Category Archives: Technology

Marketplace Madness is Coming Because History WILL Repeat Itself

Over on LinkedIn, Jon The Revelator asked what 2005 could tell us about Procurement AI in 2024, reminding us that major ERP companies have tried multiple times to move “down market”, there’s (still) no dominant player in the pure “Procurement” sector (with a number of big firms showing up in a slice-of-the-pie analysis (and most analyst market maps), and many names that were around in 2004 are names most of today’s practitioners have never heard of.

And, as part of the conversation (check the comments), Jon asked if history will repeat itself. (i.e. Will many of today’s players disappear? Jon listed a dozen names that are no longer in existence.)

the doctor‘ answer, MOST DEFINITELY!

To be more precise, the doctor is predicting twice the percentage of (fire-sale) acquisitions and out-of-business/shut-downs over the next eighteen (18) months compared to usual. What does this mean numbers wise? He usually removes a few dozen vendors from his database every year (which is about 5% of the number of vendors in the Source-to-Pay+ [S2P+] space, as captured in the Sourcing Innovation Mega-Map), and expects that within eighteen (18) months, he will need to remove a few few dozen vendors from his database, which translates into 10% or more, or a number of vendors that is closer to 100 than 50! That’s significant.

Why? A number of reasons, which include, but are not limited to:

1) A lot of the smaller 1 or 2 module pure-play VC funded companies that took (too much) money before the Silicon Valley Bank failure and are not yet profitable are now in a bad situation given that VC funding is still recessed, PE is now looking for close to 300K/FTE for a “good” investment, and these smaller companies are not there as enterprise Procurement software acquisition for the last two years has been recessed (due to overall market fears of recession), and, in addition to sales being down, buyers have been risk averse and newer / smaller players have, in general, being doing worse than they were doing during COVID (when companies were desperate for solutions that were pure SaaS) and just pre-COVID (when companies were more willing to try smaller plays in what they thought was a globally stable economic environment).

2a) A number of smaller plays were started by consultants with no funding, no real sales team, and no marketing support and they just can’t get traction through the noise (or funding).

2b) A lot of smaller plays were started by Procurement practitioners with little or no funding, the same sales and marketing problems, and a bigger disadvantage because they only know their problems, and maybe the problems of a small peer group they meet with in their local organization’s monthly meet-up, and they don’t know the problems in general, what sells, and what doesn’t. This makes funding for them hard (as smart investors know that Procurement experience alone only goes so far), and sales and marketing harder (they were buyers, not sellers; and they don’t understand that the message they needed to hear is not one that will cut through the noise and reach buyers who aren’t as experienced and enlightened as they are).

And when you start to break down Source-to-Pay+, you find that …

3) There are way too many “tech without a cause AI plays” … with no real, demonstrateable value, and, in reality, no future. (Especially since anyone from the Golden Era remembers that all the rebel without a cause managed to do was get his friend killed.)

4) A lot of the carbon “calculators” offer no new functionality (and thus no new value). Most good DIY (do-it-yourself) spend analytics application providers can help you build one in 15 minutes (no joke! — give Spendata a call, for example). Furthermore, you need good data for them to work, so if you don’t have integrations to good data and systems with better data, what’s the point?

5) Moving on to classic sourcing, every developer and their dog can whip up eRFX functionality in a matter of weeks and there is no differentiation there anymore if you’re just another eRFX. So you have a slightly different take on a UX. Well, guess what, that don’t impress me much … and the doctor ain’t alone in that viewpoint.

6) Moving onto classic CLM, if the platform doesn’t support deep analytics, negotiation support, or something that makes it more than an e-filing cabinet, it’s going, going … gone. Way too many over-glorified document management solutions out there to survive, especially at a price point beyond a few hundred per named user per year (given how many freeware/shareware/end-consumer document platforms exist in the open-source repositories).

7) There’s over one hundred (100) SXM plays. OVER ONE HUNDRED. Given that SXM is a CORNED QUIP mash, and you need different types and depths of solution for organizations of different sizes in different verticals, there’s room for two to three dozen. But one hundred? Forget it! Especially since if all your solution ends up being is a glorified SaaS (relational) database, there’s no value there.

8) While there is a desperate need for analytics, and not enough true analytics players, first generation solutions that are nothing more than pre-generated static (OLAP) reports are about to go the way of the dodo. Real-time, dynamic, customizeable analytics are what’s needed today.

9) Standalone ePro is going to go. Given that there are dozens of P2P solutions, and a growing number of P2P solutions with built-in payment support, why would you want old-school ePro, which doesn’t help the average organizational user or get tail-spend under control.

