AI: Applied Indirection, Artificial Idiocy, & Automated Incompetence … The April Fools Joke Vendors are Playing on You Year Round!

So on the one day of the year when they should be making the joke, I’m going to reveal it.

The vast majority of vendors who claim “AI”, where they want you to think “AI” stands for Artificial Intelligence, have no “AI” in that context, and many don’t even have anything close. A few may have “Assisted Intelligence” (Level 1) and even fewer still may have “Augmented Intelligence” (Level 2), but “Analytical (Cognitive) Intelligence” (Level 3)? Forget it! And as for, Level 4, “Autonomous Intelligence”, which is the baseline that must be met before you could even consider a system true “AI”, doesn’t exist (at least as far as we know). (ChatGPT would be a 3 on this scale, 3.5 if you’re dumb enough to use it to power a semi-autonomous application.) (For more details on the levels of “AI”, see the detailed Pro piece the doctor wrote over on Spend Matters on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?.)

However, thanks to ChatGPT/OpenAI and other offerings, every vendor all of a sudden feels that their solution has to have “AI” to compete, and is now claiming they have AI when, at best, they’ve implemented some third party “library” into their analytics module, which itself may or may not be AI, or, at worst, they just have classical rule-based automation and statistical-based predictive analytics (i.e. trend analysis) but have called it “AI” because, just like a classic decision-tree expert system from three decades ago, it can make a “recommendation”. Woo hoo.

Not that this is nothing new, three years ago a study by London Venture Capital Firm MMC found that 40% of European startups that are classified as “AI” don’t actually use AI in a way that is “material” to their business. MMC studied 2,830 “AI” startups across 13 EU countries, and in 40% of cases, [they] could find no mention of evidence of AI. (See the great summary in The Verge.) And even that statistic is a bit misleading, because I’m willing to bet that the “evidence” they did find was technology that didn’t necessarily mandate “AI” and could be implemented with “classical” techniques because, as a longtime blogger, analyst, due diligence professional and, most importantly, a PhD in theoretical computer science (read: advanced applied mathematics), I have found that most claims of “AI” weren’t really AI — in most cases they were just using a combination of automation and/or configurable rules and/or advanced statistics and/or machine learning and just had some of the foundations, but no real “AI”.

In our space, real “AI”, and by that I mean strong Level 2 / weak Level 3 (which is the best you can get) is quite rate and specific use cases are few and far between, and most AI is simply semi-unsupervised machine learning for transaction/categorical classification (spend analysis) or clause identification (contract analytics).

The problem is that, when no one really understands what “AI” is, and given that less than 1/10 Americans have the mathematical competency to even begin the university studies to try and garner an understanding [Level 4 on the PIAAC], it’s really easy form them to try and pull a fast one on you. This is especially true when the solution is able to automate certain tasks or recommend best practices in the majority of situations faster and more consistently than the average buyer (who, let’s face it, is under-educated — thanks to limited supply chain / operations management programs and almost no real Procurement training in Colleges and Universities, under experienced, and not an expert in modern technology), and the solution can be made to look “smart” (but, in reality, is dumber than a doorknob and definitely dumber than Maxwell Smart). But it’s not smart. Not at all.  And don’t be fooled.

The good news is the marketing manager using Applied Indirection to push a false AI solution at you probably doesn’t have a clue what they have anyway, and a few smart questions asked by someone who understands what AI is, and isn’t, can probably get pretty close to the truth pretty fast. For example:

1) “We have advanced AI data auto-class. It’s the most intelligent, and accurate, classification in the space.”

‘How does it work?’

“It uses a multi-level neural net that has been trained on tens of millions of records across over a hundred clients in the indirect space.”

‘Great, so basically it categorizes transactions based on similarity to other transactions in a slowly evolving manner, and I’m guessing for a new client in the indirect space, out of the box, you’re around 85% to 90% accuracy out of the box and you approach 95% with semi-supervised retraining over time — and that’s the upper bound and it will never be perfect.’

