Category Archives: Market Intelligence

How Does a Vendor Build a GOOD Solution?

Two posts ago on the top final procurement concern of today (and the last five and the next three years) we told you that Gen-AI, which is (still) the tech-du-jour, is not really any different than every other tech-du-jour that we’ve had over the last two decades and, like all these preceding technologies (that were all over-hyped), it is not the panacea that will solve all your problems (despite claims to the contrary) and is, in fact, simply the latest incarnation of silicon snake oil.

Then, in our last post, we asked, and answered, why most (new) vendors are building on it. There are a host of reasons — which include greed, low TQ, hype, and cluelessness — and none of them are good. That’s why, as we stated, most (AI-first) start-ups today SUCK, and, to be honest, why most start-ups in our space suck in general (and do for at least the first few years of their existence, even if they aren’t AI first).

But we also told you that we’d tell you how a vendor can build a good solution, starting with V1. Just like selecting a solution that actually works is possible 80%+ of the time (if you follow the right method that we outlined in our series on Successful Vendor Selection Series, because, otherwise, your chances of success are about 12%), there are best practices that will maximize your chances of success. But like solution selection, don’t expect any of the big analyst or consult firms (that depend on never ending hourly support contracts) to give you any real advice! (They are all instances of The Vendor in Black … Comes Back!)

1A. Get Relevant Procurement Experience and Insight
By this I mean that if you’ve only worked for one or two companies and only done things one or two ways, you don’t really understand what Procurement needs generally — you only understand what your companies needed and what very similar companies in your niche industries need. With limited experience at one or two companies, you’re not building the perfect solution for the industry, you’re building the perfect solution for YOU, and YOU may not represent the majority of the market!

You don’t have this in your late 20s, or even your 30s. You have this in your 40s. (And then to run a successful startup, you need management experience — that’s why they’re saying 50 is the new 30 for startups … by then you truly understand what is needed and likely have the management experience to pull it off.) Any earlier/younger than this, and you better engage some real independent Procurement experts to help you define what you really need to do to address entire verticals or wide swaths of the market.

1B. Get Relevant SaaS Development Experience
You also need real SaaS Development Experience. The ability to vibe code, the ability to use low-code / no-code solutions, and even the ability to write web script DOES NOT COUNT! Script kiddies don’t build enterprise apps — the dot com boom and bust (which some of us remember — and the rest of you need to study because the Gen-AI bust could be as bad) made this clear. You need real, educated, experienced developers and architects who have worked in real tech companies building, deploying, and actually delivering enterprise apps! These are the only resources who build enterprise apps.

Now, it’s very, very unlikely you have both. That’s okay. That’s why you get the perfect partner that compliments you so that you collectively possess CPO (Chief Product Officer) vision and CTO capability from day one. Then, if the Procurement Expert founder is not a CEO, the two founders seek a third founder who is a real CEO with relevant C-Suite domain experience, and if the Procurement Expert founder is a CEO, the two founders seek a real domain expert who has product management experience who can be the CPO.

2. Define the problem you want to solve in detail!
What is the real pain point? What does the solution look like? How do you measure it? How do you get there?

Once you’ve answered the key questions and fully defined the problem, define the process that solves it. Then define the variations to the process. I.E. What are the core, required, steps. What are additional optional steps. Where might approvals or sub-processes be required in specific situations.

Then define what can be automated, what needs to be done by a human, and where there are multiple options.

Only once you fully understand the process and variation across companies of different sizes, categories of different complexity, and departments of different maturity in the verticals you are going for can you attempt to build a platform that will support it.

3. Identify the minimally appropriate and best-match algorithms for each process step and the best tech for stringing the algorithms together.

Some steps will just be collecting information on a form, validating the response type with regular expressions, and validating the data with third party integrations … and possibly require a(nother) user to accept it. Other steps will just be running pre-defined analytics and suggesting or taking an action based on the result, possibly using a rules-based multi-select with adjustable parameters. Others will be RPA auto-execute based on previous steps. Others still will be machine learning based on collected inputs from previous steps. And so on. (Very rarely will you need advanced AI and rarer still will you need [anything close to] Gen-AI. This is another reason AI-first is so wrong!)

