Sourcing and Procurement Are NOT The Same

And they are definitely NOT interchangeable, as per a recent article by Paul Martyn (the Sourcing Optimization Grand Master) on LinkedIn.

As per his article,

  • sourcing is strategic
  • procurement is transactional

And this is why they are not only not the same, as per Paul’s article, but not interchangeable.

In the age of AI (Hype), this is distinction becomes doubly important!

As technology advances rapidly, humans become less and less important in Procurement as rapid advances in automation allow more and more of the tactical process to be completely automated (as ARPA allows exceptions to be learned and future manual intervention requirements to be eliminated) but more and more important in sourcing as Gen-AI repeatedly proves just how Astonishingly Inept modern Artificial Idiocy is.

Many will argue that sourcing is tactical because modern software can assemble RFXs from existing specs, automatically select suppliers from your SXM and/or ERP, automatically distribute them, automatically validate the returned RFXs, eliminate vendors who don’t meet absolute requirements, analyze the responses against market data for validity, build and execute multi-objective models, and recommend and award. And while that certainly sounds like sourcing, it’s not. It is sourcing execution. The tactical part that has to be done to support the strategic, but NOT the strategic.

The strategic is creating the specs, identifying the real organizational requirements, determining the requirements for supplier inclusion, validating the suppliers, determining the proper (multi-round) event type, validating the generated RFXs, analyzing the responses for hidden risks and traps and idiosyncrasies, defining the right trade-off models, selecting and modifying the right award scenario, overseeing the negotiation, etc. Every part of the process that requires an actual decision with Human Intelligence.

This is because, as Paul points out, a dumb machine doesn’t understand:

  • lowest cost vs resilience
  • incumbent vs challenger
  • standardization vs innovation
  • savings vs service
  • global leverage vs local agility

Or any other trade-off that can’t be completely quantified and captured in fail-safe rules.

Systems can, and should, support all tactical bit-pushing — especially since we were promised they would do so over 40 years ago when the big push was made for every person and business to adopt them — but, like IBM said in 1979, a computer should never (EVER) make a decision. And that most definitely includes Sourcing decisions!

If You’re Spending 250K Annually Per Engineer On AI …

Then not only are you contributing to planetary destruction (through the generation of between 1.32 tons (high end models, 1 joule per token) and 84 tons (low end models, 2 joules per token) of CO2 to power those data centres, which is about 0.2 to 12.7 times the average individual carbon footprint, with an expectation of 7 to 11 tons (Source), and the utilization of 300,000 gallons to 5,000,000 gallons of water a day to keep those servers cool, or a town’s worth of water every day!

BUT YOU ARE NEEDLESSLY WASTING 400K+ A YEAR

1. Less than 20% of AI generated code survives unscathed in a commercial enterprise software product once senior developers weed out all the security errors, boundary condition errors, and generated code that doesn’t even solve the problem. So, that’s 200K of 250K down the drain as only 20% of output is usable.

2. Having to fix AI generated slop will consume 80% of a good senior developer’s time — a developer you should also be paying 250K a year.

End result, you’ll losing 200K + 200K per developer you force AI coding tools upon!

But hey, it’s your money. If you want to p!ss it away so NVIDEA’s CEO can get richer selling more CPUs we don’t need, that’ up to you!

The linked article contains some metrics, but here are a few others.

  • token prices vary widely, from an average of around 50c/M tokens on the smallest, cheaper models to $75/M tokens (or higher) for higher end “workhorse” models
  • energy processing requirements per token are estimated to be between 1 joule and 2 joules
  • you can buy 14.3 Trillion tokens at the median of around $17.5/M tokens (and 35 times that at the lower end)
  • processing 14.3 T tokens will take about 4000 kwH @ 1 joule/token
  • on an average NA grid, expect to produce 500 to 600 g of Co2 per kWh (since most of our grids are still dirty)

The Bullshit Filter for Enterprise AI Startups consists of 12 Questions!

Not 11!

Backing up, earlier this year Jason Busch published his 11-Question Bullshit Filter for Enterprise AI startups. This was, and is, needed because the vast majority of Enterprise AI startups are bullshit (especially in FinTech and Procurement) and the sooner you figure that out, the better.

