Category Archives: State of Procurement

Primary ProcureTech Concern: (Gen-)AI Integration/Impact

The non-stop hype coming straight from the A.S.S.H.O.L.E. is continuing to cause market confusion and utter chaos.

Why?

Gen-AI is on the concerns list because it’s the tech-du-jour. Five years ago it was (advanced) (predictive) analytics. Ten years ago it was the fluffy magic cloud. Fifteen years ago it was SaaS. Twenty years ago it was the World Wide Web. And so on.

But not one of these technologies, all sold as the panacea that would solve all your woes, solved your problems because all of the promised capabilities were just silicon snake oil, and Gen-AI is no different. The hype cycle may be slowly coming to an end, but it will quickly be replaced by Some-BS-World-Model-Adjacent-Agentic-AGI that will be sold as the AI that finally solves all your problems but, in reality, still won’t be anything close (but, if narrowly applied in the right domains where the client has sufficient data might actually work quite well … but won’t do anything reliably in general and the failure rate will still be 80%+, which is the average tech failure rate for the last 25 years … and SI knows, because the doctor has been following tech failure for over 25 years).

Not only is Gen-AI no different than the previously over-hyped tech-du-jour offerings of the last two decades, but with a failure rate of 94%+ (McKinsey, and 95%, MIT), it’s arguably the worst yet! And, as per our predictions, it’s not going to get much better. If the failure rate gets as low as 90% this year, it will be the closest thing to a tech miracle that we can conceivably get. Like every other tech before, Gen-AI will only solve a relatively small set of problems.

Just like

  • The Web only solves remote connectivity
  • SaaS only allows solutions to be built in the cloud
  • Analytics only provides insight where you have the right, sufficient, data and the right algorithms to get useful insights
  • Gen-AI is just a next-gen probabilistic deep neural net that often does
    • better semantic processing
    • better search
    • better summarization
    • better potential pattern identification (but only if you can learn how to prompt it to do so and only if you have it trained on the right data subsets, not the entire web which is now more than half AI slop)

    but does so at the additional expense of

    • hallucinations
    • intentional falsehoods
    • thoughtless reinforcement
    • cognitive atrophy
    • etc. etc. etc.

As a result of this, as far as I’m concerned, the AI bubble can’t burst fast enough! It’s all hype, buzzwords, and hallucinatory bullcr@p. And, frankly, any (claims of) agentic AI built on it are fraudulent. (After all, we’ve already seen what happens when you let AI run your vending machine. The last thing you want is it buying for you!)

Especially when, on top of hallucinations, we have plenty of examples of:

We’ve said many times that LLMs are not helpful and ChatGPT (in particular) is not your friend, that if you have a headache you definitely shouldn’t take an aspirin or query an LLM, and that, frankly, you’d be better off with a drunken plagiarist intern because that’s the best case result from an LLM. Most are worse.

Frankly, it’s time to stop falling for the artificial intimidation, fight back against AI Slop, and remember cutting edge tech is NOT defined by the C-Suite or the incessant marketing from the A.S.S.H.O.L.E. that is targeting the C-Suite on a daily basis!

Impact Potential

Huge! Companies will continue to waste millions individually and collectively hundreds of billions on the next generation tech that, with a probability of 90%+, will generate a (huge) loss.

Major Challenges/Risks

The major challenge is not with the tech, it’s helping companies realize that they’re probably not ready for the tech. The reason that tech failure rate has averaged 80%+ over the last twenty years is that consultancies keep promoting, vendors keep selling, and companies keep buying advanced leading edge tech they are not ready for. The reality is that unless you are in the top 10% of buyers of tech, already on the latest tech, and have sufficiently mastered that tech, you are not ready for Gen-AI (which should not have left the research lab when it did and, in all honesty, should still be in the research lab since it still only works in a small number of well defined scenarios and is so bad that every year a couple of AI founders turn away from AI because of it — with Yann Lecun walking away from Meta and LLMs and reverting to world models, that can be thought of as next generation (Semantic) Web 3.0 models augmented with [deterministic and dependable] automated reasoning and, hopefully, very little dependence on hallucinatory probabilistic models [beyond what’s needed to semantically parse an input].)

