Category Archives: SaaS

The Proliferation of AI-Generated Content Guised As Research is Damaging Our Space!

Real Research Requires Real Human Intelligence and Effort

(I’m not here to be nice. I’m here to educated and inform. Something most sites, including LinkedIn, are doing very little of lately!)

Joël Collin-Demers recently made the understatement of the year when he said 15 functionalities comparing ZIP to Jaggaer isn’t analysis/comparison, it’s pattern-matching by an LLM with no domain context. At best it’s unhelpful. At worst it points procurement leaders toward the wrong tools entirely in response to, with no due respect, a complete crock of AI sh!t published by TEEM.Finance (and reported by a TEEM member who claims instant supplier sourcing & portfolio analysis, with AI# in the tagline, which is another crock of AI sh!t that I must also address).

First of all, at best someone selects an inferior product, wastes a lot of time and money, and ends up in a situation where they are still limping along trying to get basic tasks done with yet another platform that doesn’t come close to delivering on its promise while doing nothing to deliver an increased return on the large amount of money spent on SaaS supposed to solve the organization’s Procurement pain.

At worst, it points the buyer to a product that costs five times as much, doesn’t even accomplish core use cases (if the product works at all outside the demo lab), and results in an absolute disaster upon implementation (with next to zero adoption and more bypass than the organization has ever seen due to the lack of core capability) that results in the organization having to issue another RFP and go through the whole process again with a jaded and angry employee base who expects nothing good will come of it.

The danger of a poor Procurement product pick cannot be understated or underestimated. Nothing will cripple an overworked and under-resourced Procurement department faster than a bad platform (and doubly so if it contains [Autonomous] Gen-AI)!

So, with so many bad product comparisons and maps out there (including Gartner’s and Forrester’s), which I have tackled repeatedly on Sourcing Innovation, why the need to target this one? Because while Gartner and Forrester can be relied on to give you the generally best bet from among their customers which have been confirmed to have relatively equal core functionality,

  1. a random comparison between two different players based on a mere 15 data points that are randomly selected and called “use cases” only guarantees they both exist in the Source-to-Pay space,
  2. any use of AI is flawed from the get-go,
  3. and any comparison that scores Zip 94% and Jaggaer 100% is obviously a complete and utter crock of AI generated sh!t

Let’s revisit Joel’s comment where he calls out Solution Map (which Hackett will hopefully keep).

  • Over 500 clearly defined functions are scored on a scale of technical progression (from 0 to 5). Not 50. And definitely not 15!
  • A 100% based on TODAY’S known Best-In-Class functionality would require a Solution Map score of 4.0. Most suites averaged in the 2.5 to 3 range (average to slightly above). Jaggaer is no exception (and Zip is still far from a suite, it’s I2O slowly adding baseline procurement capabilities, not S2P). (Remember, I DESIGNED the core Sourcing, Supplier Management, Analytics, and Contract Management [this one joint with Pierre Mitchell] maps and DESIGNED the common core across all the maps for Solution Map 2.0. And I scored them for 7 years.)
  • They DO NOT cover everything … there’s always innovation, and always edge cases we ignored (as the goal was to produce a useful map for the majority).
  • They were TECH and CUSTOMER SATISFACTION only. And you need to assess more than that to select a vendor (as per our Successful Vendor Selection series). (And, sometimes, you have to figure out what you should even be looking at, which is why I penned a 39 part series to walk you though the thought process (and Joel, stop complaining about having to write an 8,500 word series on P2P functional requirements … you’re just getting started).
  • And they compared apples-to-apples. This report compares apple-to-oranges, as it’s conclusions are “choose JAGGAER ONE if your organization manages direct materials, manufactures products, or operates in a heavily regulated sector” or “choose ZipHQ if your procurement team needs to configure complex approval workflows across IT, Legal, and Finance without technical resources“, which effectively boils down to “choose Jaggaer if you need Source-to-Pay, and “choose Zip if you need Intake to Orchestration” which is a recommendation that DOES NOT require you to read a report to figure out. All you need to know is
    1. Jaggaer is Source to Pay.
    2. Zip is Intake to Orchestration

    and the answer becomes pretty f*ck!ng obvious!