10) AP without full I2P support, integrated payment support, or integrated P-Card support or value beyond classic AP is also going to go. There are dozens and dozens of these solutions (including dozens that started during COVID because people needed to do business entirely online, and since there appeared to be an opportunity for anyone who didn’t do their research beyond, which is more people than you’d think, see The Biggest Mistake founders in S2P+ keep making after two decades, too many of these were started). The market just doesn’t need that many!

11) Stand-alone Intake(-to)/Orchestrate solutions. The current poster children of the space will soon have a fall from grace (and only the smart will survive)! Call me Scrooge if you like, but there’s a logic behind why I’m developing a bah-humbug attitude towards most of these. And it goes something like this.


  • Pay For View if modern procurement solutions are completely SaaS, then they should be accessible by anyone with a web browser, so why should you have to buy a third party solution to see the data in those applications? wouldn’t it make more sense to just switch to modern source to pay solutions that allow you to give variable levels of access to everyone who needs access instead of paying for two solutions AND an integrator?


  • Solution Sprawl while orchestration is supposed to help with solution sprawl, it’s yet another solution and only adds to it. Wouldn’t it make more sense to invest in and switch to a core sourcing and/or procurement platform with a fully open API where all of the other modules you need can pull the necessary data from and push the necessary data to that platform?


  • Where’s the Beef? Talk to an old Pro who was doing Procurement back before the first modern tools began to be introduced in the late 90’s and they’ll tell you that they don’t get this modern focus on “orchestration” and managing “expenses” and low-value buys because, when they were doing Procurement, it was about identifying and strategically managing multi million (10, 50, 100+) categories where even 2% made a significant improvement to the bottom line, and way more than 10% on a < 100K category.
  • Where’s the Market? This is only a problem in large enterprises — right now, many of these I2O solutions are going after the mid-market who are eating it up because of ease of use, but as soon as they realize the emperor has no clothes, and there’s no support for real strategic procurement (yet alone strategic sourcing) and you have to go out and buy more platforms, what’s going to happen? The reality is that the mid-market is better served by a federated catalog management / punchout platform, and will likely be better served still by a new breed of e-commerce B2B solutions for end-user Procurement (which is being led by providers like BlueBean. Which will only leave the enterprise space, and, more specifically, the enterprise players who are stuck with older generation solutions (due to sunk costs, etc.) that don’t integrate well or have modern bells and wizards.

And so on. The market is over crowded, most of the providers are struggling, funding has dried up for all but the best (who haven’t been overfunded already) [and already profitable with true long-term growth capability], and there’s no room for the rest.

History will repeat, and those who don’t follow best practices and avoid mistakes will be the first to fall.

Another Supply Chain Misconception That Should Be Cleared Up Now

Yesterday we discussed one supply chain misconception that should be cleared up now because, despite all of the misconceptions mentioned in an Inbound Logistics Article, it was not addressed. However, there is a second misconception that is almost as critical that was not addressed either, so today we will address it. And while, there are, dozens of common misconceptions (including the 22+ mentioned in the article), these are the two that are the most critical to understand, as they are two that pose the most risk in most of today’s Procurement organizations.



Supply Chains Have Reached (A New) Normal

Supply chains have never been, and will never be, normal as they will always be in flux due to perturbations, delays, and disruptions that happen daily. You may not see all the trial and tribulations a third tier supplier goes through every day, but trust the doctor when he says they have just as much turmoil as you do. Nothing is predictable in supply chains. When you accept this misconception in conjunction with the first misconception, it’s easy to see how almost all of the others are also misconceptions (that highlight slices of the bigger misconceptions).

For example:

  • cost becomes much less important than supply assurance due to the unpredictable nature of supply chains
  • since it’s not a linear, closed, model, zero-sum doesn’t apply
  • we made up the stages of planning, buying, transportation, and warehousing silos to fit a theoretical definition of normal that doesn’t exist
  • there is at least a hand-off at every stage, so the process is not disconnected but linked, if not intertwined
  • etc. etc. etc.

When you accept the reality, Supply Chain Management, as well as Source-to-Pay, will become a lot easier to manage because you will realize that

  1. only human expertise can adapt to new situations and find real-world solutions to the new challenges that arise
  2. technology will allow you to automate the tactical / semi-normal operations and instead focus on the exceptions and challenges, making you more productive as you focus the majority of your effort on strategy and thinking vs (e-) paper pushing and thunking which is the only thing the machine is good at (and, based on current technological understanding, ever be good at — which is exactly why we can limit it to the thunking because it can do over 3 Billion calculations a second flawlessly [if we ditch the “AI”] while we struggle to do 3)

In other words, only intelligent, adaptable, humans can manage constantly changing supply chains. Good technology can alert them and give them the intelligence they need to make good decisions, but technology cannot make those decisions for them.

