Category Archives: SaaS

AI vs No AI – Let’s Make This Clear!

There are valid uses for AI, and valid AI models you should use. (LLMs are rarely one of them, having only a handful of reliable applications, but, when you push them aside, there are lots of other AI technologies that actually work if you don’t get blinded by the hype.) But there are invalid cases, and AI models you shouldn’t use. So to make it easy-peasy for you, here’s a simple guide!

USE AI WHEN DON’T USE AI
It’s a well constrained use-case where AI has been successfully deployed in industry, where the confidence is proven, and where you have access to the right technology. It’s a poorly defined use case, AI has not yet been successful for the use case, the confidence is unproven, and/or the tech you have access to is still in alpha!
It works as well as previous gen tech but with significantly shorter training cycles and easier integration and utilization. Previous gen tech still works better, costs less, and/or has no impediments to integration or UX.
It can bring value beyond what last generation tech can bring. All of the value can be achieved using traditional rules-based (A)RPA, (decision) optimization, analytics, or (classical) machine learning.
It’s a cost effective solution that can be run predictably based on a predictable cost model. It’s based (primarily) on LLM(-based) models that have unpredictable compute costs and, with the wrong request, can eat up thousands of dollars on a single request.
You have a valid use case for agentic tech! You think you have a valid usre case for agentic tech. (If you think you’re ready for AI, you’re NOT ready for AI.)
You’ve mastered current generation tech. You’re still a generation (or three) behind on tech.
You have in-house expertise on what AI is, and isn’t; where it can, and can’t be successfully deployed; and what “AI” is typically appropriate in a given situation. You’re relying entirely on (junior) consultants from the Big X promising it’s gonna “change your life“.
It’s designed to augment human performance and make your employees more productive and more effective super humans (able to do the work of 3, 5, 7, and even 10 regular humans). It’s designed to replace humans. (This doesn’t mean it can’t reduce the number required to do a task, just that at least one is still maintained to handle exceptions and make decisions.)
The firm is selling augmented intelligence! The firm is selling AI Employees. (There are none! And any firm that makes this claim is dehumanizing your employees! But hey, it’s your choice if you want to lose all your money.)

Get it yet?

C-Suite Only Has Budget for AI? Then Lie its AI and Buy Solutions That Work!

You need modern tech more than ever, but with unemployment on the rise (and people buying less), recessionary fears, tariff wars, etc., the C-Suite doesn’t want to give you the increased budget you need to buy the tech you desperately need to be more productive.

On the flip-side, they are finding millions of dollars for “AI” (in the hopes the lies are true and they can replace you, even though any attempts to do so will result in massive negative repercussions) despite the fact you’re not ready for AI and most AI-first vendor solutions SUCK, and trying to bring in in big consultancies (who will propose multi-year big-bang projects that, like all big bang projects that came before, will result in big busts and possibly create some of the biggest supply chain disasters of all time). You know the tech doesn’t work. You saw the 6% success rate from the recent McKinsey study and the 5% from the recent MIT study (both in late 2025). That’s a 94% failure rate, which is even worse than the general tech failure rate of 88% (as per a Bain 2024 study)!

You know you need modern tech, and you know, for the vast majority of those needs, AI ain’t it. Especially since you know that there is no such thing as Artificial Intelligence. (Artificial Idiocy for sure — it’s called Gen-AI — but not Artificial Intelligence. The best you can get is Augmented Intelligence, but that’s always narrowly focussed and quite rare due to too much research, and promotion, of Gen-AI LLMs that will never work [as the models are foundationaly flawed and there is no more data to train them on].)

So what you do?

Frankly, you lie and say its AI!

The fact of the matter is, if the C-Suite is insisting on AI, it’s because they don’t actually know what AI is. (This should be abundantly clear by the fact we have a lot of vendors, and consultancies, claiming AI Employees, and we all know that’s pure bullcr@p!) And the reason they are insisting on it is because everyone else is lying to their face. The Big (and small) “AI” Tech Vendors. (Big) Analyst firms. Big Consultancies. The Media publishing the fake claims and fake stories (and presenting what are bullcr@p-filled advertorials as vetted and verified case studies). Influencers spreading the hype for their own profit.

And since this C-Suite has a lower TQ (Technological Quotient) than an average elementary school child (who can probably use your phone and tablet better than you can), it’s not like they have any real clue what AI is.

And if the C-Suite wants “proof” that the vendor you select has AI, ask the C-Suite what that means to them. Nine times out of ten it is a chatbot interface. If the vendor you select hasn’t done so already, just have them hook in Chat-GPT through an API, let the execs play for 5 minutes, get the C-Suite approval, and then have the vendor disable the interface before delivery (in platforms where it truly is useless, or limit the chatbot interface to help queries where there is very little downside to it screwing up).

When Chat-GPT first became the rage in Procurement, C-Suites insisted on “AI Guided Buying” even though, with a well designed federated catalog (that support standard service forms) — that supported contracts, preferred vendors, (learned) business rules, and budgets — it was five to ten times faster to use the integrated search bar and filters (as per our twelfth entry of our 2025 Myth-busting series where we illustrated just how dumb the Gormless AI could be). After losing out on a few deals due to this lack of functionality (even though they were the buyer’s choice), a fed-up vendor built a chat-bot led guided buying offering on standard LLM libraries. They can turn it on, demo it for the pointy-haired bosses, and turn it off again.

This is critical when the real value of

  • analytics is exploration
  • sourcing is optimization (not error-prone calculations by an LLM that might erroneously multiply a number by -1 because it’s interpretation of the request is to satisfy an arbitrary savings number anyway it can)
  • supplier management is potential issue detection and human review and remediation
  • procurement is honouring contracts, using vetted suppliers, and following rules designed to prevent risk
  • etc.

So take advantage of the fact that they don’t have a clue what (real) AI is and tell them whatever tech you need to solve your problem, regardless of how much AI it does, or doesn’t, have is the latest and greatest AI if that’s what it takes to get the tech you need to solve your problem.

I know you don’t want to lie, but the reality is that is now what you have to do to keep your job, because if you don’t get the tech you need, you’ll fail and be made redundant. And since everyone else is lying too, chances are someone is already saying the tech is AI and you can just point to them (and blame them for the lie).

End of the day, it’s whatever makes you the most productive. That might be a new AI solution, might be a classic ML or NN solution, or it might be two-decades old rule-based automation where you can encode a few rules and have the solution do 95% of your work on auto-pilot without any worry of it ever screwing up (and costing someone their job).

Remember, you’re hired to get things done, not listen to bullsh!t that comes straight from the A.S.S.H.O.L.E.. Don’t get blinded by the hype!

(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!