When you go through this process, you will find that not only do most steps not require any (Gen-)AI at all, but most are better served without AI. You’ll find it only fits in the few situations it is good at (natural language processing, large document search and summarization, potential pattern identification, etc. for Gen-AI), and that if you apply it, you should do so narrowly, with custom trained models with guardrails and, if possible, have users accept recommendations to modify rules to reduce dependence over time.

4. Remember that good enterprise solutions have MDM (Master Data Management), Workflow, and Orchestration at the core.

These are not after thoughts. In addition, if you plan to support global users or sell your solution globally, multi-language support and internationalization MUST be at the core as well.

5. Select a programming language and an enterprise stack that supports ALL of the requirements identified above.

Not the stack that is cool, the stack that makes it super simple to get MVPs out the door, the stack used by your favourite AI platform, the stack recommended by your favourite cloud provider, but the stack that will work for the enterprise application you want to build. Then select the cloud provider — most of them are pretty competitive, and most of them support the majority of enterprise stacks, especially if they are not Microsoft (which wants a .Net/C# Azure Friendly Stack).

6. Plan out three years of major features.
These major features will support additional process extensions and related processes as there’s no significant shelf life for a niche app that only does one thing unless that one thing is so complex that almost no other application does it and the cost of building such an app from scratch by a new startup is prohibitive (especially relative to the untapped market potential).

Too many startups define the MVP, race to build the MVP, and then try to figure out what comes next. This is equivalent to shooting yourself in both feet with your brand new shotgun.

1) While you’re trying to figure out what to do next, your competitors are already building it.

2) By failing to define where you are going, you’re taking shortcuts and building the foundations for a dinky niche SaaS app versus a full-fledged enterprise application. The way I like to explain this to non-technical folk is that if you’re designing to MVP, you’re building the foundation for a two-story house and that means all you can ever build on that foundation is a two-story house. When you’re thinking three years ahead, you’re building the foundation for a multi-story apartment complex, building the first floor, and just pausing before you build the second floor. (And so on.)

In the first case, once you figure out what comes next, you realize you don’t have the right architecture or infrastructure, and then have to stop and rebuild the core, slowing down your advancement and future releases even more unless you can miraculously define the minimal API to the core you will be rebuilding up front, simultaneously build the new features perfectly to that API while trying to re-architect the core, and somehow fully achieve that API and don’t have to change it significantly during implementation when you find out it just won’t support the required workflow or orchestration … which it inevitably won’t, and then you need to update the API, and then this necessitates a rewrite of the business logic layer (and even UX) on the fly, which not only results in wasted time but wasted development because you tried building multiple levels of a house of cards all at once. A few extra months of research and planning up front will save you years!

7. Get a couple of beta customers by the time you hit beta on the MVP.
You need to verify all the assumptions YOU made in the design and implementation with a real customer (that wasn’t one of the companies you came from), test the usability, and see how real Procurement departments work (that weren’t the one or two you had experience with). You might find you have a lot more work to do before release than you thought, but it’s better, and easier, to do this before you sell it to enterprise customers as a ready-to-use enterprise product than after!

In other words, it’s not just designing an MVP on a napkin, vibe coding your way to implementation, giving a flashy demo, and delivering on a major cloud platform. (Which is what a lot of startups are doing, and that’s why so many SUCK.) It’s deep thought from day one over months and months, if not a year or two (if you are trying to do something significantly complex). But then it’s a real solution that will be relevant for years (and years) if done right (and continuously improved, appropriately maintained, and always priced appropriately).

And yes, you can argue that more steps, or at least a deeper refinement of the above steps, are needed, but these are the absolutely critical steps and many of the ones that often skipped — which results in poor solutions and sometimes complete startup failure!

Why Do Most Vendor Solutions SUCK (For You) And Why Are Most Overpriced?

