Category Archives: Technology

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!

Why Are You Still Buying That Fancy New Piece of Software That

  • Could Get You Sued?
  • Increases The Chance You Will Be Hacked!
  • Could Result in a 100 Million Processing Error?
  • Could Shut Down Your Organization’s Systems for Days!
  • Helps Your Employees Commit Fraud?

If someone told you this when evaluating a piece of software, and asked if you wanted to buy it, I’m sure the vast majority of you would say HELL NO!

In which case I want you to please tell me, why are you all still riding the AI Hype Train, Buying, and Using Gen-AI everywhere?

It has already resulted in lawsuits and losses!
The Air Canada lawsuit over the Gen-AI chatbot is just one notable well publicized example.

AI systems are AI coded, and AI code has a much greater security risk
as it generates code using training repositories that contain large amounts of untested, unverified, and high risk code — generating code so full of security holes it’s a hacker’s dream! (See this great piece on the ACM on The Drunken Plagiarists.)

AI systems negotiate on the data they have
and with a single decimal point error and you could be paying 10X what you need to. Not to mention, they don’t always translate right. Remember, the Experimental AI DOGE used claimed an 8 Billion savings on an 8 Million contract!

Bad data generated by an AI system and fed into a legacy system with poor data validity checks can shut it down.
Plus, Gen-AI can also push out bad updates faster than any human can and you can easily have your own Crosslake situation!

Now it’s being used by employees to generate fake receipts
that look so real that, if the employee does a few seconds of research (to get the restaurant info, current menu prices, tax code, etc.), you can’t distinguish the generated image from the real thing. And, before you say “Ramp solves this”, well, it only does if the employee is lazy (which, let’s face it, is human nature, so you’ll catch about 90% of it). But what happens when a user strips the meta data which, FYI, can be as easy as taking a picture of the picture … oops! (And if you’re a hacker, running it through a meta data stripper/replacement routine is even easier as you’re just hotkeying a background task.)

AI is good. Gen-AI has its [limited] uses. But unrestricted and unhinged mass adoption of untested, unverified AI for inappropriate uses is bad. So why do you keep doing it?

Especially since it’s now proven it’s worse for you than some illegal drugs! (Source)

Everyone In the Procurement Ecosystem Exists For a Reason — But Do You Know Why …

… and more importantly, when you should use them?

Joël Collin-Demers recently commented on a LinkedIn post that

Everyone in the ecosystem exists for a reason. Big consulting and analyst firms are great tools for organizations in particular contexts (e.g. a big firm is a great way to get a lot of smart people deployed on a problem quickly).

The point I’m trying to make is that we tend to over-rely on big consulting and analysts.

And he was correct. Big X consultancies, niche consultancies, implementors/integrators, analyst firms, suite vendors, best of breed vendors, etc. were all started for a reason and continue to exist for a reason. Understanding both of these helps you determine when you should use them, why, and what you should (and should not) expect. In this post on where we asked If You Really Want Success … or Just Say You Do, we made it abundantly clear that Analyst Firms, Big X, Implementors, and even Vendors (beyond a certain point) ultimately don’t care about your success because

Big Analyst Firms (that produce the pay-to-play maps) make money pushing the solutions of the vendors that pay them the most, not on making sure those solutions solve your problems. While there was a time you could always count on the best unbiased advice from an analyst firm, that was long ago. Ever since the first big vendor realized it was faster (and cheaper) to buy influence by sponsoring reports or cutting big research access POs, the end of unbiased recommendations began. (And it’s more your fault than the vendor’s because you came to expect free reports, but no one can work for free, which means the vendors had to pick up the entire cost, which means those reports say what the vendors sponsoring them want said, not what you need to hear.)

Big X need to keep their benches employed addressing your problems, and if a vendor’s solution took care of everything, what else would they do? This doesn’t mean they are going to screw you, but it does mean they are only going to address what you ask them to, that they are going to try to do it with a diamond/platinum/sycophant partner to keep their top-tier consultancy status, and assign the weakest resources they think they can get away with to keep their top tier resources free to top paying clients. Moreover, as we discussed in our article on When Should You Use Big X, the vast majority of Big X did not start out as IT consultancies or Procurement Tech shops and this is still their weakest area (as the “wild west” tech players and boutique consultancies get the majority of best talent), so even if they are doing their best, it’s only so good. (Compared to their core strengths, which, as we said in the latter post, you’d be foolish NOT to take advantage of.) The reality is, many Big X are now mostly body shops who have to keep those junior consultants employed while keeping their big software partners happy. And that’s a difficult balancing act, especially considering their overheads and the luxurious lifestyle these partners have grown accustomed to.

Implementors make money implementing solutions — if that solution solved everything for the next five to ten years, how would they keep their bench employed as well. Now, they are going to make sure it’s implemented to the best of their ability, but since they weren’t hired as a consultancy, they aren’t going to be the ones to tell you when a solution is not the best for a certain task — they are going to do what they are paid to do (so that, when you realize you need another solution in a year and then use the same Big X again to recommend it, they get that contract too).

Vendors need to keep their investors happy, which means securing sales as fast as possible, not ensuring they are the perfect fit and/or outlining where they will fall short. Now, of everyone in the ecosystem, they definitely want you to succeed, but the reality is, they can only spend so much time on you because they took too much money from investors at too high a multiple, aren’t growing at the expected rate, and the management and sales team risk being fired (and the entire company being shut down) if they don’t continually increase the rate they bring on new customers (whether they can reasonably support them or not). It’s all about “what their solution can do for you” and not about “is their solution right for you”.