The only place you should be using Gen-AI is where a non Gen-AI solution doesn’t exist, the task is well defined, and you can build a custom in-house model that works reasonably well in the majority of situations and that can be implemented with guard-rails. But that’s something you can only do if you have a high TQ (Technical Quotient) and have mastered last generation tech. Right now, you should be tripling down on E-MDMA and Advanced Analytics as this tech has improved to the point where it can allow you to optimize processes, spending, schedules, and anything else you can think of with high accuracy and low cost with basic analytics skills as so much comes pre-packaged and the visualizations and drill-downs are much more intuitive than they were a decade ago. Plus, these firms have figured out how to use multiple forms of AI to classify your data with high accuracy and minimize the work required by you to fix errors and reclassify to your preferred schemas. It’s literally drag and drop as compared to the complex rule-building that used to be required. In addition, you should be looking for the mature A-RPA (Advanced Robotic Process Automation) solutions that are highly customizeable and capable of “self-learning” such that the parameters that trigger exceptions will adjust over time based upon user acceptance or rejection of recommended actions and the platform will automatically encode new processing rules based upon the users’ actions on an exception. Much better than Artificial Iiocy that decides everything based on hallucinations.

THE FINAL WORD

If you haven’t mastered all of the tech that existed before Gen-AI, including classical machine learning AI that has been studied, optimized, and proven to work for over a decade, you’re not ready for Gen-AI, should treat it like the drug it is (as it does more damage to your cognitive abilities than many illegal drugs), and JUST SAY NO!

Primary ProcureTech Concern: Weakness & Volatility in Emerging Markets / Trade Wars

Emerging markets are your future markets, and often the source of critical raw materials.

Why?

Given that a lot of outsourcing has been redirected to these “low cost” markets over the past two to three decades, any rapid increase in volatility becomes a significant concern, especially if the markets are not strong enough to weather the storm. A major event could wipe out an entire subset of the supply base literally overnight, greatly increasing supply shortages and increasing the market complexity. Or at least make it unsustainable, such as a 145% tariff on China which is the source of over $500 Billion dollars in imports into the USA.

Impact Potential

The impact of a “low cost” market becoming unavailable, or at least unsustainable, is moderate to severe, especially if all of your outsourced eggs are in the same country basket. One lesson that some companies haven’t learned yet is that dual sourcing is not reducing risk if the two sources of supply are in the same country (or the same small geographic region — because if you have two factories located 100 miles from each other on two sides of a border, guess what, one natural disaster can wipe them both out).

If your primary source of affordable supply is wiped out overnight, it could take months to identify a new source of supply and quarters to secure the supply and get your supply chain flowing properly.

Major Challenges/Risks

Foreign Market Predictions
It’s hard to predict what’s going to happen in a foreign market that you aren’t in everyday. You can follow economist predictions, follow currency trends, try to get a grip on the trade relations between that country and your home country, and so on, but it’s not easy. If you can predict early enough, you can take action. But if an administration, without warning, decides to drop 100%+ tariffs on your source of supply, you’re in trouble.

Alternate Sources of Supply
Sometimes there’s few sources of supply for a given material, part, or product outside of a given country that has a similar total cost of acquisition, especially if you aren’t sourcing at full volume. Identifying alternate sources of supply that you can switch to quickly can be quite a challenge.

New Market Identification
If the emerging market also happens to be one of your primary emerging sales markets, the hit from volatility can be quite significant if the volatility results in rapid inflation, job loss, or both and your sales start to drop rapidly.

Final Words

Given the globalization of today’s supply chains, where a product can depend on materials and parts from dozens of countries, weakness and volatility in emerging markets is a significant concern. And we have yet another (fourth) reason you need an economist!

Primary ProcureTech Concern: Tightening Credit Conditions

The world runs on money, regardless of what form it comes in. Gold, cash, or credit. Credit is particularly important because it helps an organization bridge between cash cycles.

Why?

If economic downturns or inflationary pressures arise quickly, then credit will also tighten.

Impact Potential

If the organization, or its suppliers, needs credit to produce and distribute the goods for sale, the lack of interim credit could lead to reduced inventories and sales and even bankruptcies.

Major Challenges/Risks

Economic Market Prediction:
Predicting whether the economy is going to grow, stay flat, or recess (or depress) is the first challenge, as that’s a leading indicator of credit markets.

Credit Market Prediction:
Based on the projected economic changes, predicting the base and prime rate changes, availability of credit, and the future cost to your organization and your primary suppliers.

Alternative Credit Sources:
If your primary sources are projected to become considerably more expensive or restrict credit access, can you identify alternate sources? Moreover, how much will those cost, how long to establish the relationships, and how reliable will they be?