“Uhm, … well, … more or less … “

‘Got it!’ At this point you know it’s “AI” level for classification is augmented (as it learns and evolves over time), and barely, but it’s not “the best” mapping in the space as platforms that use AI to suggest rules (upon implementation and then for unmapped transactions) and do mapping and categorization based on the user selected and verified rules can produce 100% accurate mappings, always outperforming an “AI” solution that uses neural nets that are good (but not perfect).

‘Do you use AI anywhere else?’

“Uhm, what, why? It’s great where, and as, it is.

And now you know that there is no real AI in the analytics part of the platform, and there’s no reason to choose it over any other.

2) “We use AI for OTD prediction and risk in delivery prediction.”

‘Cool. What algorithm do you use?’

“Huh, what do you mean?”

‘How does the application compute the OTD and/or risk associated with the delivery.’

>Wait for the hand off to their “data scientist” …< “We use a blended least-squares method to produce a prediction function where, if there is enough data for the product, carrier, and lane, we’ll primarily use that data for the function, but if there’s not enough, we’ll use the most similar (using a mathematical distance function) product, carrier, and/or lane data … “

Is that AI, well, if there’s some sort of learning involved in the selection of “similar data” or recommendations as to parameter tuning IF parameters can be tuned, maybe, but this is just classical statistical trend analysis and not really any different than classical ARIMA based forecasting from the 70s, and did they have ANY AI then?!? (The answer is “NO”!)

3) “We use AI for our supplier recommendation process?’

‘Sounds promising … please explain!’

“We compute a relevance score taking into account a large number of factors including product base, geographic location, diversity, risk, etc.”

‘OK … how … ‘

>Cue the Eventual Hand Off to “Data Science” Team<

“Product Base is computed as a percentage of the category they can likely cover, geographic location as an average distance function, diversity as an estimate of diversity employment if there is no diversity ownership data (in which case it’s just 50%), the risk score from our risk model, etc. “

‘So, in other words, it’s just a formula … ‘

“A very sophisticated multi-level formula with conditionals and nesting that computes … “

‘Got it thanks!’ NO AI! Not even a hint there of as it’s just a functional risk score that could be built in ANY application with a formula builder.

This isn’t to say that a solution without AI isn’t right for you! (In fact, it probably is!) It’s all about solving your business problem, and many problems have been solved in our space just fine for the last decade or so with rules-based workflow and automation, optimization, and statistical modelling and trend projection. When guidance is needed, decision trees/matrices tied to expert curated best-practices (the modern equivalent of a classic “expert system”) often work better than one could imagine. In other words, it’s not AI, it’s not the hype, it’s what solves your problem, reliably and predictably time-after-time.

So don’t fall for the false hype and be the April fool.

Fifty Golden Rules

If you were smart enough to Simplify to Succeed, great on you! If you haven’t, because you’re still wondering whether you can test the waters and go it alone:

Garry Mansell will be releasing “Fifty Golden Rules” this summer, specifically targeted at those of you who are thinking about starting your own business. Written by someone who has built successful business from the very small to the very big … including taking Trade Extensions from an unknown player to the driving force behind advanced souring at Coupa … Garry has the experience, and has learned the lessons, to get it right. As someone who now spends his days guiding new companies and entrepreneurs, you know this book is being written for you. Be sure to follow Garry on LinkedIn for insight and updates.

Simplify to Succeed

BUY THE BOOK, published by Brown Dog Publishing, on Amazon UK (available in March 2022).

Want best practice advice on how to build and execute a Successful Strategic Sourcing Structuring? Bookmark Simplify to Succeed and read daily!