And so on.

Niche Consultancies are the best IF they do not have preferred vendor partnerships (which require a certain level of business to maintain) as they know they have to perform to get their next contract, but these are few and far between. And even though it is critically important, almost no one does Project Assurance for their ProcureTech project (and then wonders why we have had Two and a Half Decades of Project Failure).

Short story, everyone in the ecosystem exists to make money off of YOU. While that’s not a bad thing IF they provide value (and heck, I’ll happily give you a dollar if I am guaranteed two dollars in return in a reasonable time frame), not all of them do … and those that do are not equal in the value they provide (primarily due to conflicting pressures, not intent). Until you understand that, your returns will be limited.

The important thing to remember is that if you’re just starting your best-in-class Procurement journey, you typically don’t need an end to end suite, and if you’re Procurement maturity is still elementary school, you don’t need a 7-figure mega suite when a low 6-figure mini suite, which can be implemented in 1/4 to 1/6 the time, can get you 80% of potential savings. Especially when this level of savings will take you 3-6 years to realize. Then, when you’re ready (and know how to get the additional ROI the mega-suite can provide), you can upgrade to the seven figure mega-suite in confidence you’ll achieve the same level of ROI. (Instead of being the next ProcureTech disaster. And while you may believe in a beautiful disaster, there is no such thing where tech is involved.)

Follow the Money to Find Future Opportunity — Which Will NOT Be Fully Found With Autonomous Sourcing!

Spend Matters has thrown caution to the wind and followed Gartner’s lead jumping onto the AI Hype Bus (with no steering and no brakes) that is still heading straight for the cliff and are wheeling out webinars on AI faster than a prairie fire with a tailwind. (Needless to say Sourcing Innovation does not think this is a good thing. There are valid uses for AI and automated processing, but fully handing over financial decisions is like wheeling in the Trojan Horse and leaving it unguarded in the server room with unrestricted access to your bank integration.)

Recently, The Maverick advertised yet another Spend Matters webinar on Autonomous and AI Sourcing where he said we should “follow the money”. Which we should, but there are a few things we need to clarify first.

1. No Money Changes Hands In Sourcing

It changes hands in Procurement … and it’s because most companies don’t follow the money after the contract is signed that 30 to 40 cents of negotiated savings never materialize in many companies, which The Maverick should remember from his AMR and Hackett days, as it was laid clear in Mickey North Rizza‘s famous 2009 “Reaching Sourcing Excellence” series, which we know is in his archives.

2. “Speed” is NOT a strategic edge if you don’t get it right!

If you don’t go out with the right strategy, don’t know the current market price, don’t know the reason for the current market price, and don’t have the knowledge to project if the trend is going to continue, stabilize or reverse, going to market is not a good decision … and it’s an even worse decision to automate the sourcing project and secure an award as fast as possible if you don’t know if it’s the best you could have done or the worst you could have done.

3. “Pecunia non olet”, but yet these vendors are asking you to treat it like it does!

They want you to automate spend analysis, sourcing, contracts, purchases, and everything else that involves money by turning over everything to their Agentric AI because, apparently, money stinks and you don’t want to touch it. (But they are quite happy to not only spend yours for you but takes as much of it as they can for their services.)

But here’s what they don’t tell you.

  • AI is NOT Intelligent.
    The level of intelligence in their “AI” is equivalent to the level of intelligence in a carpenter’s hammer. The level of effectiveness is entirely dependent on how skilled the person “training” the system and how skilled the person “using” the system is, just like the effectiveness of a hammer is dependent on how well the carpenter was trained and how experienced he is in it’s use.
  • AI Does Not Know What it Does Not Know.
    If the data is incomplete, the recommendation is very likely incorrect.
  • AI Cannot Do Better than the Best A Human Has Ever Done in Decision Making.
    So, if none of the situations it was trained on led to great results, neither will what it recommends for you.

You need to remember how Gen-AI does its work (or should we say does not work). It is large document search and summarization and chain of compute. Now, the more advanced players are trying to embed knowledge graphs into this, but these are not perfect either. With good training examples, and a very similar situation, the probability it will work well is very good, but it’s still only a probability. As a result, nothing should ever be fully automated where money is concerned. The tools should be used for their recommendations, and if the recommendations are good, and the risk is low, most of the tactical data processing and event management should be automated, but the decisions should ALWAYS be made by a human, who should be involved at every decision point. Even if that decision is verifying the system recommendation. It only takes one miscalculation due to an incomplete data source to project a wrong trend, rush an auction, lock in a price 3X what you are paying now, only for it to fall in a month later when a factory (which went offline temporarily due to a manmade or natural disaster) comes back online and the supply-demand balance returns to normal. And while you may have stocked out for two weeks, those losses will be orders of magnitude less than paying 3X at a contract you have to honour (unless you want to get dragged into court).

Now, if you really want to make money, forget all this Autonomous and Agentric AI BS, look for Augmented Intelligence solutions that make your staff two, three, five, and even ten times more efficient, purchase those, and, remembering that the US infrastructure is crumbling fast (and not going to get renewed under a Republican administration that is more interest in trickle-on economic tax cuts for its billionaires than ensuring you have running water), it’s time to remember how the smart made money in ancient Rome — public bathhouses and latrines. Time to invest in your own desalination facilities and be ready when the public wells run dry. After all, “Pecunia Non Olet“.