(And the doctor, who dreaded saying Bye, Bye to Monochrome UIs can’t wait for the day he can say bye, bye Gen-AI.)

One Supply Chain Misconception That Should Be Cleared Up Now

Not that long ago, Inbound Logistics ran a similarly titled article that quoted a large number of CXOs that made some really good observations on common misconceptions that included, and are not necessarily limited to (and you should check out the article in full as a number of the respondents made some very good points on the observations):

The misconceptions included statements that supply chains should:

  • reduce cost and/or track the most important metric of cost savings
  • accept negotiations as a zero-sum game
  • model supply chains as linear (progression from raw materials to finished goods)
  • … and made up of planning, buying, transportation, and warehousing silos
  • … and each step is independent of the one that proceeds and follows
  • accept they will continue to be male dominated
  • become more resilient by shifting production out of countries to friendly countries
  • expect major delays in transportation
  • … even though traditional networks are the best, even for last-mile delivery
  • accept truck driver shortage as a systemic issue
  • accept the blame when anything in them goes wrong
  • only involve supply chain experts
  • run on complex / resource intensive processes
  • … and only be optimal in big companies
  • … which can be optimized one aspect at a time
  • press pause on innovation or redesign or growth in a down market
  • be unique to a company and pose unique challenges only to that company
  • not be sustainable as that is still cost-prohibitive
  • see disruption as an aberration
  • return to (the new) normal
  • use technology to fix everything
  • digitalize as people will become less important with increasing automation and AI in the supply chain

And these are all very good points, as these are all common misconceptions that the doctor hears too much (and if you go through enough of the Sourcing Innovation archives, it should become clear as to why), but not the biggest, although the last one gets pretty close.



We Can Use Technology to Do That!

the doctor DOES NOT care what “THAT” is, you cannot use technology to do “THAT” 100% of the time in a completely automated way. Never, ever, ever. This is regardless of what the technology is. No technology is perfect and every technology invented to date is governed by a set of parameters that define a state it can operate effectively in. When that state is invalidated, because one or more assumptions or requirements cannot be met, it fails. And a HUMAN has to take over.

Even though really advanced EDI/XML/e-Doc/PDF invoice processing can automate processing of the more-or-less 85% of invoices that come in complete and error free, and automate the completion and correction of the next 10% to 13%, the last 2% to 5% will have to be human corrected (and sometimes even human negotiated) with the supplier. And this is technology we’ve been working on for over three decades! So you can just imagine the typical automation rates you can expect from newer technology that hasn’t had as much development. Especially when you consider the next biggest misconception.

Valid Uses for Gen-AI!

the doctor has been told he’s too hard on Gen-AI. He doesn’t think he’s hard enough, but there are those who keep insisting that Gen-AI has some valid uses. And they’re right, it has some. Not the uses that you need it for, but actual uses nonetheless.

So today, in a rare moment of weakness, he’s going to acknowledge those uses. Soak it in. He may never do so again.

1. Ensure your insurance / bank only covers and lends to people you like.
One of the great things about Gen-AI is that almost all models are biased, and it’s really easy to train them to be as biased as you want. Only want your health insurance to accept only young people between 25 and 40 with no family history or indicators of any illness whatsoever? No problem. Don’t want your bank to approve a loan to anyone who isn’t an all American Christian white? No problem. Race-Biased Gen-AI to the rescue!

2. Have it make up a new story for your child who constantly wants new stories every night.
Train it on thousands of stories kid suitable and it will make up a new story every night (with a high probability of most those stories being safe and suitable — chances are only a few will scare them into therapy). Your kid will be happy (at least until they get scared into therapy) and your brain will get the rest it needs at night (so it can start worrying about how it’s going to pay for that therapy). Put those constant hallucinations to use. It’s your own personal Scheherazade, with just a little bit of Grimm and occasionally a bit of King (Stephen).

3. Incite the mob.
Need a mob behind you to get your cause front page on the headlines? Incite a mob to cover your theft attempt at a corporate headquarters above a luxury department store? Maybe even help you overthrow a capitol? No sweat! Program that Gen-AI to be as hateful and incitory as possible and have it pump out fake news propaganda 24/7 until you have the mob you need on your side and there you go!

4. Scam the Scammers. (Or at least keep them busy and out of your inbox.)
Most scammers will keep trying as long as someone is responding to them (and eating up their time). Guess what AI has a lot of — GPU time. Most models have 10,000 (or more) GPUs at their disposal. That’s a lot of scammers an AI can tie up for you. (Especially if they can’t differentiate easy pickings Grandpa Joe from a very agreeable but completely broke GrandpAI Joe.)

