Category Archives: AI

Gartner Inadvertently Makes the Case for NO AI in Supply Chains (which includes Source to Pay)

Gartner, which promotes the use of Generative AI in customer service, even though it did place Generative AI on the Peak of Inflated Expectations on the Hype Cycle for Emerging Technologies, just inadvertently made the best case for never, ever, ever using AI anywhere in the supply chain, including Source-to-Pay, and we love it!

In a press release on their newsroom in late September, where Gartner Says 80% of Supply Chain Not Accounted for in Current Digital Decision Models, the subheading clearly stated that Digital-to-Reality Gap Shows Current Technology Use Fails to Improve Outcomes for Supply Chain Decision Makers.

As a result of this “digital-to-reality” gap, Gartner’s research, based on an analysis of 600 survey responses of supply chain decision makers, not only found that current use of digital models to analyze trade-offs made no meaningful impact on the rate of good decision outcomes but actually found that slightly more bad decisions were made with the use of digital tradeoff analysis than without and marginally increased the percentage of bad decision outcomes. Moreover, More than half of supply chain leaders reliant on digital technology to make a recent strategic decision told us that they felt they would have landed on better decision outcomes without the use of their models, and our analysis suggests that they are correct.

In other words, if source-to-pay and supply-chain decision makers cannot even make decisions when relying on traditional, focussed, machine learning and modelling technology, there’s no chance an unpredictable probabilistic incarnation of Artificial Idiocy that randomly changes its output by the millisecond is going to make good decisions. And the reason is the same — just like traditional (guided) (machine learning) models require good data and a digital representation that covers the majority (if not the entirety) of the process and relevant variables, so do Generative AI models and, in just about every organization on the planet, this necessary digital representation DOES NOT EXIST!

As a result, applying AI without the data it needs to have even a snowball’s chance in h3ll to make a decision is pretty much guaranteed to lead you to worse decisions than you, or any other intelligent human with a decent understanding of the situation, will make without the use of any technology whatsoever.

You don’t need AI, you need end to end process modelling, data collection, data enrichment, data validation, and the ability to use those end-to-end digital tools, interpret the data and recommendations, and make good decisions off of that. And since, with the current rate of digitization, it’s unlikely the majority of organizations will go from 20% supply chain digitization to 80% supply chain digitization (which is the minimum level of digitization you should have before even considering any AI, even for inconsequential decisions) by the end of the next decade, you should not even have AI for decision making on your future roadmap before the next decade rolls around.

the doctor doesn’t say this often, but thank you, Gartner. (Because it really is the case that stupid is as stupid does.)

The 1-Step Guide to Responsible AI in Procurement

Forbes recently published an article on Responsible AI Procurement: A Practical Guide For Selecting Trustworthy AI Vendors. It wasn’t bad, but it missed the point.

Today, there’s only one way to responsibly address AI in Procurement.

JUST SAY NO!

1) We don’t really understand proper AI Governance (especially when most vendors are using third parties which are illegally scarping content, not checking for bias, and tweaking models on the fly without consideration for the new problems the on-the-fly tweaks will cause).

Plus, it’s not just ethical codes of conduct, it’s agreeing on what the ethics are, and, most importantly, making sure the models are transparent and unbiased — but we don’t know how to do that today, especially since all these models are huge black box models.

2) You can demand all the evidence you want from the vendor as backup for the vendor claims, but if you can’t verify it, how can you trust it?

3) These models require huge datasets to train. Even if you know the data set used and the processing method used, how can you be sure every element was properly vetted? Just like one bad apple can spoil the bunch, just one bad element in a clustering or optimization model can spoil the entire model. Just one!  It only takes a small amount of bad data to spoil a model, regardless of the model used.

4) These models can fail, and sometimes fail spectacularly. If you don’t understand the model, you don’t understand where it can fail, and thus what to look for. Also, many minor incidents (which can foretell future catastrophic failures) will go unnoticed if a human isn’t checking everything.

5) These models are not secure … the AI can leak any training data at any time without warning. Your vendor can have every security certification under the sun, and all will be for naught if they use LLMs.

So, JUST SAY NO!

Yes, McKinsey This Is Generative AI’s break out year, BUT:

We should NOT be celebrating the fact that it broke out of the prison it should be contained in only to:

So, even if your Global Survey confirms the explosive growth of AI, you should not be celebrating Generative AI’s breakout year and hold off celebrating until someone manages to put this destructive brain-dead genie we’ve unleashed back into the bottle it was released from!

