Blinded By The Hype!

To the tune of Blinded by the Light by Bruce Springsteen.

Madman, PR, bummers
Marketers in the summer with a teenage solution
In the dumps with the mumps
As the adolescent pumps his way into the chat
With a boulder on my shoulder, feelin’ kinda older
I tripped the merry-go-round
With this very unpleasin’, sneezin’ and wheezin’
Calliope crashed to the ground

Some all-hot half-shot was headin’ for the hot spot,
Snappin’ his fingers, clappin’ his hands
Some bespot mascot was caught doing the robot trot
with a whatnot in its hand
Now young Scott with a slingshot finally hit a tender spot
and throws his vendor in the sand
Some bloodshot forget-me-not whispers, “Legal’s within earshot,
save the buckshot, buy on the lamb”

But they were blinded by the hype
Revved up like a deuce, another Gen-AI ectype
Blinded by the hype

They got down but they never got tight
They just won’t make it alright

Some brimstone, baritone, anticyclone, rolling stone
Preacher from the West
He says, “Replace the reporter”, hit me in the funny bone
I thought it was all in jest
And some new-mown chaperone was standin’ in the corner all alone
Watchin’ the young ones code
And some fresh-sown intern was messin’ with the data files
And all the while hoping it wouldn’t implode

Yeah, he was blinded by the hype
Oh, revved up like a deuce, another Gen-AI ectype
Blinded by the hype

He got down but he never got tight
He won’t make it alright

Some silicone sister with a manager mister
Told me they got what it takes
She said, “I’ll turn you on sonny to something strong
If I use the prompt with the funky break”
And desk-bound Aneed was checkin’ out the chatbot feed
To see if it was safe to go outside
And little Early-Pearly came by in his curly-wurly
And asked me if I wanted to try!

Oh, some hazard from Harvard was tripped on hype,
playin’ bacykard financier
Yes, and Dragon’s Den was trying hard,
they sent some dude with a calling card
He said, “Do what you like, just use AI here”
Well I jumped up, turned around, spit in the air, fell on the ground
Asked him which was the way back home
He said,” Take a right at the light, keep goin’ straight until night,
and then boys, you’re all on your own”

And now in the bazaar, a shootin’ star was ridin’ in a side car
hummin’ a lunar tune
Yes, and the architect said, “MVP, but first remove the cookie crumbs,
we’re gonna teach those boys to buy too soon”
And some distressed pro-buyer was complainin’ his system caught fire
from an AI he tried last night
Well, I purged his system cache as his drive I flashed,
I saw him calm and hoped that he’d be alright

But he was blinded by the hype
Revved up like a deuce, another Gen-AI ectype
Blinded by the hype

Mama always told me not to look into the eyes of a sage
Woah, but mama, that’s where the truth is, oh yeah

They were blinded
They were blinded
They were blinded
They were blinded

They were blinded

Is Valuing A Company At Over 10X Ever Justified?

THE REVLATOR asked a damn good question earlier this week in a comment to his Daily Double LinkedIn post where he asked how can Zip’s 2 Billion Valuation, for example, have any basis in reality or merit? Especially when THE PROPHET pointed out in his article on Zip’s Series D funding round at a 2.2 Billion valuation that this was a 40 to 50X revenue multiplier!

As usual, the doctor, was happy to answer, but since his answer is deep within the comments, it’s being recorded here for easy reference.

Damn good question. There’s no basis in reality for a valuation more than 10X (if the solution is currently priced right).

When you consider that most companies can’t grow at more than 40% year over year without imploding, as you can only identify good people, hire, onboard, and train them so fast; including support personnel. And while you can add partners for implementation, but they need to be trained and supported (and until you have a well flushed out “academy” and good Full Time support reps for each project sold through a partner, rapid growth will likely be disastrous); at 1.4x it’s 7 years for the company to reach a 10X valuation.

If this is truly a fantastic company, we’ll allow an average 50% growth rate, it’s about 5.5 years to reach a 10X valuation, which is about as long as any investor wants to wait for a return on their money.

Using this math, in order to justify a 20X valuation, it would require a vendor to DOUBLE prices the minute they received the investment (and screw over the existing customer base while limiting the future customer base). Unless the product was insanely underpriced or the vendor could add a lot of extra value in a short time frame (and neither is likely since a vendor grossly underpricing likely wouldn’t have survived long enough for a raise, and development takes time), doubling the price is just not justified.