In order to be useful, at a bare minimum, this is what a comparison needs to do. Define the product domain being compared. Identify the extent of core, should have, and nice-to-have functions required by a product to support the product domain (based on standard functionality and domain use cases). Create a maturity definition for each function. And then use HUMAN INTELLIGENCE to score each product selected for inclusion (on actual demos from the vendor or willing partners and/or current customers). Not bullsh!t Gen-AI that can be fooled by bullcr@p marketing!

Anything less is not a meaningful product comparison. It’s simply an exploration against a few points of interest.

Now, if that’s human led, that can be useful as supplementary material in a decision. After all, the Solution Map will merely grade functionality like flexible workflow configuration on a standard scale but won’t track specifics of how it’s done, how user friendly vs. partner friendly vs. vendor friendly the configuration is, actual customer use cases where the workflows had to intersect 3 or more departments and average customer sentiment on that feature, or provide any other color that might help you make a decision when two solutions look acceptable from a technical and customer satisfaction perspective.

So, if TEEM.finance or someone else wanted to hand pick the most common / relevant use cases, dive in, do a human review, and present their analysis as key points to consider — that would be awesome, and a great excuse to keep writing (so long as said writing is NOT turned over to [Gen-]AI)!

After all, I’m not going to do it (because, frankly, I’m not interested in seeing the same old functionality over and over [as I already saw, and wrote, about it all multiple times — and you should be able to access that if you have a Hackett Membership] as most of the suites have done little to upgrade anything in the last few years as they have switched private equity ownership and bled key talent), and neither are most analysts (who have to cover more vendors than most can handle — remember, there are over 700 vendors in our space, and if you don’t believe me, I again refer you to the mega-map of 666 vendors SI compiled for you).

But it has to be a real review, based on a real demo and/or real discussions with customers, and not AI in any way, shape or form. Otherwise, at best, it’s sl0p. At worst, it’s the written word equivalent of toxic waste. And let’s NOT forget that and continue to fight against the use of AI where AI should NOT be used!

Now, as to the other crock of sh!t, namely instant supplier sourcing & portfolio analysis, with AI. There’s no instant. Yes, there are some great tools out there that can identify a list of potentially relevant suppliers in seconds, compared to the weeks of manual searching you might have had to do in the past, and there are tools out there that can automate sourcing ONCE you have identified your precise item needs, your price tolerances, and your pre-vetted supply base … but, guess what, AI CAN NOT DO all the stuff in between, especially if the product (or category) is high-risk, high-complexity, or high-impact (under the Busch-Lamoureux Exact Purchasing Framework).*

You have to vet the supplier. You have to make sure it’s still operating, the license certificates, registrations, and insurance are both real and current, that the products are still offered, that they are real (by getting a sample), that they will suit your needs, and that the supplier is capable of producing the quantity you need in the time-frame you need it in. You then have to qualify the risks and impact, sign off on them, and enter the supplier (and approvals) in the system. Then you have to define the sourcing project, your tolerance, and your conditions for bid acceptance. YOU! Not BS AI!

In other words, there’s nothing instant about it … and for a highly complex product, or category, that could be days or weeks of manual human work even after all the tactical drudgery is automated for you. So, while a tagline that said faster supplier sourcing and portfolio analysis, with AI, would be 100% true, a tagline that says instant is inherently false. (Unless, of course, your risk tolerance is sky high and you don’t care if the worst case scenario hits and destroys your business … so if you’re looking to be the next Eddie Lampert and dismantle a 100+ Billion company [in today’s dollars] in record time, go for it!)

# name and image hidden as I’m not entirely sure it’s not a bot auto-publishing AI slop

* to be totally honest, you can’t even expect AI to be reliable for low-risk, low-complexity, and low-impact products/categories either, but since the impact of the mistakes it’s going to make will probably require less manual effort to clean up than dealing with all of those products manually, you can potentially live with it

You Need Automation. But You Don’t Always Need Agentic and You Almost Never Need Gen-AI!

