How AI Enhances 10 Common Procurement Challenges Part I

A recent CIO article drew my ire because it claimed that AI Overcomes 10 Common Procurement Challenges as it oversimplified the problems and overstated the benefits of AI. Let’s take them one-by-one.

Procurement Takes Too Long, Slowing Innovation: According to the article, AI-driven platforms can generate RFPs, accelerate sourcing, automate approvals, and reduce cycle times … which is mostly true. Properly applied, AI can accelerate sourcing, reduce cycle times, and automate approvals … but not all approvals. As for RFP generation, that’s very limited — LLMs can generate RFPs with a simple prompt, but not necessarily a good RFP. The best RFPs are designed by humans (and then automation, which may or may not use AI, can pull in data from supporting documents as needed), and as for acceleration, it depends on the project — it can’t speed up supplier qualification where humans need to inspect the products and verify the requirements.

Moreover, a rush to AI can make things worse, and not better. Letting AI generate an RFP that misses a key requirement in terms of required certifications, performance criteria, production capacity, etc. can entirely invalidate an RFP process and lead to months of wasted effort if no human realizes that this key requirement was missed until an award is offered and a request for the certification, capacity, etc. is delivered and a “sorry, we don’t have / can’t do that” is returned.

Legal and Budget Complexities Create Bottlenecks: Budget tracking systems and rules-based automation allows for instantaneous budgetary approvals. Contract negotiation software can automate redlining, compliance checks, etc., but cannot handle a complex negotiation for a complex project where each side has a lot of requirements and multiple parties to satisfy. AI speeds up the technical drudgery, but not the human interaction.

Moreover, if you turn over negotiations to software, you have no idea what the end result will be. If you let it negotiate based on market data, and the cost data is off, you could be committing to a bad deal. If you let it predict timeframes based on how it expects prices to rise/stay high, but it’s off by two years, it could lock you into a three year deal when you only need a one year deal. And so on.

CIOs Need to Upskill Their Teams in AI and Cybersecurity: Just because “AI” can simplify processes with guided intelligence, that doesn’t mean the team is upskilled in the process. The reality is, there is no incentive for users to learn anything if they think the system will guide them in everything they need to do.

Thus, if you over invest in AI, especially the kind that guides users in every task they have to do, and works quite well on the basic tasks they have to do daily, and doesn’t screw up the first half dozen or so moderately complex tasks, the user will believe the system is almost flawless, start to trust it implicitly, stop questioning it as time goes on, start believing there is no need to learn anything else because the system knows it, and, over time, stop thinking. And then, instead of performance improving, it will decline … and that decline might be accompanied by a major financial loss if a bad contract is signed or major risk ignored.

Data Inaccuracy Leads to Poor Procurement Decisions: While it’s true that over three quarters of organizations struggle with unreliable data, AI doesn’t magically fix the problem. It can help with cleansing, validation, and procurement trend analysis, but ask any spend analysis vendor who has tried to apply an LLM to unclassified vendors about the classification accuracy (which tends to top out around 70%) — good data still requires manual cleansing and classification, especially where the system reports good confidence. It can definitely help, but it doesn’t take the onus off of the human.

In other words, if you believe that you can plug in a magic AI black box ad that it will fix your data, you are gravely mistaken. Sure it will tell you that it has cleansed, classified, and validated all of your data, but if it’s only 70% accurate, it’s only made matters worse if you trust the data 100% and don’t know what 30% is inaccurate. When you base your decisions on data, and the data is bad, you are bound to make a bad decision. The question is, how bad. You don’t know. And that’s a big problem!

B2B Software Selection is Increasingly Complex: Moreover, despite the claims, AI-powered vendor analysis doesn’t really help that much — see Pierre Mitchell’s crazy conversations with DeepSeek-Rq. Note how it not only recommends inappropriate vendors, but also recommends vendors that don’t even exist anymore … it can help you discover potential vendors, but you still need human reviews and deep pricing intelligence (from expert SaaS optimizers).

