Category Archives: Economics

Turst is Real Procurement Currency — And That’s Why AI CANNOT Do Procurement!

A couple of months ago Garry addressed a point made by the Peter Smith, the Bad Buying Bard, which boiled down to an issue more important than anything technical where AI is concerned … and that point is Trust.

In his original post, Gary asked if AI would change Procurement. However, after reading Peter’s comment, he realized the real question is whether Procurement is trusted enough that the organization will accept Procurement setting the rules around how AI is used. As Garry notes, that’s the crux.

When it comes to trust, it’s not whether or not the suppliers trust Procurement that’s the real issue, it’s whether Procurement is trusted internally. If Procurement is not trusted, it will be bypassed, ignored, and even sabotaged. This includes the (mis)use of AI. If Procurement is not trusted, it will not have any authority, and the organization will not heed their warnings (based on logic and the research they are used to doing), charge ahead with AI, and become yet another failure contributing to the 94%+ failure rate (while costing the organization millions upon millions of dollars and wiping out any savings Procurement may generate, especially if the C-Suite dictates an AI-first solution for Procurement).

Furthermore, you can’t use tools that you cannot trust. And you can’t trust any Gen-AI Procurement platforms built on hallucinatory LLMs. Since hallucinations are a core feature, results can’t be guaranteed, and LLMs can’t even be counted on to follow explicit instructions (and will corrupt your documents and data even when explicitly told not to), you can’t use Gen-AI/LLM-based AI.

And, unless your data is clean, categorized, up-to-date, and easily accessible through modern APIs, “classic” AI won’t work either. Good Procurement Pros will remind you that you can’t jump straight to AI. Just like you couldn’t expect a tribesmen from a culture with no written word who never set foot in modern civilization to begin reading lessons on the works of Shakespeare accessible only on a modern tablet, you can’t jump decades of technology. Or process.

Successful Procurement requires:

  1. getting your processes in order
  2. getting the supporting data in order
  3. implementing classic technology with high-degrees of deterministic, dependable, determination

And then, and only then, do you sit down, identify where there are still inefficiencies and/or a lot of tactical bit-pushing work, and try to figure out where AI will actually help. This means that most organizations are still years behind where they need to be to successfully implement any AI. In the classic Hackett journey to best-in-class, which will take an average large multi-national 8 years, it will be at least 4 years before the organization is far enough along on any process to consider advanced AI. (For a mid-size, this journey can be reduced to 6 years, and then it’s 3 years before Procurement is ready for advanced AI. It’s always People, Process, and Data before AI!)

Fastest Freeway to Financial Failure? Gen-AI!

Not joking here.

First of all, AI is getting more expensive for coding.

Input-output token pairs, which used to cost pennies per M tokens, are approaching $100/M for high-end models.

An average enterprise app starts at 100,000 lines. It will require 2M output tokens for initial output. It will take at least 5 iterations to get code good enough for the devs to even begin to work with, or 10M tokens. Then you will have to test and debug, figure another 5 iterations, or 20M tokens. But this doesn’t include the context history or coding samples required to produce a baseline, integrate a security framework, or account for multiple service-based deployments. This will consume an additional 10X to 30X the token count, and you will require 40M to 80M tokens to produce the app along with an experienced team of senior developers who will have to shore, as only 20% of AI-generated code survives unscathed. And then comes the testing, debugging, and QA. This could double the token requirement again.

For coding, which requires about 20 tokens per line, it would, in theory, only require 10,000 tokens to produce 5,000 lines of code, which is the net-new production code you’d expect from a senior developer every year, but given that it will require at least 5 iterations to get something to start with, and then all the updates to get it to testing and then all the testing and debugging, that’s at least 50M tokens as per above — with prices expected to rise (and possibly double) by the time you’re done (at the current rapid rate of token cost increase), or $10,000 to $20,000. Not bad in theory, as a senior Dev costs you 10X to 20X that on the low end, but …

As we said before, only 20% of AI code ends up being usable, so you still need a team of devs to review it and fix the major bugs/issues. With 80K lines needing correction, and a top dev only producing 5,000 lines of net new production code a year, you would still need 16 devs. That’s still expensive. You might realize that you only need to fix the critical issues to get your MVP out the door, and cut the team in half because you can stagger the reviews and fixes to issues. And while you think you saved the cost of 12 devs …

As time goes on, you realize there are fundamental flaws in the code. The security framework it chose was an old framework off of an abandoned Github code branch that used a lot of methods and procedures that were already marked for deprecation in the next framework release, which hit as soon as you released your code. They all have to be redone. The “multilingual” support is clumsy and requires the manual production of very carefully crafted fixed format text files. The workflow is rigid and not malleable. You wanted it AI friendly, but it doesn’t properly support MCP. And so on.