That’s Right, You Do NOT need AI for Automation!

In our last article, we stated that our space was full of Overpriced “AI” you don’t need in source-to-pay, and one of our three examples was “Sourcing Automation” in Sourcing. To be clear, we’re not saying you don’t need automation — the whole point of software has always been efficiency through automation — we’re saying you don’t need “AI” automation.

The reason we’re doubling down into this topic is that we know there are a number of vendors pushing AI Automation and while automation is very good, AI is just not needed. But we know you’re going to get pushback if you echo the doctor‘s viewpoint here, so we’re going to double down into the details and explain why no AI is needed for great automation.

In our last post, we noted that, at its simplest, it’s the ability to auto-source a (set of) product(s) or service(s) once the need has been identified or the request approved. It’s useful, but you don’t need AI to accomplish this, just good-old rule-based (workflow) automation. After all, it’s just

  1. instantiating a new RFP (which can be done if you have a template tied to the product/service types)
  2. distributing it to known, approved suppliers (which is easily done if you have supplier management that tracks approval status and associated products/services)
  3. collecting the bids (automated submission management through a portal or provided spreadsheet for upload)
  4. selecting the lowest bids and marking it as an approved award (simple analytics)
  5. assembling the contracts (with templates, it’s just sucking in the supplier details, product details, and bids using tag-based search and replace)
  6. push it into the e-Signature portal (via the API)
  7. alert the buyer when the contract is ready for signature (via alerting)

1 You just need templates, and good providers have had those for a long time. And “AI” is not going to invent one you can trust.*1 It’s not too hard to tag your (provider’s) existing templates to all of the products and services you buy, and you only have to do it once.

2 When you onboard a supplier, you should tag it as approved, associate it with the products and services it is approved for, look up its risk and environmental scores, and track its performance over time. If it’s performance drops, it can automatically be suspended from consideration for new projects using old-fashioned business rules that will prevent it from being included in events it shouldn’t be. Thus, approved supplier management isn’t that hard to do and simple saved searches find all the suppliers that should be automatically invited to an event.

3 RFP and e-Auction software has been around for 25 years, so don’t let anyone ever tell you that you need AI.

4 If you’re trying to administer an award subject to constraints or goals, that’s good old fashioned strategic sourcing decision optimization. That’s not AI. MILP using classic tableau and interior point algorithms works just fine in predefined scenarios that suck in the organizational constraints … that leading SSDO (Strategic Sourcing Decision Optimization) providers were building over two decades ago.

5 Contract templates should be prescribed by Legal Counsel, not by software flipping random bits using layered statistical algorithms in combinations no one truly understands. The vendor will provide you with templates, but you should be the one reviewing them to make sure they are too your liking. This includes the standard clauses and variation by geography, industry, or risk you want to address.

6 Software integration happened for decades before AI.

7 Alerts have been standard software capability for decades, no AI needed.

If the right data is captured, and the right rules are written, standard workflow-driven software systems can be fully automated without any AI. The only thing preventing them from going from one step to the next is the human verification checkbox being completed. You can turn that off and they will work just fine. So, again, don’t be fooled that you need AI for Sourcing Automation, because you don’t. And with rules-based systems, you’re guaranteed you won’t get the odd, unpredictable result, every 10th sourcing project (because AI is only statistically effective, which means, eventually, it will always fail).

*1 Sure “Generative AI” can generate one. But there’s no guarantee it won’t be hot garbage.

Overpriced “AI” You Don’t Need in Source-to-Pay (S2P)

Everyone and their dog is trying to sell you an “AI” solution. Most of which, as we continually lament is “Automated Idiocy” at best (and “Applied Indirection” at worst, see our article on the April Fools joke vendors are playing on you year round that relaunched SI full time). Some vendors, for select capabilities, actually have the first stage of AI, Assisted Intelligence and a few, for very select capabilities, actually have the second stage of AI, “Augmented Intelligence”, but, and this is what they won’t tell you, especially if you’re a mid-market (MM), you probably don’t need it.

In fact, if you don’t yet have complete S2P, we’d wager that you absolutely don’t need it and likely won’t get an ROI from it, at least not with respect to the price tag they try to charge. (Just like spending more than 120K a year on S2P as a MM generally decreases your Return On Investment [ROI].)

