Category Archives: Technology

Solution Smash-Up! PROPHETic Vision or Magic 8-Ball!

A few weeks ago, THE PROPHET, who noted he was often asked about which disparate providers and/or solutions might work well together (as part of his strategy and M&A work), said that the answer(s) always depend on hard dollars and common sense in a recent article on LinkedIn.

He noted that there were questions that could be asked to help make the determination between any two specific providers and/or solutions, which included:

  • From a TAM perspective, will it increase the TAM beyond 1+2=2?
  • Does it add additional ideal customer profiles or elevate the solutions to the C-Suite?
  • Does it open up additional GTM strategies and channels?

… but also noted that you can go beyond just payments with AP (traditionally Treasury and Accounting), and provided five examples of solution smash-ups that were a bit more “radical”. In a nutshell, with only minor paraphrasing, these were:

1. Intake Management, “light” e-Pro, and GPO.

This makes perfect sense — there’s a reason intake pre-dates stand-alone intake solutions (Zycus launched iRequest back in 2015, almost nine years ago), and that’s because intake and e-Pro go well together; adding in the GPO allows the organization to take advantage of better prices for regular purchases and makes sense.

2. Contract Management and Price Compliance.

The whole point of contracts is to lock in commitments, which are useless if not realized. Integrating contract management into a price monitoring solution, be it part of e-Pro or AP or payments, is a great choice.

3. Third-Party Risk and Working Capital Management.

Before a cash outlay, or an agreement thereto, it’s a good idea to understand the risk.

4. Spend Analytics and BOM/Part-Level Management.

Well, this already exists in some specialists — mainly in electronics (think Levadata and SupplyFrame), but other players are popping up in other verticals as well. (Sievo and Scalue do a great job of doing direct material or part analysis; and Scalue’s material categorization is great for direct management.)

5. Solve Supplier Supervison Sheol

A few companies are starting to make good progress here on “on-boarding, 3PRM, cyber, GRC, and ESG in one place”. Think Brooklyn Solutions, for example.

So, 3 for 5 on new ideas for solution smash up.

The real question is, what solutions could we smash-up that that, on an initial analysis, shouldn’t increase the TAM, elevate the sale, or open up obvious new GTM solutions … because that’s the smash-up no one will see coming, that we won’t see twenty new entrants next year (where ten will ultimately fail), and that will create the new unicorn. And for this, we’ll need to extend Source-to-Play further into the enterprise.

Here are three smash-ups that might seem strange on the surface, but if look deep, and innovate, you can see how they might just be one of the next break-out solutions.

A. Payroll, Benefits, CLM, SOW, and Sourcing Optimization

Manage all people-related spend in one application to balance employees vs. contractors vs. services firms to balance cost vs. risk (of knowledge walking out the door, resources not being available, etc.)

B. WIMS, Distributor Marketplace, and central e-Procurement Catalog

Optimize not only inventory balance between the local office/warehouse/retail outlet, central warehouse, and distributors and guide the buyer to the right inventory at the right time, auto-replenishing as needed.

C. MRP, Assembly Line Control, Quality Control, and Order Management

Continuously monitor materials coming in, used, defect rate, and intelligently re-order against an existing contract as needed.

Of course, if you want to be the next magical unicorn, you’ll have to get even more radical. Anyone have an idea for a solution smashup that makes almost no sense on the surface but, if you get radical, could revolutionize the space? (If so, and you need a prescription to help flesh it out, you know who to call.)

Why Do Outsourcing and AI Go So Wrong?

In a recent post on how We Need to Hasten Onshoring and Nearshoring, Jon The Revelator was inspired to ask the following question:

even though outsourcing and AI have merit when properly implemented, why do things go so wrong?

This was after noting, in another post, that we have suffered year-by-year, decade-by-decade disappointment when 80% (and even higher) of initiatives fail to achieve the expected outcome.

Because in both cases [and this assumes the case where the organization is implementing real, classic, traditional AI for a tried-and-true use case and not modern Gen(erative) A(rtificial) I(diocy)], things have gone wrong, and sometimes terribly wrong, on a regular basis.

So, the doctor answered.

Fundamentally, there are two reasons that things consistently go wrong.

The first reason is the same reason things go so wrong when you put an accountant in charge of a major aerospace company or a lawyer in charge of a major hobby gaming company (when the first has zero understanding of aerospace engineering and the second of what games are and what fans want from them).