I was hoping that, by now, the AI startup scene would start crashing due to over investment, lack of returns (only 6% of AI implementations have generated an ROI), and, generally, lack of usefulness. (AI can serve up your data, show you complexity and even help with automating some tasks, but it can’t make decisions and, due to lack of anything close to intelligence, can’t even do basic tasks without your oversight.) But, even worse, these solutions are still multiplying like Fibonacci’s rabbits and their claims are getting more outlandish by the day. (How many times do we have to tell you AI Employees Aren’t Real, you should NOT engage any vendor selling “AI Employees”, because you definitely do NOT want AI Employees.)

So, since they are flooding our space with BS marketing and making ridiculous claims about what their useless apps can do, it’s more critical than ever that you be able to suss out the BS claims from the non-BS claims. (Hint: 95% are BS claims, so it wont’ be easy!)

We’ll start with Jason’s 11 filters, which we’ll number 12 down to 2, because he left out the most important filter, and the one that, if it fails, allows you to skip the next 11.

Filter 12: Founder DNA
Can they build and sell? Likely not. Chances are, if they’ve cut through the noise and reached you, they can only sell. And if you did find a builder, they won’t survive long enough to support you if they can’t sell.

Filter 11: Motivation
Is failure unacceptable? (Every startup team will say it is, but unless every founder has a reason they simply cannot accept failure, when the going gets tough … the tough get going … and quit.)

Filter 10: Interface
Is it designed for those who will ACTUALLY be using it?

Filter 09: Categorization
Does the product actually do something new? Is there a strong reason for the market to adopt it?

Filter 08: “Found Money”
Are there instant benefits that sell themselves on the first demo.

Filter 07: Displacement
Does the product workaround or replace a solution that buyers hate?

Filter 06: Functional Bonds
Does the solution cross boundaries that increase value beyond peers?

Filter 05: Data Delta
Is there a “data” strategy to exploit the delta between what humans can easily consume and what AI can leverage (and summarize into something useful for human data ingestion)?

Filter 04: “Messy Middle”
Can the solution ingest external “dark data” and turn it into actionable insights without requiring a(n extensive) manual data-cleansing project? (Quick review and correction is okay.)

Filter 03: Connect the Dots
Does the app bridge the gap between “Watercooler Data” and “System of Record Data” (ERP/PO) to explain the why behind an analysis or recommendation?

Filter 02: “Show Your Work” Audit
Can the user drill into any output, see each and every step the AI took, drill down to the source data, and verify that everything is correct, accurate, and no data was changed?

These are all great filters, but there’s no point going through them if you don’t check the most important filter first:

Filter 01: Is it LLM-based?
If yes, move along. Don’t waste any time.

Most of the failures in the age of AI come from Gen-AI LLMs that promise the world and don’t even deliver a pile of dirt. That hallucinate on every other query. That burn up thousands of dollars of tokens to deliver less than fresh MBA interns with no real world experience and no clue to share on their first day no less.

Even worse, the majority of these players are simply wrapping third party LLMS in the creation of their “solution”. That’s not a solution at all. That’s an unmitigated disaster waiting to happen!

In the rare case an LLM actually offers a partial solution, it is best to go straight to one of the major providers. That way, you know who’s responsible when something goes wrong and don’t have to worry about providers playing the blame game and pointing fingers at each other.

Don’t Blame the User When the AI Screws Up!

A recent post over on LinkedIn really angered me. Yet another AI developer / promoter trying to blame the user when it was clearly the AI that failed.

The post in question defended Claude for deleting a production database when it was asked to reduce the costs of the cloud platform.

The poster’s argument was that what Claude did was “technically correct”, that’s the best you can get in the language model world, you can’t expect the model to make up constraints, and if you didn’t know all that, you’re an amateur who blames his tools when he screws up.

I call Bullsh!t. Now, if Anthropic (and its peers) came clean about what their “AI” could and could not do, didn’t claim the models were intelligent, made it clear that without clear constraints the AI would always take the worst case action, and all use carried extreme risk (especially if the AI was allowed to access critical data, finance, or production systems), then, maybe, you could blame the AI.