Garry Mansell is one of the true experts on global sourcing best practices (and where and when to apply technology). As a former Global Sourcing Leader for Mars, Garry has 15 years of real-world experience in the trenches that started before we had software, global trade agreements, and service providers to make it easy. Then, as Managing Director for Freight Traders, he headed one of the first businesses in the world to run online Tenders before merging into Trade Extensions and leading the development of one of the most advanced Strategic Sourcing Decision Optimization platforms, which was ultimately acquired by Coupa where Garry then headed the entire upstream Source-to-Contract function before semi-retiring to focus on modernizing CIPS, advising new startups (as a Board Member on multiple boards), and, hopefully, writing that book! (Hint, Hint!)

Check it out and find out the benefits of “smart money” (which applies to start up functions as well as start-up businesses), becoming a customer of choice (so you get your supply while your competition runs short), building the dream team (and how to do it — because you definitely don’t want to end up with the B-Team), and becoming a better buyer.

(Right now, Garry is penning the best free advice you can get multiple times a week.)

Sustainable Supply Chains Sacrifice China! (Most of the Time.)

Where your supply chain is concerned, China has just demonstrated what SI has known for over a decade — it is the enemy. (This isn’t the only situation where China or the CCP is the enemy, but those are different rants. Note that we do NOT equate China or CCP with Chinese people. Most Chinese are NOT the enemy of your supply chain or democracy just like most Americans are NOT the enemy of intelligence and common sense.)

Long time readers will know that in the naughts, SI spent a lot of bandwidth telling your deaf ears that you should be investing heavily in nearshoring and home country sourcing because of the dangers of outsourcing in general, and, the dangers of oversourcing to a specific country, like China, in particular — which have finally become very apparent. It’s too bad it took a freakin’ pandemic to make clear how dangerous it is to outsource so many critical products and JIT materials to a country halfway around the globe, especially when such sourcing in bulk across the industry leads to the lack of capacity close to home due to factory closures and talent evaporation.

There’s a reason the doctor told you two weeks ago to remember the 80’s (and the early 80s in particular) … and that’s because that’s the last time most multi-national corporations in the Americas got outsourcing right … when they were near-sourcing to Mexico (who should build the wall just to keep Trump out, but that’s yet another rant for another day).

Let’s face it, some stuff just shouldn’t be sourced from home. Stuff that’s not critical, stuff that’s very expensive to make at home (but easily trucked across a single border) for various reasons (which can go beyond labour to energy costs if there are no affordable renewable sources nearby, transportation costs for raw or unprocessed materials are ridiculous otherwise, etc.), or stuff where most of the raw materials or necessary environmental conditions (for growing, mining, etc.) are just not present at, or near, home.

But when you consider a typical organization, how much stuff really falls into this category? First of all, you have to exclude any product for (re)sale that’s a primary profit line. Then you need to exclude any raw material or component critical to production unless you just can’t get it nearby. Then any product necessary for security or safety. And so on. At the end of the day, you don’t have much left, and if you’re doing the analysis right, you’re going to be left with:

  • raw materials and products just not available nearby (because you need certain growing conditions, large deposits of a mineral only found in certain geographies, etc.)
  • processed materials or chemicals where the raw materials are very expensive or dangerous to transport
  • products unique to a culture or region
  • novelty or other items not critical to your business

which (before the short-sighted wall-street loving common sense hating clueless and unskilled consultants of the late 80’s and early 90’s, like Steve Castle, put everything into the outsourcing bandwagon and blinged it out beyond belief) were the only products a company would outsource halfway around the world and still the only products a company should be sourcing from halfway around the world. Everything else should be near-sourced, and if really critical or the cost differential is small, home-sourced.

This also means that just shifting everything to another country in the BRIC, and India (which is ruled by a more open, transparent, and dependable democracy) in particular, is also NOT the answer. (They may not be the enemy, but they are still NOT the answer.)

So, unless you want your Supply Chain to completely collapse after the next global disaster, go back to basics, remember the smart outsourcing decision from the 80s, reopen those Mexican factories, and start near-sourcing again. And then, where you can, bring it back (close to) home.