Category Archives: SaaS

Now is NOT A Great Time to Buy (Part 3)

Standalone “Intake to Nowhere”, “Classic Onboarding and Supplier Management”, “Predictive” Analytics, “Contract” AI, “Agentic” AI or Classic Mega-Suites … until 2029

Yesterday we reminded you that while you need intake and orchestration, you need supplier intelligence, you need predictive analytics, you need AI-based contract analytics, and you need “Agentic” AI that executes (but does not make) decisions, you should not buy it standalone, at least not now, and you definitely shouldn’t buy a classic mega suite.

While all of the solutions we have tackled so far are currently over-priced, Agentic AI, which is the new hype, is the most over priced offering of them all, especially with the consistent over-promising by these new generation vendors that are promising BS “AI Employees” while delivering task automation that is reliable as a chocolate teapot where consistent, dependable, execution is concerned. Now, some of these vendors will figure out that you need constrained, double guard-railed, multi-agent systems with human monitoring and exception intervention and eventually deliver reliable augmented intelligence systems that make an average employee super human, and they will be worth it, most of these vendors will simply try to out-prompt each other through custom clod and chat, j’ai pété wrappers, cr@p out at about 80% to 95% reliability depending on the task, never be trust worthy, and never be worth it. Since these just started to hit the big time, with ridiculous over-funding, in the past year or two, it will be three more years before the dust truly settles and 2029 before you want to make any long term bets.

Plus, if you know the real history of AI, which is probably older than your grandfather FYI (with the first algorithm to be awarded the title developed 70 years ago), you know that it’s usually close to two decades before a new algorithm is mature enough, and understood enough, with real, solid, mathematical measures of reliability, for mass, unmonitored, industrial use. And typically at least a decade before it’s ready for leaders to apply it in industry for monitored, target, use. The first LLM hit the scene in 2018. That means 2029 is also the year it will finally start to be reliable for a certain (but small) set of tasks in certain (but a small set of) domains. It will still hallucinate more than an LSD loving dead-head, but by then we’ll have much better detection methodologies and confidence measurements and will actually be able to trust it when the results get through the multi-layered security gates that we’ll finally be able to build with more understanding.

And yes, as we’ve said twice already, you need this tech. But buying “best of breed” will only “bleed your cash in the best way possible” with little measurable return.

But don’t return to a “classic mega-suite”. These are now more over-priced than ever. First of all, as we’ve discussed many times on this site, unless you are a Fortune 1000/Global 3000 multi-national with extensive, and complex, source-to-pay needs, you don’t need to pay Millions of Dollars a year for a suite when an 80% mid-market solution for 250K a year will do the trick. (See our piece on how much should you outlay for ADVANCED Source to Pay.)

Not only do most organizations only have a few categories where advanced technologies are needed, and usually only in one or two of the modules the mega-suite sells, but most of their categories are so straightforward that even BoB mid-market solutions present not just an 80%+, but a 90%+, solution. Plus, modern ARPA and appropriately focussed Agentic solutions are allowing mid-sized organizations to cobble together “good enough” solutions from low-cost 80% point solutions for 10% of the cost of a mega suite that gets them started on their journey, allowing them to upgrade to better solutions as they need, and only as they need.

This is putting severe cost pressure on the mega-suites, which are going to have to admit that most of their solutions, workflow, and UIs are over a decade old and not worth the premium they once charged. For organizations that truly need these solutions, from vendors which aren’t aggressively updating their solutions (due to these vendors being purchased by PE firms at too high a valuation and, thus, being forced to cost cut to meet ridiculous sales targets), if they wait a year or two, these will soon be priced at what they’re worth, and you’ll get an annual license for less than half of what they are charging today and get all the functionality you need to boot!

So, at the end of the day, while you need a solid Procurement solution that comes with a modern intake front-end, has orchestration at the core, provides you supplier intelligence, integrates the analytics you need, helps you with your contracts and their processes (to the extent you actually need that help), and allows for adaptive robotic process automation for all your well defined tasks (and provides the data foundation for “Agentic” AI if you have valid applications where such technology will actually bring value), you don’t need to overpay for it. And you definitely don’t need to pay the double to quadruple price tags that current mega-suites are charging.

But if you can find what you need, at a fair price tag, and you buy that, you buy real value that will appreciate with time because it will do what you need it to do, at a fair price, and that’s the only way you save time and money with ProcureTech. Getting what you need, when you need it, at a fair price point. You know, classic Procurement!

Remember that.