Now, double this to 40X and the only reasonable explanation is that the investors are as high as a kite or trying to win a P!ss!ng Contest at their shareholders expense.

And the only way these investors are going to recoup that investment is to achieve rapid up-front growth and flip the investment up the chain until the company is eventually acquired by a mega tech player who’s been around for 40 years and will be around for 40 more … and can wait the 15 to 20 years to get their money back (because that’s about how long their customers get locked in for).

Two and a Half Decades of Project Failure

  • 2024 Bain: 88% of business transformations fail to achieve their original ambitions (Source)
  • 2023 HBR: Some estimates place the failure rate as high as 80%.
  • 2023 Gartner: states that 85% of AI projects fail. As well, 87% of R&D projects never get to the production phase.
  • 2023 EY: 2/3 of senior leaders have experienced at least one underperforming [digital] transformations in the last 5 years (Source)
  • 2020 Standish Group: 66% of technology projects end in partial or total failure (based on the analysis of 50,000 projects globally). 31% of US IT projects were canceled outright and the performance of 53% ‘was so worrying that they were challenged.’ (Source)
  • 2020 McKinsey: 17% of large IT projects go so badly that they threaten the very existence of the company (Source)
  • 2020 BCG: 70% of digital transformation efforts fall short of meeting targets (Source)
  • 2020 KPMG: 70% of organizations have suffered at least one project failure in the prior 12 months (Source)
  • 2019 Everest Research Group: 78% of enterprises fail in their digital transformation initiatives (Source)
  • 2018 PWC: 75% of digital transformations fail to generate returns that exceed the original investment (Source)
  • 2018 Standish Group: only 29% of IT project implementations are successful, and 19 percent are considered utter failures (Source)
  • 2017 Gartner: 75% of all ERP projects fail (Source)
  • 2016 Innotas: 55 percent had a project fail in the last 12 months (Source)
  • 2015 Genpact: more than 66% of digital transformations fail to meet expectations (Source)
  • 2013 Innotas: 50 percent had a project fail in the last 12 months (Source)
  • 2012 McKinsey: large IT projects run 45 percent over budget and 7 percent over time, while delivering 56 percent less value than predicted (Source)
  • 2011 HBR: average project cost overrun is 27%, 1/6 projects is a black swan with a cost overrun of 200% or more Source
  • 2011 Forrester: 70% failure rate of change management initiatives (Source)
  • 2010 Deloitte: only 37% of projects delivered the functionality on time and budget meaning that 63% of projects failed to some degree (if not entirely) (Source)
  • 2009 Standish Group: failure in 68% of projects is probable (because success in 68% of projects is “improbable”) Source
  • 2001 Standish Group: 52.7% of projects will cost 189% of their original estimates and 31.1% of projects will be canceled before they ever get completed (Source)
  • 2001 Robbins-Gioia Survey: 51% viewed their ERP implementations as unsuccessful while 46% did not feel the organization understood how to use the system (Source)
  • 2001 Conference Board Survey: 40% of the projects failed to achieve their business results within one year of going live those that did achieve benefits had to wait (at least) six months longer than expected (Source)
  • 1999 Gartner: 75% of e-business projects will fail to meet the business objectives through 2002 (Source)

Is it just me, or is it the case that:

  • many of the firms who have been chronicling project failures for over two decades are also
  • many of the firms that have been guiding IT projects for over two decades?

Advanced Supplier Discovery Today — No Gen-AI Needed!

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 enterprise 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 be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that were (about to be) in development five years ago and are now available in leading best of-breed systems. And we’re continuing with Supplier Discovery.

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 found, or 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 Supplier Discovery that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Supplier Discovery success. (This article sort of corresponds with AI in Supplier Discovery Tomorrow that was published in March, 2019 on Spend Matters.)

TODAY

Deep Capability Match

As noted in our original posting, if you want a custom produced FPGA, an industrial strength power converter that can handle feeds from your wind farms and water wheels, or a new state-of-the-art surround sound system, you don’t want just any supplier. This is especially true if all they do is produce a fixed set of products, use production technology that is not appropriate for the design you want, have a record of sourcing inferior raw materials, or don’t have the right quality processes in place.

So, when we last tackled this subject five years ago, the new/leading supplier discovery platforms were working on deep capability match that could take a set of requirements for a product, or even a bill of materials, and find matching suppliers for the parts.