Then, like so many enterprise app startups are finding, you can’t scale the MVP into enterprise quality, have to scrap it, and rewrite if from scratch. Which means the 10K to 20K in LLM cost and the 800K to 1600K + in minimal dev support cost to get the MVP up and running in a production environment was all wasted — most of your seed money went up in smoke, and you have to start from scratch.

Second, its performance is much worse for trying to correct/update existing code where it has to ensure all unit, functional, user journey, workflow, and integration tests still work. This is evidenced by the fact that many companies, like Uber are now blowing through their annual AI budgets in a quarter. Engineers trying to rely heavy on AI are already spending 2,000 a month! Backtracking the math, it’s easy to see that the amount of project code, documentation, and online (GitHub) samples it has to ingest and compute to create an output, that might not even be 20% acceptable on the first few passes, is astronomical!

Plus, as we’ve explained before, when a dev has to correct up to 80% of the code, you’re losing on the efficiency improvement if a dev is spending 20% of their salary to get you that 20% increase in code lines which, as we’ve also explained before, is still of a worse quality than if that senior dev had wrote it by hand, that’s not a savings. That’s, at best, net 0.

However, this isn’t taking into account that it will likely have to be refactored or written out in very short order. You won’t get the median 2.5 to 3 year lifespan for a small app or 5 to 7 years for an enterprise framework, you’ll get 0.5 to 1 year — which means you’ll write and re-write each line of code three times as often with the use of AI. Or, in other words, you’ll inadvertently spend three times as much on that code! And your customers won’t pay 3 times as much for an app just because you spent three times what you need to, so bankruptcy will be just around the corner!

Third, it is getting infinitely more expensive for any document processing with a legal ramification.

Judges are now fed up with AI hallucinations and slop. Include AI hallucinations, and you’re getting fined at a minimum, and probably sanctioned.

Even worse, if it takes out a risk mitigation clause or creates an unforeseen risk you didn’t catch, a failure could cost you (hundreds) of millions of dollars that you would have otherwise been protected against if an experienced lawyer had written the contract for you.

Fourth, it’s making us physically AND mentally sick.

The cognitive atrophy is becoming well documented. People aren’t remembering what they wrote even an hour later when they use Gen-AI. They are being lulled into a false sense of security and accepting its outputs, even when those outputs are false and dangerous to their health (and tells them to effectively commit suicide). (But go ahead, eat that poisonous mushroom. The one rock a day it told you to eat will protect you, right?) Average decline in mental acuity and performance after regular use is 17% (which effectively equates to a loss of 17 IQ points. In comparison, it took us almost 120 years since the Victorian age [before we had industrial revolution technology to make our lives easier or media to dumb us into submission] to lose 14 IQ points). It’s making our society mentally sick!

Moreover, given how much energy and water a modern data centre consumes annually (100MW for a hyperscalar site or an amount of energy that would power at least 10,000 greedy American homes for a year) as well as how much water it consumes for cooling (100M+ G, assuming it recycles efficiently, or easily 200M+ G if it doesn’t, which would meet all the water needs of at least 5,000 of those homes per year, if not all 10,000), when energy and fresh water is becoming in scarce supply in first world countries, we’re jeopardizing the well being of 10,000 people for every unneeded AI data centre that we build. Given that there are now about 11,500 data centers consuming about 2% of planetary energy and likely between 0.1% to 1% of available fresh/drinking water, that’s a lot of energy and water being wasted to produce cr@p code and poor documents that can often be produced better by interns*. Especially when, in energy or water stressed areas, these data centers take systems to the breaking point and risk our health due to lack of necessary heating, cooling, bathing, and/or drinking water.

