Category Archives: SaaS

Exact Purchasing Helps You Define Your Tech Needs

In our last post we illuminated how Busch-Lamoureux Exact Purchasing required Category Intelligence (not just the Category Management most Procurement organizations aren’t yet doing) and, thus, ups your Procurement game in more ways than one. Many more ways than one, actually.

Only with Exact Purchasing can you figure out what you actually need from your Procurement technology, If you go back to our piece on assisted solution selection is a seven stair methodology, step 1 is understanding your needs. By breaking your procurement needs into categories you can specify to a high degree of detail, you get an understanding of what you really need to do in your Procurement organization.

In the seven stair methodology, step 2 is the holistic solution requirement — and this is what is embodied in Exact Purchasing! With exact purchasing, you are holistically evaluating a category from all key perspectives — complexity, risk, and organizational impact — and creating sourcing, procurement, supplier, and supply management plans that balance the requirements from a holistic requirement.

Step 3 is organizational maturity, and here’s what most Procurement organizations miss — the lack of a proper, formal, category management strategy that allows them to start on the journey to Procurement excellence through better processes, risk management, and intelligence is what holds them back. Exact Purchasing gives them a foundation to not only figure out where they are on their Procurement journey but where they need to go and what process improvements might get them there.

Step 4 is vendor pool selection, and here’s where exact purchasing really starts to help as it helps you identify what the tech has to do, which helps you (possibly with help from an expert analyst or consultant) identify what type of Procurement tech you really need, and then you can use an independent analyst or consultant (who doesn’t have to sycophantically cater to the bejeweled emerald software partner in order to maintain that bejeweled emerald status that sees a lot of integration work thrown his way as long as he maintains it) to identify the vendors most likely to be a great fit for you.

Step 5 is the vendor assessment process, which is itself a 7 step process — and Exact Purchasing helps you out end-to-end here.

Step 5a is RFI creation. With Exact Purchasing, you know what the critical functionality is, you can easily specify what it is, and then quickly eliminate any vendor that can’t meet 100% of the critical must-have for your organization before wasting any significant time on them.

Step 5b is collaborative RFI review. Once you’ve eliminated those that you’re certain won’t fit, if too many vendors survive the cut, the team is educated on both what is needed and what will make their lives easier and can holistically assess initial responses to narrow down to the providers that go beyond the basics in ways that might be helpful.

Step 5c is the qualifying demo. You can create a script that not only covers all the essentials, but should haves that will help illuminate where the key strengths and weaknesses are likely to be both in the given vendor’s application but the vendor pool over all and get the insight you need to ensure that you’re both on the right path and that the vendors you select for the RFP will be worth the next stage review.

Step 5d is the RFP creation. From here you can elaborate all the should have and nice to have functionality, double down on your key pain points that you would like solved (potentially in innovative ways), note what intelligence is critical, identify where you’d like services and support, and identify any must-have organizational requirements beyond Procurement that would be a deal-breaker. You’re able to focus on the what, instead of the typical 500 point feature list where half the features you might never use.

Step 5e is the RFP review, where again the team has the understanding on what to look for and can ensure that any vendor who wouldn’t make the cut doesn’t get invited to the full demo stage.

Step 6 is the full demo where each vendor provides a two-part demo against the basic deep dive should have script the RFP was based on and detailed requests based upon claims they made in the RFP against nice-to-haves, uniqueness, process improvements that will save you time and money beyond what peers can do, etc.

Step 7 is the decision, and you can make that against what the vendors offer relative to your category needs and organizational goals, not just a feature list you don’t understand (but copied from a Free RFP anyway because you needed to look competent).

In other words, Exact Purchasing gives you the understanding you need to go to market, and the understanding that not every solution may be appropriate for every category — and that’s okay. Sometimes two or three targeted BoB solutions are still less than half of the cost of a mega-suite that still only solves half of your problems.

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.