Category Archives: Market Intelligence

Outcomes is a Dirty Word! Part I

And you shouldn’t have to hear it!

The word of the day is outcomes, and, no matter where it’s used, it’s a dirty word.

You all know that where DEI is concerned, especially in North America, it’s a dirty word. As @Jason Busch will explain in detail at every opportunity, DEI has replaced “equal opportunity”, but unlike properly applied equal opportunity, which took us two steps forward, DEI, or at least its “outcome”-focussed interpretation, has taken us two step backs.

These days, everything has to be measured, and the belief is that if you don’t meet the goals for whatever racial/religious/women/minority metric your organization has defined to be an appropriate racial/religious/women/minority mix for your organization, then you aren’t diverse, equitable, and inclusive and, therefore, you should go out and immediately hire the racial/religious/women/minority employees you need to meet the metric. Merit be damned. No longer is it the most qualified resource, where someone of a minority is hired when two or more applicants are otherwise equal, it’s the most qualified resource of the identified minority, who might not be at all qualified for the job! It’s the token black employee taken to a whole new level! Not only does it reward incompetence, but it insults minorities who study and work hard to be just as competent, if not more competent, than their white male counterparts.

But I digress — we already know outcomes is the dirty word of DEI. But what you don’t know is outcomes is a dirty word across the business, wherever it is used – and Procurement is no exception! Why? It’s only become the popular battle cry since the Age of (BS) AI, whereas its prior use was been limited to situations where the consultancy, vendor, or analyst firm could hide the darkness and venom that the word contained.

More specifically, until recently, outside of DEI, outcome was primarily the verbiage of GPOs, who were doing their best to convince you to turn over a significant percentage of your procurement to them, or recovery audit firms, who were doing their best to convince you their services were the only way to recover your money that your suppliers were assuredly screwing you out of.

But they reality is that they’ve been both misleading you since the get-go. Sure a GPO can get you better prices than you can get on the long tail with their volumes, but that’s only true for the long tail. Moreover, the reality is that the costs aren’t that much less, if any less, than what you could negotiate on your own if you did a winner-takes-all long-tail RFQ to a MRO, office supplies, electronics supplier who could meet the volume across your long-tail needs, especially since that GPO is charging the supplier an administrative fee of up to 3%, and they’d happily give you the same price to NOT have to pay that fee! Add to that the GPO is charging you for their services, and you’re not saving much. Plus, when you work your way up to the head of the tail, you are definitely in 3-bids-and-a-buy RFQ or auction territory, and the application of a well designed tail spend sourcing solution will save you just as much as a GPO, IF NOT MORE!

Moving to recovery audit firms, their outcome-based pitches sound great, as you only pay their 33% if they recover the money on your behalf and fatten your bank account, but here’s the thing. If you had a properly designed retail-focussed e-procurement solution that integrated supplier and product management, did m-way matches, and prevented payments where you didn’t have good receipts that matched the invoice that matched the PO where the prices matched the contract, rejected duplicate invoices, tracked rejected units and associated credits, applied those credit notes against future orders (with the matching product), etc., you could prevent all of those overpayments in the first place — despite the fact that all the recovery audit firms tell you that overpayments (and their services) are unavoidable.

But there are more, and more modern, examples. The worst is AI-first services-as-software vendors convincing you that you should pay based on “outcomes” instead of on a traditional SaaS pricing model. Their rationale? The majority of SaaS tools that you are paying for aren’t offering you immediate, measurable, savings and, therefore, are too expensive. But if you paid for software based on “outcomes”, you’d have measurable value and you could claim the fee was worth it. And the argument sounds convincing, even if it’s complete and total bullshit. The purpose of most software is to increase efficiency, not save money. That’s the value.

And when the real reason they are pushing outcome-based pricing is that they can’t afford to sell based on a SaaS model because the compute costs of their BS AI-first are too high to cover on traditional SaaS pricing — even though there is a traditional A-RPA SaaS application that does everything their app does for a fraction of the cloud and compute cost, as long as you don’t need a fancy-smancy natural language interface or a slick UX. In other words, if they were honest about the true value of their application, they could never charge enough to cover their costs and would be out of business yesterday.

A second, more modern, example is the big consultancies taking a queue from their GPO, Recovery Audit, and now AI-first services-as-software peers and trying to justify their highly inflated pricing (which has skyrocketed over the last decade as they became the go-to firms for all big tech strategy). Especially since it’s the only way they can overcharge for projects where they are primarily deploying a multitude of AI agents (which we know produce utter garbage, just look at the Deloitte fiascos in Australia and Canada) and juniors that they hope will catch and clean up all of the hallucinations in the deliverables. (Because if they charged based on what the tech and juniors were worth, in a climate where no one wants to pay inflated rates for consultants for projects with potentially guaranteed return, they wouldn’t be able to maintain their high rates.)

There are more examples, but by now you should see the common theme. Which is simply this: “outcomes” is always a way to charge you more for less (and sometimes next to nothing) (just like DEI is an excuse to replace people with actual capability with people with next to no capability).

But the worst part, the blatant financial rip-off that always accompanies a (pure) “outcome-based” sales pitch isn’t the worst of it!

You Really Don’t Need to Read Another State of Procurement Report for Five Years!

Just read this 34 part series and you can ignore the 10+ surveys / studies / reports that will be collectively released by every major ProcureTech consultancy and analyst firm this year (which will likely include, but not be limited to: Capgemini, Deloitte, Everest Group, EY, Hackett, McKinsey, PwC, and many, many more)! We say this with certainty because we reviewed all of the reports they put out for the last 5 years and the vast majority of the content was the same year-after-year and firm-after-firm. You can practically count on any survey/study that tackles barriers, risks, and concerns to overlap with the following at least 80%, and that these will be the most significant barriers, risks, and concerns. In fact, in five years, only one concern will have changed, and that’s the tech-du-jour, because that’s all that was really different between 2025, 2020, 2015, etc.

You’re welcome!

You Don’t Need To Read Another State of Procurement Study for the Next 5 Years!

Top Barriers to Success

Breaking Down The Major Procurement Risks with High or Moderate Impact

Primary Concerns for Procurement Leaders

BONUS

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.