Alternative Credit Arrangements:
If right now you are just using loans or lines of credit, maybe you need to consider early payment discounts, invoice factoring, or alternative supply chain based credit arrangements.

Final Words

Credit conditions depend heavily on economic conditions, so this is yet another reason you need a good economist.

Primary ProcureTech Concern: Managing Digital Fragmentation / Digital Transformation

As per our previous concern on Technology Transformation, it’s time to be digital, but with digitization comes digital fragmentation, especially when you don’t fully understand what you’re doing.

Why?

Digital fragmentation increases the risk of IP/cyberattacks (which is one of your top risks) as each fragment presents its own unique weaknesses and opportunity for attack. Moreover, it explodes the tech execution support required and increases one of the largest barriers to organizational success.

Digital transformation is also a concern because organizations know we have reached the age of digitize or die, but the digitization project failure rate is at an all time high of 88%+ (and 95% if it’s AI-based for the sake of AI) and every digitization effort to date has just resulted in more digital fragmentation. (To the point that the average mid-size organization has over 600 SaaS subscriptions and some have over 1,000.)

Impact Potential

The impact potential depends upon the degree of fragmentation. How many software applications? How many different hosting platforms? How many data pools? The impact of data fragmentation can be low if there are a relatively small number of software applications, they are all AWS hosted, and there’s only one data warehouse/lake/lakehouse. Or it can be extremely high if there are 1,000 SaaS applications, they are hosted on half a dozen cloud stacks (AWS, Azure, Google, IBM, Oracle, and Salesforce), there’s a data warehouse/lake/lakehouse for each of the divisions, and so on.

Major Challenges/Risks

Cybersecurity
Every one of your SaaS applications provides an entry point into your organization if hacked. Every cloud provider provides multiple entry points if hacked. Your data warehouses provide a huge amount of data that can be used against you. These hack points are in addition to all of your internal servers / on-site applications, employee laptops and smart devices. An average organization these days is a cybersecurity nightmare and a hacker’s dream.

Data Integration
Chances are all of your applications have their own data models, own unique entity ids, and own standards for data access. Integrating your data across applications is a nightmare, forcing integration through data warehouses/lakes/lakehouses, which in turns creates a data replication and synching nightmare.

Data Maintenance
Not only is there the synching issue from the replication used to support data integration, but less used apps means there are less checks and updates for the critical data they are the master applications for, and data quickly becomes stale and out of date. And employees depending on that data and accessing it through the lake don’t know that, and can make bad buying and partnership decisions based on that.

Final Words

Managing digital fragmentation is not easy. In fact, it’s a nightmare because most organizations don’t have, and never had, Master Data Management (MDM) or a Master Data Governance (MDG) strategy.

Primary ProcureTech Concern: Economic Downturn & Deflation/Recession

There have been recessionary fears for the last two years. They are not going away, because most countries are teetering on the brink of a major recession if not a depression!

Why?

This is a significant concern because it contributes to the top risk of spend pressure because economic downturns always result in job loss and a drop in consumer spending as many consumers have to tighten the belt. And this, of course, contributes to the #1 joint risk of rising cost/spend pressure.

Impact Potential

The impact potential is dependent on how bad the downturn is, how long it lasts, and how global the downturn is. It can result in anything from a slight drop in sales (if you are in a mostly recession proof business and have one of the most affordable price points) to a massive drop in sales if you’re selling “luxury” goods to average consumers who, being unemployed, have to cut all luxury products from their purchasing.

Major Challenges/Risks

Prediction
When will the next downturn hit? How long will it last? How bad will it be. Right now the markets, and the US in particular, are defying all logic. Trade wars usually depress markets. Considerable over-inflation (which is the case in AI right now) typically leads to rapid depressions when the myth fails to become reality.

Detection
Detecting when it’s starting. When it’s not just a temporary blip, but when a downturn, whether a shorter one (of a few months) or a longer one (of a few years) is starting and when your organization should be adjusting its operations and strategy.

Planning
Just like you should have mitigation plans for significant risks, you should have mitigation plans for the downturn, which might involve shifting or changing product lines, pausing all expansion efforts, putting hiring on hold and planning for attrition (as people retire or contracts end), and even reducing operations with respect to production and/or support. It might also mean, for an international organization, shifting focus to different markets.

Final Words

The nature of today’s markets that allow rampant over investment without sufficient regulation ensures that recessions are inevitable. Your job is to predict them, and this is yet another reason you need a top economist.