ savings year-over-year (but since this was pre-COVID and pre- the 47th, we need to amend this finding to adjust for inflation and tariffs). And as an FYI, the other technology was NOT AI. (It was proper DIY spend analysis. Only Human Intelligence can intuit where to look for previously unidentified opportunities, the best AI can do is just follow a script and run standard analysis. Furthermore, the thing about spend analysis is that an analysis that identifies an opportunity only helps you ONCE — once you capture the opportunity, the analysis is useless. You need to do a new, and different, one.)
Rule Based Automation
When you think about most tasks across Source to Pay, most of them are just execution of simple, easily defined processes — most of which don’t require much (if any) intelligence and, thus, don’t need AI (and shouldn’t use an unpredictable AI agent when you can encode a process that gets it right, guaranteed, every single time. (Plus, the way you want to source, buy, pay, track, manage, etc. is probably a little bit different than your peers, and who knows how the AI Agent would do it for you. You certainly don’t!)
With rule based automation, you can easily execute an entire sourcing event in the background all the way to award if you like. It can run auctions, it can run multi-round RFPs with detailed feedback (it’s all calculations, response comparisons, and decisions on what data you want to share and how blinded you want it), it can run analyses and optimizations, it can calculate recommended award decisions subject to the constraints and goals that matter to you, present that to you for acceptance, or, if it’s a simple winner take all or top 2 situation, create the award automatically, send it out, get supplier acceptance, assemble the contract, and send it for e-Signature. You don’t need Agentric/Gen-AI, just tech we’ve had for over a decade!
Machine Learning
Now, when it comes to Enterprise Master Data Management and Administration (E-MDMA) and Invoice Processing, it can be quite a lot of work to keep up with the mapping, cleansing, and enrichment rules, and you don’t want to have to manually define all the new rules every time a new data element appears or a new invoice format arrives, especially if the system can auto-detect/”guess” 90% of the time through rule re-use and generalization. With machine learning, the system can keep track of your corrections, mathematically extract models, and adjust it’s rules to handle the new mapping again automatically as well as improve its suggestion logic when it doesn’t know what to do — increasing the chance that you just have to “accept” a new rule vs. defining it from scratch. (Unlike Gen-AI which just tries to find similar patterns somewhere to present you with something that may or may not have any correlation to your business and even reality!) And we’ve had great non-(pure-)Neural Network machine learning that works great with enough data for decades! Predictive analytics was making huge progress late last decade before this Gen-AI BS took over and could have helped Procurement departments automate 90%+ of what they wanted to automate with just a bit more development and effort by the leading vendors — it just required a bit more time, money, and focus. (Gen-AI has set us back a decade!)
Analytics Backed Augmented Intelligence
We don’t need machines to make decisions for us (especially when they can’t think, or even reason), we need machines to do calculations for us that help us make the right decision quickly and effectively. We need the machine to automatically identify and retrieve all of the relevant data, do all the relevant situational and market analysis, do all the predictive trend analysis, identify all of the typical responses with respect to the situation, predict the likely success of each, and present us with a set of ordered recommendations, complete with the calculations and supporting analysis, so we can pick one or realize that the machine didn’t/couldn’t know about a recent event or a human factor and that none of the responses are right (and that only we could craft one, with full information on the situation). The machine may not think, but the thunking it can do far exceeds our computational ability (billions of computations a second, all flawless), and that’s EXACTLY what we should be using the machine for.
If we give up on this Artificial Intelligence BS (even if the current models are right, machines need to be 100 Million times more powerful for it to even “mimic” human intelligence. That’s not happening any time soon) and instead just give all the machines all the (boring) grunt work, leaving us free to do what they can’t (strategy and relationships). If we do so, we can be at least 10 times as productive as we are now and deliver on the promises Gen-AI / Agentric AI / AGI never will, and do so at a small fraction of the cost. And oh, we have that tech today … we just need to deploy and integrate it properly!
And this is just the beginning of what you can do when you look beyond the hype and use your Human Intelligence [HI!] to cut through all the BS.