Especially since all this needed was deep capability identification and tagging across categories, products, and services that included production process, certifications, materials, etc. Which means that deep capability match was essentially just a super smart search capability across not just a few, but dozens of requirements — as long as the data was properly structured and indexed.

This requires the ability to crawl websites and extract all text and documents, OCR those documents to text, and then semantically process for the relevant information along the recorded dimensions. This just required classical semantic processing which uses ontologies, semantic networks, and custom trained (neural) networks for POS/concept identification when classical processing is not sure. Tech that has now been around and ready for production use for over 15 years. The big challenge was the magnitude of data that needed to be processed and indexed, which is not a problem anymore given the processing power of racks, the size of modern data centres (which require 10X to 100X the processing power for the Gen-AI trainwrecks that don’t deliver), and modern distributed processing algorithms and technology.

And, of course the ability to do rapid semantically aware reg-ex (across similar key words / phrases) for anything not indexed, or indexable in a standard taxonomy.

Resource Capability Match

Sometimes you need very specialized services. As we noted five years ago, for new product design, you need an engineering resource who has designed similar products and is familiar with the new production technologies and components that are on the market. For software implementation, you need a team who has installed the current software in a similar environment that has the same ERPs, OSs, data sources, etc. For utility installation, you need engineers with the right skills and certifications. And so on.

This is essentially just a variant of deep capability match, except you are matching on the services capabilities and the individual’s resumes. Getting here was just determining everything that was relevant for a service, processing large amounts of data, tagging and indexing it appropriately, and supporting very deep multi-faceted searches, using the same semantic technology as described above, but tuned for different service (instead of product) domains.

That’s All For Now, Folks!

Again, focus on supplier discovery was, and still is, limited as there were, and still are, only a few vendors doing it. The good news is that we’re starting to see the technology predicted for “tomorrow” five years ago starting to emerge in these platforms as well.

SUMMARY

Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for a few years, have been in development and discussed as “the future of” Supplier Discovery tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand. And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s March 2019 articles on Spend Matters. The primary purpose of this article, as with the last, was to explain how more sophisticated versions of traditional ML and AI methodologies could be implemented in unison with human intelligence (HI!) to create smarter Supplier Discovery applications that buyers could rely on with confidence.

Ghosts in Data Can Indicate Fraud but …

… so can the telltale signs, and if you don’t even know how to spot those signs, why even look for the ghosts (which are very hard to find).

A recent article over on Dev Discourse on Spotting the Ghosts Using Big Data to Detect Fraud in Government Purchases described the results of a study by the University of Craiova, Romania, Institute of Financial Studies, Bucharest, and three other universities that examined how big data and online systems can help make public procurement more transparent and fair.

They analyzed the data from Romania’s public procurement system in 2023, where the government made 2.25 million purchases that totalled about 3.22 Billion Euros. In this study, the researchers were particularly interested in “exclusive” relationships, where a vendor only works with one public entity. They found that over 14% of all public purchases fell into this category, which is concerning as these exclusive deals can indicate problems like favouritism or fraud because they don’t follow the usual rules of fair competition.

This is just one standard way to identify potential fraud. Other ways, as noted by the article, are to

  • look for unusual transaction values,
  • look at the geographical distribution of unusual transactions or sole-source relationships (and for clusters in particular) as many happen in specific regions (suggesting that certain areas have higher risks of fraud)
  • look at deals that were completed too quickly (such as those completed within minutes of posting) and that were awarded considerably after hours or on weekends,

If you’re not even doing these basics to identify potential fraud, then you’re not ready to look for ghosts in the data.

And when you are doing this, and you’re struggling to weed out the likely fraud in sole-source, unusual transaction value, and transactions completed at weird times, the next step is to do a basic analysis on the supplier. As the report indicates, the correlation between award level and supplier financial performance should be correlated, not inverted. If a supplier with poor financial performance keeps getting sole-source awards, that’s a BIG RED FLAG.

Then, run the standard contract-purchase_oder-invoice matching to make sure the amounts line up. And if you do all of the above, you’ll find more fraud, non-policy compliance, and overpayments than you ever thought possible. No ghosts needed. (But if you ever get to the point that all of the above comes up blank, reach out and the doctor will tell you how to find ghosts in the data as well as ghosts in the machine.)