Now is NOT A Great Time to Buy (Part 2)

Standalone “Intake to Nowhere”, “Classic Onboarding and Supplier Management”, “Predictive” Analytics, “Contract” AI, “Agentic” AI or Classic Mega-Suites … until 2029

Yesterday we told you that while you need intake and orchestration, you need supplier intelligence, you need predictive analytics, you need AI-based contract analytics, and you need “Agentic” AI that executes (but does not make) decisions, you should not buy it standalone, at least not now, and you definitely shouldn’t buy a classic mega-suite.

While analytics platforms have been traditionally scarce, they’ve been popping up faster than bluebonnets in spring, faster than the weeds in unplanted fields, and faster than Starbucks on an empty corner in Y2K. This is creating severe downward price pressure as well as diluting the average actual functionality as many of these are built on third party white-labelled platforms and, even worse, wrappers on third-party LLMs that are great at conversation, not so great at analytics, and entirely dependent on these third party platforms where accuracy can change overnight on the same prompt, models can change with zero notice, and token pricing can skyrocket with no notice.

Even worse, some of these don’t work reliably because they depend on experimental LLM/AGI models, which are horrendously unreliable at math, and they will inevitably fail. That’s why you should be careful with what you buy from the “new standalone analytics startup” category — many have no real functionality (and won’t last very long because of it) and those with only average run-of-the-mill functionality will soon be available for dimes relative to the dollars they are trying to bill you for today. If you need a solution, get one that was available on the market by 2022 at the latest, as the current generation of LLM/AGI/wrapper solutions started to spring up about 3 years ago.

With regards to contract AI, this has been emerging for quite some time, but the current generation of LLM-powered plays that have been multiplying faster than cane toads in Australia started spiralling out of control about 2 years ago when the proclamations started to be made that your corporate future would be human lawyer free.

After all, clod and chat, j’ai pété can review a contract, spit out a summary, and (purportedly) tell you if any clauses are missing in a matter of seconds. Also, if you give it enough (historical) contracts and a few instructions, it can also draft a contract for you in seconds, even if you need a hundred page monstrosity. Who needs a lawyer?

Well, you. These are not infallible, and they make mistakes all the time. Often, they are minor, and easily fixed by a contract or legal expert with a quick review, but sometimes they aren’t so minor. Sometimes the omissions or clause errors are so major that it takes a lawyer longer to rewrite than if they just had their paralegal assemble the draft by hand and they dotted the i’s and crossed the t’s.

In other words, since you get results that are just as good using your own low-cost LLM subscription, it’s not worth a large price tag for a “Contract AI” that just wraps someone else’s tech. Especially when it’s going to get a lot cheaper and have no value unless embedded in the sourcing and supplier management applications you use everyday where all of the embedded supplier and agreement data need to complete the contract is embedded in the application. Wait a year or two and you’ll get it for a dime on today’s dollar.

To be continued …

Now is NOT A Great Time to Buy (Part 1)

Standalone “Intake to Nowhere”, “Classic Onboarding and Supplier Management”, “Predictive” Analytics, “Contract” AI, “Agentic” AI or Classic Mega-Suites … until 2029

Yes you need intake and orchestration.

Yes you need supplier management.

Yes you need predictive analytics.

Yes you need AI-based contract analytics.

And, yes, you definitely need “Agentic” AI that executes (but does not make) decisions.

And if you’re a (mid) mid-market or larger, you need a suite.

But you should not buy any of these products, at least not now.

Why?

The majority of intake and orchestration platforms, and especially the ones that have raised nine figure VC (i.e. 100M or more), are overpriced and under featured. Modern intake and orchestration that modernizes interfaces, simplifies workflows, optimizes clicks, and makes process adherence easier than doing it any other way is valuable, but not that valuable. It’s essentially better middleware 3.0 for the web, and very easy to replicate. There might only be a few players now, relatively speaking, but you’re about to see a slew of these players who literally do nothing but put very nice lipstick on a big fat prize-winning pig (who’s not happy about it), which is a lot easier to do than raising the prize-winning pig from a piglet to a prize winner. At the end of the day, you need actual functionality, and that’s where the real value lies. (And today, that means shilling out for a classic mega-suite to layer the fancy new intake platform on top of, which costs you more, not less.)

As competition heats up, and technology advances, this will be the easiest, cheapest, play and you’ll soon find out that what the big players claim is worth 1M is only worth 100K to 250K, and you still need to go spend that 1M+ on traditional platforms that have deep functionality. So either buy a platform with real functionality with orchestration built in, or wait another year or two. It’s gonna get cheaper and better for standalone I2O. The market has not yet been zipped up.