Trusting AI to select your software is worse than trusting an analyst firm map! And we know all of the problems those maps contain. (First of all, they only mention the same 10 to 20 vendors year after year, ignoring the dozens of other vendors that might be more appropriate for you.) AI cannot understand your needs, cannot truly map needs to requirements, cannot truly map requirements to features, and cannot truly assess how relevant a solution is, and definitely can’t assess how well a provider’s culture will match yours.

Come back Thursday for Part II!

We Finally Know the Source of the AI Buzzword Bullsh!t!

The Agentic Software Service Hyper Optimized Learning Engine custom built for drowning the World Wide Web in soundbite and buzzword marketing bullsh!t centered on AI, or the A.S.S.H.O.L.E. for short! (With fervent thanks to the esteemed Arthur Mesher for delving deep into the depths to uncover the source of this madness!)

Technology Project Failure is at an all-time high, boosted by the recent AI failure rates (which are on the rise as almost half of AI initiatives are being scrapped in process, see CIO Dive), and while the hype should be subsiding (and shifting to the next hype cycle), it’s now hitting us harder and faster in what should be its death throes than any hype cycle that has come before.

The AI marketing onslaught is coming so hard and fast that it’s impossible to imagine how so much new soundbite, buzzword, FOMO, and FUD content can be produced so fast and so overwhelming to the point that it seems humanly impossible. And that’s because it is. It’s not coming from humans, it’s coming from the A.S.S.H.O.L.E.. As we have indicated in our previous posts on Gen-AI LLMs, one of the valid uses for Gen-AI is mass content digestion, search, summarization, and generation.

It appears that one of these systems was customized to ingest all of the initial human-generated AI BS and trained to spew out marketing soundbites, social media posts, articles, and other forms of web content ad nauseum and to continually ingest new content on the subject to create even more content, including AI-generated BS content from other AI systems that tried to copy the original A.S.S.H.O.L.E..

And even though it doesn’t matter, since apparently every LLM can be trained to emulate the original, the only question that remains is, who currently owns the source engine, what LLM was it originally built on, and what LLM is it running on now? This is obviously the industry’s best kept secret. I hope someone who has gotten to the bottom of this will let us know the full story of the A.S.S.H.O.L.E.. Considering the intellectual and financial pain and suffering it has caused, we deserve to know the truth!

For those interested, since I’m sure LinkedIn will disappear Art’s post if it hasn’t already, here’s the original. (And the Gartner rant ain’t half bad either!)

What Are the Biggest Organizational Cost Saving Levers?

Every year there is a new survey or research report that will name one to three levers as the biggest cost savings levers in an organization, but it’s really not that simple. For example, the SCMR last year reported on a BCG study and the Hackett Group 2024 Procurement Key Issues Report and said, in Managing Procurement in a Price-Sensitive Environment, that:

  • supply chain costs and
  • manufacturing costs

are the biggest levers for cost savings. And while generally true if more than 50% of revenue is being spent outside the global organization’s many four-wall structures, it’s not true if most of the spend is internal (on headcount, property, etc.).

And it’s not true at all in the current environment in America where now tariffs are increasing costs by up to 145% (and there’s no solution, beyond BTCHaaS) and everything is unpredictable.

Moreover, supply chain is generic — is the cost inefficiency in the manufacturer (and if so, is it in their material and component supply chain or in their operation), the distributor, the logistics partners, or the organizational warehousing and inventory management. And if its manufacturing costs, is the bulk of the costs raw materials governed by commodity markets or in the production process? If the former, you can’t do much. If the latter, the assembly line is your oyster.

And then, even if you find the lever, where is it located? Who has access? Do they have the strength and permission to pull it? It’s tough!