5. Take down a rival’s network.
Simply train in some sleeper behaviour for a few months into the future, and once the competition is done with their tests and trust it … poof … down goes their network.

And if you want to be truly evil, you can always use Gen-AI to

6. Ensure your terror campaign is as lethal as possible.
We’ve read the stories of how even recent tests of self-driving systems decided to ignore the shadows of what were actually people RIGHT in front of them and drive into those shadows at full speed. A few minor alterations and instead of avoiding people-like figures and shadows, it will be the murderous trolley that tries to kill as many as possible. And who says you have to limit it to trolleys? Use it to program bomb-bearing drones and it will seek out the densest crowd possible. And so on. And yes, we went to a very dark place, but just where do you think AI is taking us? There are currently NO bright outcomes. Ponder that before you go singing its praises.

Of course, if you just want to be a little chaotic around the house, and only take that first step down the dark path, just hook up it’s hallucinatory outputs to a random direction generator and use it to:

7. Power your Roomba.
Your pets will think it’s truly possessed!

So there you go — 7 valid uses of Gen-AI. You decide how many of them you want to use.

Enterprises have a Data Problem. And they will until they accept they need to do E-MDM, and it will cost them!

insideBIGDATA recently published an article on The Impact of Data Analytics Integration Mismatch on Business Technology Advancements which did a rather good job on highlighting all of the problems with bad integrations (which happen every day, and especially if you hire a f6ckw@d from a Big X [as that will just result in you contributing to the half a TRILLION dollars that will be wasted on SaaS Spend this year and the one TRILLION that will be wasted on IT Services]), and an okay job of advising you how to prevent them. But the problem is much larger than the article lets on, and we need to discuss that.

But first, let’s summarize the major impacts outlined in the article (which you should click to and read before continuing on in this article):

  • Higher Operational Expenses
  • Poor Business Outcomes
  • Delayed Decision Making
  • Competitive Disadvantages
  • Missed Business Opportunities

And then add the following critical impacts (which is not a complete list by any stretch of the imagination) when your supplier, product, and supply chain data isn’t up to snuff:

  • Fines for failing to comply with filings and appropriate trade restrictions
  • Product seizures when products violate certain regulations (like ROHS, WEEE, etc.)
  • Lost Funds and Liabilities when incomplete/compromised data results in payments to the wrong/fraudulent entities
  • Massive disruption risks when you don’t get notifications of major supply chain incidents when the right locations and suppliers are not being monitored (multiple tiers down in your supply chain)
  • Massive lawsuits when data isn’t properly encrypted and secured and personal data gets compromised in a cyberattack

You need good data. You need secure data. You need actionable data. And you won’t have any of that without the right integration.

The article says to ensure good integration you should:

  • mitigate low-quality data before integration (since cleansing and enrichment might not even be possible)
  • adopt uniformity and standardized data formats and structures across systems
  • phase out outdated technology

which is all fine and dandy, but misses the core of the problem:

Data is bad (often very, very bad), because the organizations don’t have an enterprise data management strategy. That’s the first step. Furthermore this E-MDM strategy needs to define:

  1. the master schema with all of the core data objects (records) that need to be shared organizational wide
  2. the common data format (for ids, names, keys, etc.) (that every system will need to map to)
  3. the master data encoding standard

With a properly defined schema, there is less of a need to adopt uniformity across data formats and structures across the enterprise systems (which will not always be possible if an organization needs to maintain outdated technology either because a former manager entered into a 10 year agreement just to be rid of the problem or it would be too expensive to migrate to another system at the present time) or to phase out outdated technology (which, if it’s the ERP or AP, will likely not be possible) since the organization just needs to ensure that all data exchanges are in the common data format and use the master data encoding standard.

Moreover, once you have the E-MDM strategy, it’s easy to flush out the HR-MDM, Supplier/SupplyChain-MDM, and Finance-MDM strategies and get them right.

As THE PROPHET has said, data will be your best friend in procurement and supply chain in 2024 if you give it a chance.

Or, you can cover your eyes and ears and sing the same old tune that you’ve been singing since your organization acquired its first computer and built it’s first “database”:

Well …
I have a little data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

Oh, data, data, data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

It has nonstandard fields
The records short and lank
When I try to read it
The blocks all come back blank

I have a little data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

My data is so ancient
Drive sectors start to rot
I try to read my data
The effort comes to naught

Oh, data, data, data
I store it on my drive
And when it’s old and flawed
The data I’ll archive