In our last post on the final top procurement concern of today (well, to be more exact, much of the last five and possibly the next three to five years), we told you Gen-AI, which is (still) the tech-du-jour, is in many ways no different than every other tech-du-jour we’ve had over the last two decades (Advanced Predictive Analytics, Fluffy Magic Cloud, SaaS, World Wide Web) in that, like all these technologies before, is being presented as a panacea that will solve all your woes while being nothing more than the latest instantiation of silicon snake oil, with the only exception being that its failure rate is higher and its much more dangerous (and even deadly) when wrongly applied.

Unless you’re in the top 10% of technologically proficient Procurement/Supply Chain departments, have, and have mastered, the last generation of tech, you shouldn’t even be looking at it. And even if you are, you should be identifying constrained use cases (where you have nothing else) where you can build, and train, your own custom models and install it with guardrails for the inevitable hallucinations (blackmail, and even murder threats).

So if it’s so bad, why are most (new) vendors building on it? A host of reasons, and none of them good.

GREED: they want to build something quick, sell quicker (on the hype), and exit within 3 to 7 years (through PE acquisition or public offering); they are NOT in it for the long haul and not a company you should be looking at

TQ: more specifically, lack of technical knowledge; they see the hype, they see the ability to rapidly build offerings, they see that the solution works okay in the very small set of hypothetical test cases they train and test it on, and see that if they focus on something specific, they can probably build something without a lot of effort or skill

HYPE: Open-AI, Meta, Microsoft, etc are spending so much hyping the tech, and without a lot of counter-hype (or studies showing the dismal success rates, with the first two significant studies from organizations with clout only appearing late 2025), they want to build on this hype and marketing to sell their solutions (often by integrating with or building on the flawed solutions from the big vendors)

CLUELESSNESS: As I have said before, many founders not only have limited technical competence, but limited market knowledge and even Procurement knowledge. They’ve only worked for two or three companies, which had outdated Source-to-Pay solutions (if any), and are only aware of a handful of solutions. I.E. they looked at the Gartner or Forrester Map (which, as we know, haven’t changed in a decade and only contain decades old suites), did a Google search, looked at the website of the first three results that came back, and decided that there was NOTHING at all that even partially solved the problem they identified at their two or three jobs and only they could build it … even though, as we have shown, there are dozens (to over a hundred) solution for every major function in Procurement and Source to Pay and if none solve the problem fully, quite a few likely come quite close! (Like orchestration.)

That’s why most (AI-first) start-ups today SUCK. There’s a right way to build a solution, and, as you can guess, it’s NONE OF THE ABOVE!

If You Think You’re Ready for AI, You’re Not Ready for AI!

All of the Big Analyst Firms, Consultancies, and Vendors are telling you that you need AI, that it’s the only technology that’s going to allow you to get with the digital times, and that everyone else is using it, so you should too.

But the reality is that you probably don’t need AI, it’s not the only technology that can bring you up to date in the digital age, and while many people are using it, 94%/95% are FAILING.

The only hope you have to succeed is to be brutally honest, to ADMIT what you don’t know, that you’re only chasing AI because of FOMO and FUD, and that real progress has always been methodical and one step at a time.

More specifically, from where you are starting, not from where the market pretends you are.

The only organizations that have been successful at AI are those that:

  • honestly assess where they are today
  • determines their readiness for change
  • identifies the most time consuming processes they are willing to change
  • identifies the appropriate automation one process at a time, which is often just simple workflows/RPAs/built-in automations in existing platforms and other times ML/ARPA
  • monitors and tweaks them until they run smoothly and reliably
  • uses modern meta-workfows/ARPA/AI to connect the individual automations together where, and only where, it makes sense
  • only slaps guard-railed semantic tech / focussed SLMs on top to provide a natural language interface that processes inputs and outputs fixed action requests where appropriate

Successful companies don’t go all in an unproven tech, don’t try to do big bang projects (as that only results in big failures and sometimes the greatest supply chain disasters of all time), and definitely didn’t take the advice of the BIG X that promoted multi-year modernization mega-projects with no successes that they can point to.

In other words, the only companies that have succeeded with AI (the 5% to 6% depending on if you would rather go with McKinsey or MIT) are those that learned from the mega-ERP disasters of yesteryear and did a sequence of successive mini-projects that each built on the lass and slowly ramped to mega success.