But, even worse, since this energy often comes from grids powered by dirty coal and oil, and the water extracted from desalination plants also require energy from those same grids powered by dirty coal and oil, they are polluting the environment to a significantly measurable degree as they account for somewhere between 0.5% and 1.0% of global CO2 emissions. With the global slowdown in shipping thanks to all the conflicts in the Red Sea and the Strait of Hormuz as well as the lack of water (due to less rainfall) in the Panama Canal, and the rampant increase in Data Center construction, data centers will soon account for more CO2 production than global (unregulated) shipping, which is the dirtiest industry on the planet. That’s NOT good for our health!

* There’s a reason Builder.ai was successful in its efforts to pass off human-written code as AI for over 7 years. Human produced code actually works! Even hastily written shoddy code works better than AI generated code by orders of magnitude!

The Mythical AI ROI!

A few companies claimed ROI from AI. (About 6% if you believe McKinsey or 5% if you believe MIT.)

And by few, we mean a few. One in twenty (1 / 20) is not a lot. And that’s just some ROI, not amazing ROI. Not necessarily enough to justify the elimination of even a single human (that you had hoped to replace), as that human is still generating more ROI than the BS AI you were sold (and making decisions at a much higher success rate).

There’s only one way to get true AI ROI.

1. Stop believing in Artificial Intelligence, realize all the vendors claiming it are only offering Artificial Idiocy, and that the best you can get is Augmented Intelligence.

Repeat

2. Identify a major problem that is hurting.

3. Use your Human Intelligence (HI) to map the current, and required, workflows end-to-end.

4. Identify all the manual steps that could be automated with the right data.

5. Do the hard work of identifying where all the data is, implementing a data orchestration platform to collect it all, and make it forward deployed everywhere it is needed for task automation.

6. Automate each step with the appropriate (A)RPA tool.

7. Implement a workflow orchestration platform to connect all of the steps together to the extent possible which ensures everything that can be automated with the automation and orchestration tools is once the intelligent human provides the right inputs and makes the right decisions.

8. Analyze where humans are still involved and where human inputs and/or decisions can be further automated through the integration of additional (external) data feeds and encoding of the (business) logic the human always uses to make the decision.

9. Analyze what’s left and determine where “AI”, even with a poor accuracy rate and hallucinations, could be helpful to an intelligent human making decisions and acquire small, focussed, specialized model licenses only for those steps.

10. Ensure Augmented Intelligence, connected to your forward deployed data, is available everywhere Human Intelligence (HI) requires it to make a decision.

until all major problems solved.

One by one. Put the effort in once, do it right, and with modern tech, you’ll never have to do it again.

You only win with AI when you’ve first centralized, validated, and forward deployed your data; implemented deterministic (adaptive) robotic process automation everywhere you can, and identified precise use cases where custom solutions actually provide a benefit (and not just a fairy-tale promise).

There’s NO Faster Path to a Markdown than “Growth At All Costs”!

THE PROPHET is bemoaning the start of markdowns in private equity when he should be happy (as a former investor) they took this long to happen, especially when the reality is that these markdowns are going to start coming fast and furious in any firm that wants to still be around by the end of the decade.

This is because most of their portfolios in Software, and FinTech/ProcureTech software in particular, have been pursuing growth at all costs as a result of:

  • the insane valuations during COVID for FinTech/ProcureTech that helped companies buy and pay online
  • the insane valuations during the current AI-HYPE for any company that could convince the investors they had a unique AI capability (even if it was just a clod or chat, j’ai pété wrapper)

… which has resulted in unreasonable, and practically unachievable, sales and growth targets being placed on them which they will not reach, especially in a flat, or down, market for software purchases as a result of the AI price squeeze (since “AI” offerings are currently cheap with the big firms underpricing compute costs to try and hook clients, even though it’s costing those firms Billions).

But as Garry Mansell, one of the Godfathers of Modern Procurement, has so eloquently explained in his can of worms post, growth at all costs is equivalent to self-sabotage. That’s because it comes laden with fallacies, traps, and brand value destruction!

Garry points out the three biggest harms we see every single time.