Supplier Management is very important, but the vast majority of the one hundred plus (yes, that’s 100+) solutions on the market — be they best-of-breed, suite-modules, or hybrid data/contract/content management solutions with a supplier focus — are no longer up to the task. As per our recent post on supplier management must be continuous and proactive throughout the supply chain, these classic point-in-time solutions are no longer up to the challenge of modern supplier management as they can’t even detect changes in supplier behaviour in real-time that are indicative of emerging risks, yet alone supply chain events that are going to seriously impact supplier behaviour in short order.

As a result, they aren’t worth very much. Moreover, if you look at what classic onboarding applications do, they collect data that they force the supplier to enter during the onboarding process — data that, for the most part, is publicly available on the supplier’s web site, third party registries, and other (internal) enterprise systems — data that could easily be consolidated and pre-filled by modern RPA / Agentic applications that would not only allow a supplier to “onboard” in a fraction of the time by pre-gathering all of the fields. In other words, modern (A)RPA and Agentic data gathering applications do the work of classic onboarding, and with their prolific propagation, do it cheaper and put serious downward cost pressure on these classic applications. Moreover, these next generation solutions, which are dropping in price as well as they are becoming a dime a dozen, can continuously monitor these data sources, detect data changes, and (queue) update(s) in real time. Plus, they can accept feeds from supply chain systems and correlate events that might be meaningful.

So, if you’re still in the situation that can survive off of a last generation solution, wait a year or so, and get it for pennies on today’s dollar. But if you can’t wait, make sure you get a modern solution that can monitor supplier and related supply chain changes — it won’t cost more, and if you don’t lock into a long-term subscription, you’ll be able to keep costs way down on renewal (or easily switch to another platform with the same functionality for a lower cost in a year or two).

To be continued …

SaaS Discounts are Lies and Other Common Tricks and Traps You SHOULD NOT Fall For!

(These are also signals that you should run for the hills at their first utterance.)

In our last post on the subject we told you that If A SaaS Provider Offers You a 95% Discount you should

Slam the door, lock it; close the shutters, bolt them; don’t answer the phones, and rip the cables out of the wall; turn on the frequency jamming, and throw the cell phones in the Faraday cage; close the gates to the parking lot, and man security 24 hours. Because, no matter what they told you, the discount meant one of two things:

  1. the provider was trying to rip you off or
  2. the provider is in serious financial difficulty

And both are reasons NOT to do business with the provider.

Unfortunately these aren’t the only tricks and traps you have to watch out for. Other common tricks and traps include:

  • 1. We will give you a 50% discount off of standard prices if you don’t do a bid and just award us the contract without going to market.
  • 2A. Since we lost the bid, you can have it for a 95% discount and a right to use your logo on our webpage …
  • 2B. … but note that, once the contract is signed, we have to right to reprice your entire enterprise deal based on the total number of associated members [including janitors, advisors, and part time contractors who will never use the software] in your organization on LinkedIn (if we’re charging by the seat) and/or average daily use in the prior month (based on CPU cycles and storage against our chosen enterprise averages). [This will probably quadruple the quote within a few months.]
  • 3. If you [still] don’t select us after we drop our price (multiple times), we will go straight to the CFO/CEO of your company to tell them YOU are an incompetent fool bribed by our competitor who is making a huge mistake.

Before you even think twice about their offer, you need to remember that expecting them to treat you well as a client after you sign the contract is akin to expecting your abusive significant other who beats you regularly in drunken fits to all of a sudden stop once you get married. (And yes, I went there. It’s the same rationalization. As per my last post, if they give you this much of a discount, they’re losing money until they can trigger price escalation clauses or change orders, and even then they might not break even on your account. As a result, it will be too costly for them to give you any support whatsoever and, thus, they will ignore you the majority of the time and treat you poorly when they do respond.)

While I shouldn’t have to state this again, all of these situations happen way too often in our industry when companies are struggling (due to taking too much investment at too high of a valuation which resulted in angry investors breathing down their neck with nooses in one hand and pitchforks in the other when they didn’t make ridiculous targets) or they hire that 1/20 pathological salesperson (with a great close record at his last job) who only cares about his* year end bonus and not about whether or not you actually get served once you’ve paid the bill.

* Yes I’m being sexist here as a man is 3 times as likely to be psychopath than a woman, and a salesperson in enterprise software is 2 times as likely to be a man. This which means that your chances of a being ripped off are at least 6 times higher (and I’d argue more) if the salesperson is a man. (I can’t speak for everyone, but like many who have been in the enterprise software space for 30 years, I’ve encountered my share of sleaze-bags and grifters, and, as you might have guessed, every single one of them has been a man — and, FYI, they don’t think much of technical people either!)

Phil’s new HfS Services-as-Software FlyWheel Is Right On the Mark From a Customer-Centric Viewpoint

… but hides the full support required on the back-end!