Let’s look across the spend (ignoring tariffs because they are beyond your control):

  • products: low quantity, no lever; high quantity, sourcing if the market conditions are in your favour (or about to not be in your favour, so you lock a contract in early for a small hit); if the product was never sourced before, it’s tail spend which typically sees 15% to 30% overpsend
  • services: low quantity, tiny lever; high quantity, across a nation or the globe, if you take a multi-level view, are willing to work with multiple providers, and apply SSDO (Strategic Sourcing Decision Optimization), 30% to 40% can be shaved off with no detriment in service level
  • logistics: mode matters; intermediate storage matters; FTZs matter; source and sinks matter (if you’re selling in multiple countries, you might want to consider producing from multiple countries); easy to take 10% off just with a better network design, sometimes 20% off with a better network design, smarter load distribution across carriers, more cross-docking (and less intermediate storage), and the most appropriate (mixed-modal) transport plan
  • taxes and tariffs: source and sink matters! and, in some countries, so does minority/diversity/etc.; you can cut these in half (or even eliminate them) with better planning; when tariffs can be 20% or more, this matters
  • warehousing: major cities and hubs are expensive, secondary locations can be a fraction of the cost; and if smartly located, can cut your “local” distribution costs to your “local” stores, plants, offices, and/or customers; for years all the studies said inventory cost can be as high as 25% of product cost; better management (not just JIT, that can lead to more stock-outs and losses than a few extra percentage points) can halve this while reducing stock-out rates
  • facilities: if you’re willing to consider a balance between on-site and remote, shared spaces (and designated lockers), locale of choice, costs (and savings) can vary wildly; millions can be saved here in larger companies;
  • personnel: you pay the best people the best rates and you keep them as the best deliver an ROI multiple that is many times an average Joe; but that doesn’t mean you have to overpay for benefits (and with good negotiation, you can get great benefit plans at below market average rates); this can be hundreds of thousands to tens of millions

There are many levers, and the savings potential differs by industry, company size, organizational Procurement maturity, and individual company.

In other words, don’t just look at the top two or three levers, look at all of them and focus on the ones with the most potential, even if they are on the bottom of the “expert lists”.

Financial Business Risk Prioritizes Supply Chain Vulnerabilities …

… but it does not identify those vulnerabilities, although it can tell you where to start looking. So while an article in the SCMR last year provided a good overview on how to evaluate, and quantify, supplier risk, the title was misleading when it said they were calculating business risk to identify supply chain vulnerabilities.

The article, which described an approach by the authors to find a way to improve the evaluation of risk impact on a business, culminated in four main findings. The approach, which looked at the total financial impact a supplier failure would have, yielded two findings that we’ve known for over a decade, ever since Resilinc pioneered the approach of assessing the financial risk associated with a supplier failure (based on mapping where all of their parts are used and which of those are single source)

  • procurement spend with a supplier is NOT correlated with the financial risk of a supplier
  • part standardization can increase business risk impact

As well as two insights that are rather new:

  • procurement spend is not correlated with the revenue of the company (the Resilinc model could have shown this, but they did not focus on this or collect those metrics last time SI was made aware of their methodology)
  • true high-risk impact suppliers are a substantially smaller amount of spend than an organization might think; in the authors’ study, they represented only 28% of total spend (whereas most companies will highlight the high spend suppliers as high risk and identify the suppliers that represent almost 3 quarters of spend, or 73% in this study)

The reason for this is that they linked all of the organization’s data sources that contained information related to the BoM for each SKU, the revenue for each SKU, and the suppliers for each BOM. By creating a network of connections between components, products, and suppliers, and identifying single source parts, the link between the criticality of a supplier and the revenue became clear. Consider the supplier who supplies that custom control chip for the fuel injection management, cruise control, or even for the monitoring of the tire pressure. If they were to fail, the absence of a single, $10, custom control chip can bring down a multi-million dollar production line, and close down an entire production plant, as the recent semiconductor shortage did to many plants during COVID. Given that these were being put into $10,000 to $100,000 cars, these suppliers would never have blipped on a spend-based risk assessment. And this is just one example.