  1. Quarterly Earnings Trap: with the constant pressure to reach unreasonable, if not unobtainable, sales targets, it becomes all about delivering good news on the quarterly earnings call (whether to the public or the PE firm); it all boils down to revenue and cash in the bank, and sales teams are told to hit targets by any means necessary, including, but not limited to, deal-making, over-promising, and grand assurances the solution will solve that problem without any plan to ensure it will do just that once the deal is signed; this leads to unhappy customers when the implementation will take a year (vs. the three months they expected), the expected enhancement needed to solve that problem is pushed two years down the roadmap, and the customer support is non-existent (because all the support reps were fired to fund increases in the S&M budget to try and hit the insane targets)
  2. Heavy Discounting Fallacy: because it will get “not ready” or “likely to go with a competitor” customers over the line and get the deal in the door; first of all, it doesn’t always happen (as some customers see through it and then spot the “we have the right to reprice on a quarterly basis if your user base goes up, and we get to use LinkedIn growth metrics to do so” clause where, even if you hired a dozen janitors for your new office building or 50 fleet drivers for your new private fleet who never use the system, you will be charged for them anyway); secondly, even if it does, given that the smart ones know the old adage “you get what you pay for” is true, if they didn’t pay much, they will believe it’s not worth much and not put in the hard work that’s required on their end for a successful implementation (especially since they also know you can’t afford to, and thus won’t, support them at that price); third, voices carry, word gets out you’re cutting quotes 80% to 90%, and suddenly everyone knows (or at least assumes) you’re doing massive mark-ups with the sole intent of getting whatever you can (and not what the tech, and the IP contained within, is really worth — as you’ve just devalued the IP to the floor)
  3. Shelfware is the Reputation Killer that Keeps On Killing: Good software that generates value for a valued client that uses it daily is the gift that keeps on giving because a happy client, as long as you keep your prices fair, never goes away; but shelfware is the villain that keeps on striking at your darkest hour as that unhappy client will never tire telling people how you are robbing them blind in a contract they can’t get out of for software they aren’t using …

As Garry has said repeatedly, which SI has echoed repeatedly (while giving you a simple relative corporate debt equation to help you calculate how likely that vendor is pursuing growth at all costs, and, thus, likely to screw you [whether they intend to or not]), the only true growth is controlled growth with ready-clients at a sustainable year-over-year rate that allows all customers to be served to expected levels of service, all new employees to be adequately trained before being thrust into critical customer-facing roles, and all current employees to get the regular time off they need to prevent burn-out.

And, as Garry has also pointed out, where the model incentivizes utilization and renewal over implementation and sale, where every member of the organization is incentivized on those metrics, where the sales person doesn’t get a dime of commission until go-live and where the full commission depends on adoption and renewal, that’s where you will see success. (In other words, the sales person should NOT be happy if the client isn’t. That’s one of the best ways to de-incentivize bad deals — what salesperson is going to bend over backwards and/or pull every dirty trick in the book to get a deal he’ll never see a dime of commission on?)

When you remember Kraljic, don’t forget Coase!

Coase laid the original foundations … Kraljic gave us fundamentals … and now the Busch-Lamoureux Exact Purchasing Framework is building on that to give you a guide to modern Procurement!

The Kraljic matrix is broken. A big problem, as we have regularly explained, is that you can’t compress two independent dimensions (risk and complexity) into one. A little problem is people don’t understand how to qualify the importance of a purchase. It has nothing to do with cost or volume but everything to do with the organizational impact if the product or service being purchased suddenly becomes unavailable. Similarly, it has nothing to do with how much you buy from the supplier, but how critical it is they don’t go out of business. It might be a critical component, but if there are ten other suppliers who can meet that demand for you tomorrow, the supplier is not critical to your organization.

But the biggest problem is that people regularly misunderstand the purpose of the matrix — it was a tool, and the first of it’s kind, designed to get us thinking critically about purchasing and point us in the right direction. It wasn’t the be-all and end-all. It was the first formal methodology an organization had to segment purchases and suppliers, think about them critically, and approach sourcing and supply assurance methodologically. And it was created in a time when global sourcing was more predictable (because natural disasters were a fifth of what they are today, war’s didn’t breakout overnight without warning on the whims of a mad man stuck in a macho cold war colonial mindset), risk was primarily complexity, and if you used the methodology, you probably had a success rate of 90%, which was phenomenal.