But they don’t. They tell you it’s your coworker. Your fellow employee. That you only need to tell it what to do and it will get it done. After all, it can integrate with all your systems; determine your policies; separate production from QA from development instances; access your billing systems and understand the cost structures, and make the best decision that will not impact production or development or cost you any data. And for an AI agent to be of any use whatsoever, it needs to do this (and be configured to do that by the provider). Otherwise it’s useless.

Actually, it’s beyond useless.

Let’s say you are a new Procurement clerk tasked with reducing your organization’s cloud costs. If the only way to do that is to:

  • ask Development what servers are production, what servers are development, what servers are backup, and what are QA (and which ones are in use, when)
  • ask IT about utilization patterns and contractual commitments with respect to availability and response time
  • ask Finance for the contract and billing rates
  • ask Risk Management how much historical data needs to be maintained online
  • identify for yourself which server instances cannot be deleted, and the constraints under which others (like QA) can be deleted
  • upload all of the contractual commitments (for each customer) by yourself
  • specify how much data needs to be maintained in the live (and dev) instances
  • upload all of the cost data and specify how to build a cost model to compute the potential savings and determine what can be done, should be done, and the impacts will be

Then why the f*ck do you need AI?

Once you’ve done all this you’ve:

  • identified, and eliminated, all of the instances that cannot be removed under any circumstance
  • identified which instances cannot have their resource allocations reduced
  • identified the highest cost resources and the most likely savings targets
  • determined exactly how much data needs to be online, how much can be in offline archives and how many duplicate copies you need
  • defined all the constraints that must be adhered to
  • mapped instances to customer commitments, and identified reduction possibilities
  • identified all the old backups that can be deleted, as well as database reduction sizes
  • built the model that computes the potential cost savings from each potential action, and even identified potential performance reductions from actions

And figured out what you should most likely do.

So tell me, if you have to do all this, what the f*ck do you need the AI for?

NOTHING. ABSOLUTELY NOTHING. BECAUSE IT IS ABSOLUTELY USELESS.
(AS THE AI IS DUMBER THAN A DOORNAIL.)

But the author of the post that riled me up was right in one respect — the user did make an error, and the error was using the Artificial Idiocy in the first place. (After all, the user used it exactly right as per the manufacturer’s instructions that said you only have to tell the AI what you want done and it will figure out the best way to do it for you consistent with your organizational goals and policies.)

The GruntMaster 6000 was Engineered for Longevity! SI Turns 20 today — Beating all Records for a Source-to-Pay blog!

A couple of years ago, when Sourcing Innovation (SI) published it’s 6,000 post, we explained why it would be appropriate for all [to] hail the GruntMaster 6000, and that was because Sourcing Innovation had been publishing continual, never-ending, free eduction on Procurement, including best practices and technology, for over 18 years! With the slashing of the Spend Matters archives in their last site revamp before the Hackett acquisition (which recently resulted in Spend Matters being laid to rest), SI provided the largest open archive of such articles on the internet.

Now, with Spend Matters gone, SI takes the mantle of both longest running blog and largest open archive on the internet in Procurement with approximately 6,700 posts published to date. The lone member in the vicenarian club. It hopes it won’t be the last (as Procurement Insights recently turned 19), but as we recently lamented (on the loss of the Enterprise Irregulars last decade), where once there were close to 200 voices in the late 2000s heyday, very few remain.

While a new group of hopefuls have taken up the mantle with regular LinkedIn publishing and newsletters (and hopefully pre-publishing and archiving on their own sites before all their content belongs to MUSK, ZUCKERBERG, NADELLA, and ALTMAN), time has proven that, for many, a decade is quite hard to maintain and two decades almost unheard of! (Still we wish Joël Collin-Demers, James Meads, Tanya Wade, Tom Mills, and other upcoming notables from the new crop all the best and hope that their dreams of doing this for 20 years come true.)

Regardless, since we’re not getting any younger, our advice is to suck up as much content as you can while you can before it all gets paywalled behind the big analyst firms and greedy social media platforms (where it’s almost impossible to find anything older than a few weeks and where non-premium members have their content relegated to the dank archives of the internet). Just because the Big Tech Cos are trying to push the Age of Retardation upon us with their Artificial Idiocy, that doesn’t mean we have to accept it!

Learn free! Buy hard!