But it is an example that demonstrates the blind spots companies have with respect to small and specialized suppliers that aren’t in the top 80% of spend but yet supply sole-sourced and/or custom parts or products. This means that when doing a risk assessment, it’s not just risky suppliers or risky supply chains that need to be assessed, it’s any supplier that supplies something that isn’t easily replaced by another source should something happen to the current supplier. The risk could be low that they will fail, and lower still that you couldn’t quickly modify a design to use an alternative, but you don’t know until you assess. And that assessment must be revenue and criticality based, not spend based. Spending $100M with a steel supplier to acquire the raw material for a frame assembly makes the supplier strategic, but doesn’t make using that supplier super risky when all their competitors offer the same grades of steel. But if you need a custom chip for that car, power transformer, etc., and you currently only have one supplier to supply it, then that supplier, no matter how stable and how low-risk its profile looks, is a risk even if it only gets one hundredth of the spend. And you need to determine if it has any vulnerabilities and, if so, monitor them so you won’t be surprised by a sudden failure.

The Lack of Adoption of Analytics is NOT Complicated!

According to THE PROPHET, the reason that we’ve never seen a breakout $100M+ pure-play (spend) analytics vendor is it’s complicated. (Source: LinkedIn)

But the reality is that it’s really not.

First of all, approximately one third of all multi-nationals are headquartered in the US. In other words, one third of global enterprise is based out of the US, where the strategic decisions are made. Let’s say that again, one third!

Secondly, and this is the real explanation, in our age of participation trophies and only focusing on the positive (when there really isn’t any), no one is willing to state the truth, and that is most of the employees responsible for strategic [spend] analysis are just too math stupid.

Analytics, at its core, requires good mathematics skills and, with traditional analytics applications, good computer skills.

However, the US, where many multi-nationals are based, consistently ranks in the lower part of the OECD international rankings and is currently 34th in the PISA [out of 79 scored countries] (with an average numeracy score of 249, below the TOTAL OECD average of 263, with over 1/3 of its adult population at level 1! This means they can’t even do basic arithmetic and problem solving [or calculate a tip FFS, but that does explain why they believed their administration when they lied and said other countries pay the tariffs] — and that’s the average business employee in the US, since anyone with a level 2 on the OECD can likely fake it in a STEM career in the US.

As for THE PROPHET‘s reasons as to why Spend Analysis has consistently underperformed the hype:

  • While 3/4 of solutions have always been reporting in drag, I’ve been highlighting at least a dozen Best of Breed solutions consistently for the past decade. They have existed for the past 20 years, you just had to look (and understand what to look for. But this site did a great job of helping you with that!)
  • Yes, scale came at the cost of dumbing down the UX (for the US market in particular)!
  • Unfortunately there is no faster way to die as a Spend Analysis vendor then to get scooped up by a (mega) suite or a Big X Comsultancy.
  • Actually, the analytics and optimization is not powerful or complex enough in most solutions. Again, the problem is that the vendor didn’t add incremental levels of simplification (i.e. dumbing down) so each user could take advantage of it at their mathematical (in)competency level.

But the real reason, as hinted above, is that employees resisted these advanced spend analytics solutions because they knew they didn’t have the mathematical skills to use them. (Which the US Education System should be blamed for [and why it should be fixed, not dismantled], not the employees, unless those employees went to University and chose not to take math courses to try and make up for the failings of the public education system they were subjected to.)

As for THE PROPHET‘s signals that the times they are a changin’:

  1. Good + Cheap = Dangerous
    Faster? Check! Cheaper? Check! Smarter? Well … Ask Woody!
  2. Analytics is Merging with Execution
    This is key for adoption of analytics — do it when you need it and apply the findings right away.
  3. Intake, Orchestration and Agentic Tech
    I guess I have to say it again!
    ????? ????????????? ?? ???????? ??? ??? ??????? ????!
    When what we really need is a Revenge of the Nerds! (If the USA even has any left!)

However, the real reason that we may finally be entering a new era in analytics is the following:

4. Most companies are trying to stave off bankruptcies as a result of US trade, market, etc. decisions that have already bankrupted many SMEs and they now realize that analytics is a key part of that solution. You can’t optimize spend you don’t understand, or understand the impact of a sudden 145% increase in tariffs if you don’t understand how much you are sourcing from the country in question.