Category Archives: Technology

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.

Coronavirus/COVID-19 Response: Analytics Can Help Get You Through the Crisis

In the first stage of the pandemic, mines close, processors close, or other suppliers of critical raw materials become unavailable and your direct procurement becomes threatened, and you have to identify new sources of supply quickly to maintain supply assurance, while also making the best selection for the business to keep total of cost ownership acceptable and predictable (as a lower cost risky alternative could put you back in the same position in a few months). You need good analytics to make the right decision.

In the second stage of the pandemic, factories close, certain distribution channels become unstable, and distributor stockpiles run out and indirect goods become scarce and problematic across key categories. And you need to respond. Good analytics will again be key as you don’t want to be going back to market in three to six months, but you also need to keep costs down to insure you have the cash to deal with cost spikes in direct lines where supply unavailability significantly tips the supply/demand balance scale or where costly expedited logistics will be needed. You again need good analytics to make the right decision.

And unless you have a modern best-of-breed Source-to-Pay suite with great analytics embedded or a best-of-breed stand-alone analytics solution, you don’t have anywhere close to what you need. Just a few of the questions you will need to answer include:

  • How much am I paying now for a product, and how much should I pay based on today’s commodity pricing and currency volatility?
  • How do I understand the cost impact of supplier failure?
  • How do I understand the cost impact of raw material availability?
  • How do I identify outliers that might signify future issues or opportunities?

… along with dozens more. So how do you answer these questions? What technologies do you choose? Check out the doctor‘s CORONAVIRUS RESPONSE: Advanced Procurement Analytics — find the risks hiding in your data, prioritize and take action Pro piece over on Spend Matters. Even if you don’t have Pro access, the content in front of the paywall is still useful and might give you some ideas on where to start.

Vendors They Are Complainin’

come gather ’round vendors
wherever you roam
and admit that the methods
around you have grown
and accept it as truth
tech reviews set the tone
if your time to you
is worth savin’
then you better accept it
or you’ll sink like a stone
for the time’s they are a-changin’

come purchasers, sourcerors
rally the call
don’t stand in the doorway
don’t block up the hall
subjectivity
it will cause you to stall
there’s a battle outside
and it’s ragin’
it’ll shake up you platforms
and rattle your apps
for the times they are a-changin’

come buyers and sellers
throughout the land
don’t let vendors fault
what you can understand
as market assessments
are beyond our command
the old ways are
rapidly agin’
push those out of the new one
if they can’t lend their hand
for the times they are a-changin’

In case you haven’t figured it out, SolutionMaps launched last month to the delight of practitioners who can get a 100% unbiased tech. vs customer view, and the disdain of a handful of vendors who (complain for weeks because they) think we should take more subjective factors such as long-term roadmap, innovation, market size, customer size and complexity, product strategy, market strategy, etc. etc. etc. into account (so our maps will look more like the other tragic quadrant and grave reports).

While we all readily and wholeheartedly agree that these are all extremely important factors in your vendor selection, none of these are relevant in platform due diligence, which is the first thing you need to do before considering a vendor for your shortlist. (If the platform can’t do what you need it to do, it doesn’t matter how great the vendor’s organization is.) Since this is the hardest thing for a relatively non-technical Procurement (or Finance) person to do, this is what, and only what, we focus on — verifying that the foundations of the platform are solid and that key requirements for the module / suite functionality we evaluate are there. If a vendor platform gets a good analyst score, you can be sure it’s solid. If a vendor gets a good customer score, you can be sure the vendor has a history of delivering on what they promise and/or providing great service. If a vendor gets good analyst and customer scores, then, for their target market, they are a great fit.

However, as we make clear in this white paper on How to Use SolutionMaps, just because a vendor is great for their current customers in their target market, that doesn’t mean they’re great for you. If their target market is mid-size companies and you are a F500, or vice versa, then they might not be a good fit for you. That’s where you have to do your market research and focus your pre-qualification RFIs — on the business, market, services/support, and other non-tech factors that are relevant to you. With SolutionMaps you know that if a vendor does well, you don’t have to ask 500 feature/function questions in the pre-qualification RFI, only general questions about the vendor’s confidence and capability to support the key processes you are looking to digitize and automate.