This is important to point out for two reasons:

  • Gen-AI Hype-mongers will use this as another excuse to claim most white-collar functions will be entirely eliminated when, in fact, it strengthens the need for true back-office white-collar workers and real software engineers
  • Expert human support becomes more critical at each stage of the process (while bit pushers became less and less useful)

But let’s backup. In his most recent piece where he (re-)introduced the SaS Flywheel, Phil made one critical statement which is constantly overlooked by the industry: Stop treating FDE as optional: Your AI Flywheel will not spin without it.

As Phil astutely points out: the hard question nobody is answering is this: who actually wires AI into your live systems, governs it in production, and makes it keep working when the AI software vendors leave the room. The answer is, of course, your Forward Deployed Engineer (FDE) — and if your transformation strategy does not have it, you are building an AI theatre, not an AI operating model. (Which, FYI, is what most companies are building — and, as Stephen Klein astutely points out, putting on puppet shows. Great for entertainment, but not so great for getting anything done. Especially since they all overlook what AI can actually do.)

Now, a forward deployed engineer alone will not get you out of pilot purgatory, but it is an essential condition — just like you can’t climb out of a deep wide hole with smooth 90° vertical surfaces on all sides without a rope or a ladder, you can’t fly your way out of a pilot without a working plane, which you don’t have without an engineer to keep it running.

As Phil continues, FDE is not implementation – it is the engineering layer that makes AI governable this is because FDE teams build ontologies that reflect how the enterprise actually operates, wire models into real data with real permissions, and design the governance architecture that keeps autonomous systems accountable, which will, and for quite some time into the future, wire in non-overridable human oversight, approval, and review.

Phil goes on to list a few key things that LLMs cannot do on their own. (It’s in no way a complete list, but hopefully enough to get executives questioning all the AI-BS form the AI-Hype-mongers presenting grandiose claims that likely won’t be a reality within most of our professional life-times. Even better, Phil points out that Agentic AI without FDE governance is not transformation. It is risk accumulation!, and points out five key requirements of workable AI that can’t be achieved without an FDE. (There are more, but again, these should be enough key points to help executives realize that not only are LLMs sorely insufficient for almost every task they are being promoted for, but they aren’t even usable at all without the help of a FDE team.)

Phil also does us a great service by pointing out that while vibe coding creates velocity, FDE prevents it from becoming chaos — which is what happens every single time you employe vibe coding without FDEs (and a real engineering team — but we’ll get to that).

Vibe coding is simultaneously one of the biggest boons to software development and the greatest destructors, especially since it is almost universally misunderstood and misapplied. For example, while Phil’s statement that business analysts can express intent and receive working agent code in return is technically correct, it’s not practically correct. That’s because vibe coding produces code that is insecure, inefficient, and not appropriate for enterprise software. In fact, just about every startup that tried to launch an enterprise app on vibe-coding alone have lost hundreds of thousands (or more) attempting to do so — see this great post from Alex Turnbull.

Vibe Coding is super useful because, with the help of an FDE team with a good business analyst, the end user organization can quickly create functional prototypes that demonstrate precisely what they are looking for, which are much more powerful functional specifications than traditional functional specification documents with text descriptions of required functionality and powerpoint mockups. Plus, these prototype specifications can be created in a fraction of the time. But that’s all they are, prototypes. Real applications still need to be built by real software engineering teams who can build optimized, bug-free, secure code — vs. unoptimized, buggy (especially at the boundaries), and insecure code regularly generated by AI-based vibe coding tools (where, depending on what source you access, 53% to 78% of code generated has serious security issues).

In other words, it’s a great article, from a customer-centric viewpoint and written for customer executives. From a back-end, provider perspective, it’s missing one key step — the development step that takes vibe coding prototypes and produces real (AI-backed) enterprise applications.

Moreover, it centralizes the FDE activities when, in reality, they are ongoing throughout the entire cycle.

  1. they activate, and put the foundation in place
  2. they train the users on how to properly use the LLMs for accelerated research and are always on call for help
  3. they maintain the orchestration layer, and improve (and correct) it as necessary
  4. they work with the end users to vibe code prototypes
  5. they work with the development team to build the next generation (or iteration) of the enterprise apps in the SaS model

In other words, AI can enhance SaS, but it cannot replace the need for skilled humans on the provider side (for development, implementation, maintenance, and improvement) or the buyer side (for process definition, improvement, decision criteria, etc.).

At the end of the day, AI can only replace bit-pushers who do tactical data processing tasks which should have been automated by machines 30 years ago (when it was promised), but it can’t replace anyone who needs to make a (strategic) decision. This is true regardless of the model, and the right model, like Phil’s SaS flywheel, actually exemplify the need for the right, skilled, talent.