Category Archives: SaaS

(That Vendor Rep) He Ain’t Pretty …

A verbal commentary on the current state of SaaS …

I wore two hats, I was pounding the sand
And on the weekend in a rock & roll band
One Monday aft in the office board room
In walked a rep who looked like Max Headroom

He stared at me and it was scaring me off
He said he worked for the vendor on top
I heard a voice inside me say
He ain’t pretty he just looks that way

We made a date for demo round two
I wore my jeans and he wore a suit
There was this misconception all over town
That he sold software savings by the pound

He said “Buy my app, there won’t be no fuss”
I said “Why? you haven’t shown me cost-plus”
Watching him leave I heard his grunt-in-tow say
“He ain’t pretty he just looks that way”

So, I called his office, the admin was there
Said “He’s busy, he can’t come to the phone”
I held my breath, decided to wait
A guy like me needs to set some things straight

I got stuck with the sales rep from hell
Didn’t take much time for my hormones to tell
Letting him in has been a grave mistake
He ain’t pretty he just looks that way

His ego wrote cheques incredibly fast
But the software he sold wouldn’t save us the cash
I laughed out loud to my total dismay
He ain’t pretty he just looks that way
He ain’t pretty he just looks that way

He ain’t pretty
He ain’t pretty
He ain’t pretty
He ain’t pretty he just looks that way

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!

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!

Dangerous Procurement Predictions Part II

As per our first post, 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. And to make sure you don’t fall for them and make bad decision based on them, we’re going to tackle some of the most dangerous predictions, which include predictions that look innocuous at first glance (like the last prediction on how a big legacy suite will go out of business) but hide the dangerous consequences of what will actually happen if a big suite finds itself in big trouble. Today we tackle the next four, and you can be sure this won’t be the last post in our series. Feeds are still being flooded with prediction posts, and I’m done ignoring the insanity.

4. The jobs market will be tough for the first half of the year, but will start to pick up in Q3 and Q4.

The job market is tied to the economy, and everyone predicts the job market will rebound when the economy picks up. But here’s the thing. Even when the economy picks back up, the job market never does quite as well as the last time. And the economy isn’t going to magically improve half-way through the year. This is the exact same thing we’ve been told the last two years, and it hasn’t happened.

First off, most of the first world economies around the world are flat, borderline recession, or in recession. Secondly, the only thing propping the US economy up right now is AI, and the money circles keeping it afloat as all the AI, Hardware, and Software companies keep moving the same money around investing in each other to keep each other afloat. If the bubble bursts, the US is in trouble, and the economy will quickly flush itself down the toilet. And the job market will go with it.

Considering only the big tech giants who have been hoarding cash for the last few years are in good shape, and everyone else is trying to conserve cash to survive not only the current market but a potential recession, the last thing they are going to do is hire unless absolutely necessary to fill a critical role as a result of a departure. Remember, they’ve spent the last two years using AI as an excuse to lay people off and are always looking for the next excuse to lay people off, not hire them!

Jobs will continue to be super scarce, and only the best will have a chance to land one.

5. We’re in the early stages of a broader pushback (against unnecessary upgrades or technology investments).

A few companies smartening up and saying no to forced big provider upgrades, eight (8) figure consultancy projects, and big Gen-AI investments is not pushback. There have always been a few leaders who have broken away from the pack, did the math, and made the right decisions, but the pack is still charging ahead on Gen-AI. Every big software shop except IBM (who hired a CEO who can actually do math) has invested heavily in Gen-AI, which still loses four dollars for every dollar of revenue, despite any hopes of a real return in the near future and a 94% failure rate.

Let’s face reality. I warned this space about The Vendor In Black nineteen years ago and how he always Comes Back sixteen years ago, no one took heed then, and no one is taking heed now. The business model of the enterprise software space, which has not changed for the two decades I’ve been covering it, is to solve the problem created by the old sh!t by selling the customers the new sh!t that comes with new problems so they can sell even newer sh!t in three years to fix those (and so on). Same old story. Only the vendor names change.

6. We Won’t Buy Things; We’ll Orchestrate Ecosystems.

This prediction likely came straight from the A.S.S.H.O.L.E. and anyone who repeats it should be ashamed of themselves. There are no AI Employees. Claims to the contrary are false and anyone making those demeaning and degrading claims is simply dehumanizing you. And, as we have clearly explained, you definitely don’t want agentic buying because it will happily spend your money not only on stuff you don’t need but stuff that doesn’t exist and, if you’re super unlikely, stuff that is highly illegal. You need wood, it will buy up all the Minecraft wood because it’s cheap and call your problem solved. And that’s if you’re lucky. If you’re not, it will fulfill your resin need with an illegal purchase of hash (the drug) on the dark web (which is labelled resin so the poster can claim they never advertised an illegal drug). And so on.

Plus, as we have already noted, most of today’s “orchestration” platforms in Source-to-Pay are really ORCestration platforms and can barely connect a handful of major Source-to-Pay offerings. They’re nothing close to what is needed to orchestrate ecosystems.

7. Boards will Zero in on Supply Chain Security and Supplier Risk shifts from quarterly PowerPoints to continuous “signalops”.

Just like they won’t invest more in cybersecurity, they won’t invest more in supply chain security until they lose a shipment in the tens of millions. After all, they’ve got supply chain insurance, why should they care? Especially since their current security measures have been sufficient up until now.

But here’s the thing. When the economy goes down, jobs go down. And then two things happen. People get desperate and turn to crime. And criminals, when their investments in drugs, alcohol, gambling, prostitution, and other quasi-legal through illegal activities start losing money because unemployed people run out of money to spend on their vices, these criminals get desperate too — and high value theft becomes more attractive. A temporarily unguarded truck here. A container there. An entire warehouse. And so on.

If it’s critical raw materials they can move (like rare earths), in-demand finished electronics they can sell (like iPhones, where a single container will contain at least 20M worth), military equipment or weapon (component)s that are now in demand globally, they’ll take bigger and bigger chances, especially if there are weaknesses in security. It’s not just cyber attacks that are going to increase, it’s physical attacks, supply chains aren’t ready, and companies won’t even stop preparing them until they lose tens of millions, don’t recover it all through insurance, and risk losing their insurance entirely. No one likes the math of risk prevention because, when it works, you don’t see the return. Even though it’s so much cheaper than insurance! And that’s why, in the majority of organizations, nothing will change.