In other words, they understand that you have to crawl before you can walk and walk before you can run. And if you can’t even crawl, you’re not ready to try and run at the Olympics, which is the level of tech maturity you have to be at to HOPE to succeed with AI.

Dangerous Procurement Predictions Part III

As per our first two posts, if you read my predictions post, you know SI hates predictions posts. It fully despises them because the vast majority of these posts are pure optimistic fantasy and help no one. Why are the posts like this? Because no one wants to hear the sobering reality off of the bat in the new year and the influencers care more about clicks than actually helping you.

But the predictions are not only bad, they’re dangerous if you believe them. So we are continuing to lay bare the reality of the situation to make sure you understand that this year isn’t much different than last year, no miracles are coming, and only hard work and the application of your human intelligence are going to get you anywhere. Today we tackle the next three, and while we hope we’re getting close to the end of the series, we’re pretty sure there will be at least one more entry.

8. Global Trade Will Shift, Prioritizing Resilience Over Cost.

In the mid to long term some trade will shift to prioritize resilience, but most trade won’t. While defence procurements, critical mineral and material acquisitions for high-end electronics, and valuable commodities that can be traded like currency (such as gold, silver, platinum, diamonds, etc.) will be shifted for resilience, the reality is that, even with natural disasters, sanctions, trade wars, and actual wars, most companies aren’t going to make any changes to their supply chains (unless given absolutely no choice) because

  • finding new suppliers (in new countries) takes time and effort
  • qualifying new suppliers (in new countries) takes time and effort
  • identifying and contracting reliable carriers takes time and effort
  • building and securing new supply lines takes time and effort
  • etc.

and most companies are in constant fire-fighting mode, overworked, overstressed, and they just don’t have the time as long as the current supply chain, while strained, still works. Until their supply completely dries up, their primary production lines and revenue streams are threatened, and they have no other choice, they won’t change because they’ll keep telling themselves random natural disasters won’t impact them, the tariffs are only temporary, sanctions change with administrations, and wars eventually end.

9. Your employees will orchestrate outcomes.

Woody Woodpecker, take it away!

The level of talent needed to orchestrate outcomes is well beyond the average level of talent in an average (and even most above average) Procurement Department(s). There’s a reason that talent is a concern, a <href=”” target=_blank>top risk, and a top barrier for not just the last five years of studies and surveys, but at least the last ten. Talent has been scarce for a decade, and the situation is much worse since COVID. COVID saw many early retirements of the forced and chosen variety. Then the constant fears of recession saw more layoffs, starting with the highest paid (and most experienced) talent first. And you can be damn sure many of them are not coming back. We told you a year ago that talent is about to become scarce, and we’re sad to say we think we underestimated just how scarce talent is about to become.

And the reality is that only top talent can orchestrate outcomes. All the vast majority of talent can do is execute tasks one by one in a well-defined process. They can’t create new processes, and they certainly can’t define new outcome-centric processes on the fly. Especially when the ORCestration platforms they are given can’t even “orchestrate” a process to lead a mouse to the cheese it desperately wants.

10. New Year, New Me.

Who were you last year?

That’s right, the same person you are this year.

This BS lasts until all the bubbly you drank on New Year’s eve wears off, the rose coloured glasses go dim from the glare of doing the same damn thing as you stare at the same damn screen 12 hours a day, and you get overwhelmed with all the same tasks you were doing last year. Within two weeks at most, the new year, new me bullcr@p disappears with your last new years resolution and you’re just fighting to survive being overworked, understaffed, underfunded, and under-resourced, especially on the tech side (because the C-Suite wasted all the budget on a Big X Consultancy Gen-AI project that never even got to beta testing because the prototype phase never actually worked).

Most people won’t even make an effort to improve, which is the best one can hope for! (So if you have an employee who does, proactively give them a raise, any training they ask for, and keep them. Because, as per our response to the last false, and dangerous, prediction, talent is scarce and you should do whatever you can to keep whatever talent you have [instead of trying to replace it with fake AI that will never work fully autonomously].)

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!