In a previous post we dove into how analytics must drive source to pay, because most of source to pay should be automated and touch free as most of the source to pay process is straight forward (and capable of being automated for the last decade), non-strategic, and low to medium value.

Strategic Sourcing is an activity that should be focussed on high risk, high complexity, and/or high value categories and occasionally focussed on medium risk, medium complexity, and/or medium value categories where there is incomplete information or insufficient product/category history, atypical turbulence in the market, or highly particular requirements that just came into effect as a result of new regulations. That’s a minority of products/categories, not a majority.

Procurement should only be focussed on significant exceptions. And, with proper, modern, systems with proper e-document integration and exchange, most of the documents should be arriving in standardized digital formats, and most of the processing should, thus, be fully automated. And most of what is non-standard will be PDF in relatively standard formats that LLMs will be able to process to 95% accuracy and only require a few human verifications and field completions. The days of 20 people invoice processing team should be long gone, as the tech, even for standardized PDFs, has been in production by the leading players for over 8 years. Invoice discrepancies can be auto-identified, suppliers auto-notified, suggested corrections auto-included, one-click acceptance emails/screens for the suppliers included, and most contingencies accounted for. Only in the rare situations where suppliers refuse to accept a correction, invoices are in very non-standard or handwritten format, payments don’t go through, etc. should a human need to get involved. However, 95% to 99% of all documents and transactions that flow through Procurement should be 100% automated.

But most of this doesn’t need experimental Agentic AI or Gen-AI. Classic RPA will do just fine. For most of the rest, Adaptive RPA, with a bit of Machine Learning / Auto-Suggestion based on human-based exception processing, will do the trick nicely. If you look closely at current generation (A)RPA, Machine Learning, Optimization, and Predictive Analytics and walk through the full source-to-pay process, there is very little that can’t be automated without Gen-AI LLMs or experimental Agentic Systems. Sourcing — there are many standard (seven step) processes that can be completely automated based on data analysis, data-based risk assessments, goal definitions, and optimization. RFX (including e-Auctions) can be fully automated and, from the time you specify a product/category to source, everything can be automated to the award (including the demand pull/calculation from other systems).

When it comes time to contract, if you have standard templates or a large clause library, the system can automatically create the contract from the template and RFP responses, integrate DocuSign, and auto-execute it. If you don’t, or if you have to use the supplier’s paper, then you might use an LLM to create a draft for human review and/or analyze the supplier’s paper for terms, pricing (to make sure it matches the bid) and potential risks, as well as suggested revisions, before you sign. Gen-AI/LLMs unnecessary, but useful on a point-basis if you don’t have a good historical equivalent of a solution like Coupa Exari or iCertis.

Supplier onboarding can be fully automated with RPA powered dynamic workflows and third party data ingestion, as can risk and compliance analysis — no modern Agentic solutions needed.

Then we get to automatic invoice monitoring and point-based re-orders, receipt creation from inventory integration, and invoice processing in e-Procurement which has all been around for at least a decade. Automated approvals subject to tolerances, rules and pre-approvals — as well as predictive analytics on payments for new or one-time suppliers/orders or (slightly) out-of-tolerance invoices can automate the entire invoice-to-pay process.

We can get through the entire process on best-of-breed, classically oriented, RPA tech with some machine learning that processes human decisions in exception management, alters or augments the rules (and guardrails), and auto-processes the same type of situation next time. We quickly get to 95%+ throughput for any task that should be mostly automated, and a top human employee with BoB (A)RPA solutions and some augmented intelligence packages for analytics and research becomes 10 to 20 times as productive as they would have been in the past.

That’s the real future of Procurement. Small, top-talent teams (mentoring small emerging top-talent teams) doing the work of teams five to ten times their size, doing it better, and delivering more value than anyone would have believed possible with best-of-breed tools. Not error-prone, hallucinatory, agentic systems that work well in demos and a few select categories, and go all over the place in reality (and then try to hide their mistakes like Nick Leeson [who single-handedly collapsed Barings Bank] until they do a modern equivalent of the 2005 J-Com trade and cost you hundreds of millions of dollars on your key billion dollar product line).

