Author Archives: thedoctor

How AI Enhances 10 Common Procurement Challenges Part II

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 finish them one-by-one.

Legacy Systems Complicate the Adoption of New Technology: The article claims AI streamlines integration by assessing system compatibility, automating migration, and reducing downtime. While two out of three ain’t bad, it ain’t good when the critical requirement of assessing system compatibility cannot be met by AI — since simple text matching isn’t helpful if the interface of a legacy system isn’t specified in a standard format (as otherwise it’s essentially field-name matching, which is no different than human guesswork). The reality is that humans still have to define/verify the mappings before the AI can take over.

Letting AI do the mappings is fraught with errors. And its even worse when you let it automatically connect systems, pull and push data, replicate incorrectly mapped and bad data across systems, and “fix” data that was actually correct on system integration because the “bad” data in one system is used to overwrite the good data in another system just because it appeared to be more recent. Because it’s automated, AI can propagate and exacerbate errors at an unprecedented rate and in a matter of seconds make a mess that can take months to repair.

Managing Supplier Risks is a Growing Concern: AI can continuously monitor supplier performance, predict risks, and ensure compliance. This is one situation where they were almost perfectly correct, but, when they say vendor evaluation can be time intensive and imply that AI can speed it up, they overlook the fact that evaluations still have to be done by humans and tech can’t speed that up.

Moreover, if you think you can augment your data with third party data to speed up the evaluations, you’re just fooling yourself. You just make bad decisions faster.

Manual Procurement Process Drain Resources: AI can definitely automate repetitive tasks, reduce human error, increase efficiency, and free your team to focus on strategic initiatives, but only for tasks that are well defined, typically free from exception, and capable of being processed by standard rules. However, this can’t be done until the repetitive tasks are identified, processing rules defined, standard exceptions identified, and additional rules defined. Only then can the AI automate enough to be useful.

Moreover, using a next-gen LLM with chain-of-compute to try to break the requirements of a task down into subtasks, execute those subtasks automatically, and automate a process without any human intervention is just as likely to go wrong as it is to go right.

Demand Forecasting is Often Inaccurate : AI can improve demand forecasting, but only if you have the right data — it’s not a magic box, just a black box that you need to understand.

It’s not just demand trend based on utilization / point of sale data, its also market conditions which can sharply change a demand curve overnight … traditional curve fitting / machine learning that most “AI” is based on cannot detect a change in market conditions or a political situation that can cause a rapid change in demand.

Procurement Remains Transactional Rather Than Strategic: AI DOES NOT transform procurement into a strategic function that optimizes spend, improves supplier collaboration, and aligns purchasing decisions with your business! Only people-powered Human Intelligence (HI!) can do that. Remember — transforming Procurement requires defining a strategy, defining appropriate processes, identifying the right people to transform it, and then, and only then, identifying the right technologies.

Assuming that you can slap in AI and transform a tactical function into a strategic one is worse than a pipe dream, it’s a recipe for disaster. Running fast and hard doesn’t get you any closer to the finish line if it’s not in the right direction. For more details, see the dozens of posts about AI in the archives.

Again, we’re not saying that AI is bad. Technology is neither good nor bad. But, like any technology, it has to be ready for prime time, correctly identified, correctly implemented, and correctly used — and that requires a lot of Human Intelligence (HI!) and planning, and the right processes put in place. Shoving it in and expecting a miracle is dangerous. And this is yet another article that implies you can just shove it in and get results. And you can’t. Especially if it’s the wrong technology, which can enhance your problem instead of shrinking it. That’s the problem. This article, like many others, doesn’t tell you about the dangers and downfalls and what you have to do to avoid them.

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.