Our goal in creating SolutionMaps (and the doctor led the creation of the majority of the common platform elements; the sourcing, supplier management, and analytics maps; and the first iteration of the CLM map, that has only changed about 30% since) was to flip the traditional technology platform RFI process in Procurement on its end as we saw too many companies focussing too much on tech (usually starting from free meaningless feature/function RFIs), which they didn’t know, and not enough on their business needs, which only they know. With SolutionMaps, they have confidence in the technical capability of the vendors, and can focus on everything else that’s important to their organization (and not the subjective whims of an analyst who has to rate a large number of relatively non quantifiable factors. Since all of the elements we evaluate have a pre-defined technical scoring scale, all analysts evaluate the technical capabilities equally and the maps are computed using pre-defined mathematical formulas with no analyst input whatsoever once the scoring is done).

In other words, the maps were designed to help you as practitioners identify a group of vendors to send a pre-qualification RFI to, not for vendors to use as marketing tools (but they certainly can, as it’s undisputable proof they have a great platform if they show up).

So, as you can imagine, after every release,

The Vendors They Are Complainin’

CoronaVirus Response: Dear Procurement, AI won’t save you!

In the last few years, a number of vendors have been pushing artificial intelligence. Some vendors have even been pushing AI-based suites as the future of sourcing and procurement. And for a time they had a great argument. There are too many low-value, straight-forward, simple and/or tail-spend categories that are not getting appropriately sourced in an average organization that doesn’t have enough people power or hours in a day to properly address all organizational spend in a strategic manner and identify the range of savings and opportunities available to the organization. So why not let technology take over some of this spend, especially where it can’t do any worse than what is being done now?

After all, while there is no true AI, and we won’t have anything close for at least a decade, given the computational power of modern machines, intelligently coded and applied software with advanced analytics, machine learning, and evolving model paradigms can do quite a lot for us, and with respect to some specific tasks where intensive amounts of calculation are required, computer can do it better. Where some insight and intelligence is required, computers can still use advanced analysis and probabilities to get it 95% right 95% of the time and if the right outlier rules are coded, kick it out to a human when it’s likely the computer will get it wrong.

So, given the coronavirus-related chaos going on now, and your inability to deal with the majority of day-to-day tactical tasks and regular category sourcing as you have to constantly deal with new sources of supply interruptions, new challenges of working remote, and, in most industries, declining demands or revenues for the foreseeable, you’re probably thinking now would be the perfect time to invest in AI technologies to get a few workload monkeys off your back as you’re overwhelmed. Something that can take low-value, non-strategic, or commodity category management off your plate sounds like a dream come true.

However, now that you need it the most, I’m sorry to say that now is not the time to try AI. Moreover, adopting AI now would simply result in more catastrophic failures across the organization.

Why? How? Read the doctor‘s unlocked PRO on how AI won’t save you, but rules-based automation might! over on Spend Matters. There’s no miracle cure* for the damage caused by COVID-19, and now would be the worst time to try and adopt what would simply amount to silicon snake oil in these tumultuous times.

* But there was ample opportunity for prevention, and had you listened to the doctor a decade ago when he gave you the answer, you wouldn’t be in this mess right now. But that’s a rant for another day.

Digital is Decades Old. Don’t Get Fooled (Again)!

When se said that Digital. Digitized. Digitization. Digitalization. Are the New Buzzwords for Outdated Tech we were actually understating the reality. Digital may have been the buzz of the 90s … yes … the 90s (three decades ago), but, in reality we entered the digital age in the 70s – FIVE FULL DECADES AGO!

The first digital electronic watch prototype was developed in 1970 by Hamilton Watch Company and Electro Data and it hit the market in 1972. And while the first digital watch had a price tag of over $2,000, by the end of the decade, they were readily available for under $10 a unit and universally used. And they were … that’s right … DIGITAL!

It was only five years later that the world’s first digital camera was invented by an employee of Eastman Kodak. And while the first digital camera was not sold until 1989 in Japan and 1990 in the US, it existed. Since it used digital storage, it represented the move from digital to digitized. Again proving our point that the 90s is when we really entered the digital age.

The first digital mobile phone debuted two years after that, paving the way for Digitization … almost 30 years ago!

And then four years after that, digital satellite dishes 18″ in diameter hit the market, which were the best selling electronic devices in history at the time after VCRs. We were truly in the age of digitization by 1996. Almost 25 years ago.

Then, in 2000, we saw DVDs hit the market, and we truly hit digitalization as everything was not just transmitted, but stored, in 1s and 0s across all mediums. Two decades ago.

So, do you REALLY want to buy digital technology from a vendor that could be two decades old? Think about that before you start singing along to Laura Clark!

Remember, you’ll get what you sign up for … and you won’t necessarily like it!

So the next time you hear the word Digital or any variation of it, we recommend blaring The Who at full volume! Won’t Get Fooled Again!