While what is and is not effective and valuable can be situation dependent (just like certain high-priced capabilities can be highly valuable in 10M+ categories but detrimental in 1M categories), there are some capabilities that are almost never valuable, and in this post we will give you some examples, and the reasons therefore, so that you will be able to both analyze whether or not a solution actually has AI AND whether that AI will provide any value.

While there are dozens of capabilities being marketed as AI (which, if implemented using advanced techniques could fall under Level 1 AI), we’ll pick one from three (3) areas as our goal is exposition and not an all-inclusive treatise (that’s a novella, not an article).

Sourcing: Sourcing Automation

What is this? At its simplest, it’s the ability to auto-source a (set of) product(s) or service(s) once the need has been identified or the request approved. It’s useful, but you don’t need AI to accomplish this, just good-old rule-based (workflow) automation. After all, it’s just

  • instantiating a new RFP (which can be done if you have a template tied to the product/service types)
  • distributing it to known, approved suppliers (which is easily done if you have supplier management that tracks approval status and associated products/services)
  • collecting the bids (automated submission management through a portal or provided spreadsheet for upload)
  • selecting the lowest bids and marking it as an approved award (simple analytics)
  • assembling the contracts (with templates, it’s just sucking in the supplier details, product details, and bids using tag-based search and replace)
  • push it into the e-Signature portal (via the API)
  • alert the buyer when the contract is ready for signature (via alerting)

And while very useful for non-strategic and/or low-value categories, no AI is needed. Now, the vendor will counter with multi-round, but guess what, you just implement ceiling, best X, or mandatory response rules before allowing a supplier to progress to the next round and close round one and open round 2 on pre-set dates.

Low bid prediction? i.e. when should the RFX be ended? Guess what, if the platform has anonymized community intelligence, integrates with market data feeds, or supports should-cost modelling (and knows industry average margins), it’s pretty easy to calculate what the low-bid should be (and any bidder that bids lower has likely made an unsustainable bid that should be ignored), and end bidding when you hit that. No AI needed for any of this.

Contract Management: Contract Generation

The ability to auto-assemble a contract is cool, but leading platforms have had it for almost 15 years. How?

  1. A contract template for the category that specifies the clauses that are required, the data that needs to be included, and the meta-data that is needed to assemble the contract correctly.
  2. Default clause templates for each clause, with variants for each geography or industry of interest

That’s it. Then, the system just uses rules to select the template and the clauses and fill in the required supplier, product, and price data from the RFP.

Invoice-to-Pay: Automated Invoice Parsing

Yes, it’s great if you can reduce the number of invoices you need to review from an average of 15% with issues to 1.5%, but let’s face it, you can reduce it to 5% or less with just a little bit of automation, no AI needed.

Almost all invoices are coming in electronic these days, and suppliers that invoice regularly and want to be paid fast will use EDI, XML, or PO-flip through the portal, which means the invoices will come in electronic in an easily parseble format. Missing data / errors will be easily detectable in address, PO field, line items, amounts, etc. when there is an empty field or a mis-match between expected and received data (based on the PO, etc.), etc. and the invoice can be flipped back with notifications of issues for the supplier to correct. Most of the time it will be an honest mistake or oversight and the supplier will happily make the correction to get paid.

The remaining problems will fall into two categories.
1) Those few suppliers that don’t have a solution and have to send PDFs (or images) through e-mail, but those aren’t the suppliers doing massive business (as we’re talking about one time suppliers or consultants for the most part)
2) Those suppliers who don’t accept the requested corrections and have a dispute that needs manual intervention.

With respect to these two categories.
1) An “AI” parsing solution with 80% accuracy is just going to create more manual work, since you will have to correct all the errors anyway (which will be just as much work as entering the data in the first place). (And if the invoice automatically flows through, then it flows through with errors, and that touchless system leads to overspend. Better to touch an extra 3% of invoices and get it right than trust AI that, instead of saving you money, overpays suppliers or sends money to non-existent fraudulent suppliers.)
2) No AI will resolve a dispute. In fact, it will just annoy the h3ck out of the supplier representative and make the dispute worse.

So don’t fall for “AI” in the sales-pitch, even if it isn’t automated idiocy. The vast majority of it you don’t need as good rules-based workflow, configuration, and human ingenuity in the solution still gets the job done (and as the vendors get smarter, the software gets better, and that manually driven best-of-breed software optimized for the process doesn’t make company ending mistakes).