Kraljic gave us a way to structure our critical thinking and improve the profession, and all most consultants did was water it down, create a one-size-fits-none methodology, and sell it like it was the next panacea, creating a consulting snake oil from a masterpiece of thought.

A masterpiece of thought you only understand if you understand the framework in which it was built, and those were foundations laid four and a half decades earlier by Ronald Coase in his 1937 essay “The Nature of the Firm“.

The framework was that of organizing the supply management operations of a firm, where the definition of the firm was the one put forward by Coase, which is essentially that the firm was the mechanism by which transaction costs were minimized. (Otherwise, there would be no need for a firm!)

Transaction costs are the result of the price mechanism of the open market, and include:

  • the cost of the negotiation and contract
  • the cost of the individual transactions the contract covers
  • the costs associated with production

and include all of the factors (people, equipment, technology, etc.) included in these prices.

This tells us that the fundamental purpose of a firm is … PURCHASING! And the only way a firm can grow is if it can continue to PURCHASE cost effectively (because as soon as the cost of subsequent transactions and / or production exceed the market costs, the firm is dead).

However, as most firms grew, they reached a point of inefficiency (due to management overhead, process inefficiency, and/or paperwork and/or communication point overload), and growth stopped. Also, as they grew, they became more brittle and sensitive to even tiny disruptions.

Kraljic recognized this and introduced the matrix so that firms could approach their purchasing in a more structured manner that would reduce the brittleness, simplify the management, and allow for additional growth and resiliency. And it was a great start.

But simply classifying items into non-critical, leverage, bottleneck, and strategic misses they key point of the firm’s existence. And that’s to ensure that the costs related to the category are not only always lower than the market cost, but remain low as the company scales.

When you classify an item as non-critical, it becomes ignored tail spend, and we’ve seen time and time again that the average overspend in this category is at least 15% in most companies, with many products and services being bought 30% more over market price.

When you classify an item as bottleneck, you focus on assurance of supply, and don’t dive into determining whether an item is a bottleneck because it can only be supplied by a rather limited supply base or because absence would shut down a production line. (Just because only a few suppliers produce the item to your specs doesn’t mean that only a few can, there might be a few dozen that could, and would, produce it to your specs [at a higher quality at the same price] for a guaranteed mid-to-long contractual commitment.)

When you classify an item as leverage, you double down on price (and exploitation of the price mechanism), and this can often come at the expense of quality and dependability, which can result in higher costs later if warranties come into effect or you have to replace products faster than normal (which always incurs a replacement cost in manpower and opportunity that is never factored into the “we can afford 4 of these per decade vs 3” equation).

When you classify an item as strategic, you triple (or more) the amount of effort you put into the management of that item (or category), and there is a point where the excess time investment not only fails to keep to the associated contract and transaction costs below market, but leads to no additional return on cost investment.

This is because the profiles don’t take into account the separate dimensions of risk and complexity or ensure that “importance” is defined as true “impact”, or provide any mechanisms for determining the impact (or risk or complexity).

This is why you need to go back to the foundations and build up a framework that is capable of capturing what the firm really needs!

That’s what the Busch-Lamoureux framework is intending to do.

By organizing categories based on complexity, risk, and impact

  • the cost of the negotiation and contract is based on the complexity, risk, and impact — where all are low, the whole process can be automated and costs minimized
  • the cost of the individual transactions the contract covers are minimized to verification of only what is important, and humans are only involved when automation can’t do that or finds a discrepancy
  • the costs associated with production are minimized as you are selecting a supplier that meets all of the necessary requirements at the minimum cost subject to an acceptable risk factor!

Furthermore, you’re making your contracts for durations appropriate to the category such that you’re adequately accounting for complexity and risk without locking yourself into long term deals that are not beneficial to your organization!

But most important, because the categorization helps you determine how much manpower you actually need to spend on each sourcing event, contract, and transaction, your organization is much less likely to experience decreasing returns as it grows, allowing it the funds it needs to ensure Procurement is appropriately staffed and resourced with the right systems.

Coase gave us the definition of a firm (PURCHASING)! Kraljic helped us understand the fundamentals we need to consider in our modern world. Now we’re giving you a framework to apply those fundamentals in a manner that will let you scale without fear of unnecessary waste. Go forth and transact! (The market depends on it!)