So while you need to modernize at all costs, you don’t need to go full Agentic on unproven solutions. Get 90% of the way on tech that has been proven where you can control the automation level until you get comfortable with automation and learn where you can safely hand tightly boxed “decisions” to the machine (where well-defined calculations would determine your decision the majority of the time) and where you can’t. Otherwise, you’ll just end up being another member of the 94% AI failure camp. That’s not a statistic you want to be part of, especially given the cost of this tech today (and the increased cost tomorrow as energy grids start to break and the compute costs for modern AI tech goes through the proverbial roof).

Analytics Must Drive Source-to-Pay, but not necessarily Gen-AI

Xavier recently penned another great piece on Analytics in P2P: From visibility to actionability where he highlighted the failures in analytics in traditional P2P:

  • static, backward looking, spend by category, invoice cycle time, approval rates, compliance rates
  • insights only after transactions are processed, payments are made, and cycles completed
  • late payments multiplying, exceptions accelerating, and supplier risk accumulating
  • lack of operational insight

According to Xavier, P2P can only be modernized if the embedded analytics shift from descriptive to diagnostic.

  • don’t report KPIs, explain the root causes (which approval paths contributed the most to approval time)
  • don’t report exception rates, identify suppliers that consistently cause them
  • don’t report spend anomalies, break it down and identify root causes

It’s a great start, but where it needs to get to is actionability. Xavier begins to address this point by stating the next step is “predictive awareness” where the system anticipates likely outcomes within active processes, such as predicting which invoices are likely to miss payment terms, which requisitions are likely to stall in approval or which suppliers are likely to generate disputes based on current patterns as that allows a Procurement professional to intervene before issues arise.

Finally, Xavier gets to the main point — the real inflection point comes when analytics begin to recommend actions and influence execution paths. Prescriptive analytics in P2P requires tight coupling between insight and control. If analytics identify a high-risk transaction, the system must be able to route it differently, apply additional validation or prompt a specific decision. If analytics detect a low-risk, repetitive transaction, the system must be able to reduce friction without manual intervention.

But it needs to go one step further. It must not only route differently, and apply more controls, but it must still do so automatically based on the diagnostic and predictive analytics. It can’t just apply a “one-size-fits-all” approach for automation and kick every exception out for human processing. You can’t always make the default path smarter because there should be different paths depending on the cost of the purchase, the risk associated with the purchase, the discrepancy between the invoice, goods receipt, PO, and/or contract terms and conditions. You need multiple streams that are auto-selected by predictive analytics that support the right actions given the assessment of the conditions.

The reality is this — except for truly exceptional situations, once you’ve made the decision on what to purchase, procurement should be 100% automated. It’s all e-document exchange, analysis, authorizations, and (payment) transactions. Unless something is really off, a buyer should never be involved once all the workflows, rules, and authorizations are setup.

But this automation should extend back into, and through, source-to-contract. Building on the Busch-Lamoureux Exact Purchasing pocket-cube framework, there are categories that are low risk, low value, and low complexity — you should NOT be buying these manually. “Agentic” automation should be taking care of these for you, considering that even a worst-case screw up will be of little impact. Then there are categories of moderate risk, value, and/or complexity which can be fully automated if all of the necessary data is available and there is a cost and supply history to build on, there are no special situations that need to be taken into account, and a worst-case analysis indicates that even a statistically unlikely “bad buy” will be of minimal impact. These should be 90%+ automated from the decision to buy to the recommended award, with extensive analytics and augmented intelligence for human review. And if the buyer likes the default recommendation, it should be just one click for the process to go from award to e-signed contract.

All of this requires very extensive descriptive, diagnostic, predictive, and actionable analytics and intelligence with extensive, adaptive, robotic process automation ([A]RPA) that can automate everything that should be. The reality is that while everything should be sourced (or exactly purchased), when you have all of the (market) intelligence, the standard processes, and the organizational goals encoded, then there’s no reason that the systems shouldn’t do the majority (or the entirety) of the work for you.

While buyers won’t be replaced by agentic systems (despite the over-hyped BS claims of AI Employees), they will be heavily augmented by them when most categories aren’t complex, risky, or strategic enough to require human review or intervention.

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.