Like the accountant and the lawyer, they don’t understand their organizational and stakeholder/user needs!

The second major reason is that they don’t understand what these “solutions” actually do and how to properly qualify, select, and implement them. And, most importantly, what to realistically expect from them … and when.

A GPO is not a GPO is not a GPO — these Group Purchasing Organizations specialize by industry and region; and in making an impact by category and usage. They are not everything for everyone.

AI is not AI is not AI (unless it’s all Gen-AI, then it’s all bullcr@p). Until Gen-AI, the doctor was promoting ALL Advanced Sourcing Tech, including properly designed, implemented, and tested AI, because the right AI was as close to a miracle as you’ll get. (And the wrong AI will bankrupt you.) Now, any AI post 2020 is suspect to the nth degree.

Simply stated, the failures are because they all think they can press the big red easy button and throw it over the wall. But you can’t manage what you don’t understand! And until the world remembers this, these failures will continue to happen on a consistent basis.

And, as organizations continue to press that Gen-AI powered “easy” button while outsourcing more and more of their critical operations, expect to see a resurgence of the big supply chain disasters, like the ones we saw in the 90s and the 00s (including the ones which wiped out Billion $ companies). Hard to believe that only nine years ago the doctor was worried about companies relying on outdated ERPs ending up in the supply chain disaster record books, given how many of the disasters were the result of a big-bang ERP implementation. However, the risks associated with Gen-AI makes ERP risks look like training wheel risks!

As a result, it’s more critical that you select the right provider and / or the right solution if you want a decent chance of success. (The worst part of all this is that while there have been spectacular failures, most of the failures were not the result of selecting a bad provider or a bad solution, but the result of selecting the wrong provider or the wrong solution for you. (Remember, provider sales people are not incentivized to qualify clients for appropriateness, they are incentivized to sell. It’s your job to qualify them for you. In other words, even though there are bad providers and bad solutions out there, they are considerably fewer than there were in the days when Silicon Snake Oil was all the rage.) In the majority of failures, primarily those that weren’t spectacular failures, the providers were good providers with good people, but when the solution they offer is a square peg for your smaller round hole, what should be expected?

Proper Solution Selection is Harder Than You Think!

In Jon The Revelator‘s recent post on what can 2005 tell us about Procurement AI in 2024 he listed a dozen vendors from 2004 that no longer exist and asked if we recognized these names. To this, the doctor replied every single one and noted that the market is even more fragmented today than it was in 2004 and pointed you to the Source-to-Pay+ Mega-Map. Jon then asked if history will repeat itself, and as per the doctor‘s recent post on Market Madness, it will … with a vengeance!

This response prompted The Revelator to ask which companies would join their brethren from 2004, to which the doctor provided some indications, which were many (and even more numerous in the Market Madness post). So The Revelator then asked what do practitioners need to do during these pending turbulent times? The real answer is quite a bit and, in fact too much to address in a single article, or even a book, so the doctor decided to focus in on stable solution selection.

And while the doctor made it look as easy as 1, 2, 3 in his comment, when he said:

  1. first identify what kind of solution you need
  2. then identify which providers actually offer those solutions for their geography – market size – vertical
  3. then restrict down to those that are *stable*

It’s a lot more complicated than that, and for some companies, some of these steps will consist of many steps within themselves.

What kind of solution is complicated! At a minimum, one needs to consider:

  • what processes are you doing
  • … and which of these are properly, or not, supported by your current tech
  • what processes should you be doing
  • … and what tech will support those
  • and which subsets of tech are the most relevant (and make sense to focus on)

Which providers is harder.

  • many providers will claim to be everything to everyone, but that’s not true
  • the big analyst firms over-focus on the big vendors, because that’s who they have to (contractually) spend most of their time on
  • smaller firms will focus on the smaller vendors, because some of the big ones believe their big cheque to the big firm(s) covers all their marketing/market needs, and may not have the time to dive deep into geography – market size – vertical appropriateness
  • and logo maps don’t give you near enough detail to even get a short list

In other words, it’s a heck of a lot more than just choosing the first 5 names that come back in a Google or a “chat, j’ai pété” search!

You want a vendor that is going to be around, or if acquired, a solution that is going to be maintained because it’s growing year-over-year, wasn’t built on an oversized investment (pressuring the firm to increase prices or cut costs or grow too fast), 10+ to 50+ customers (depending on solution type and implementation / replacement time and cost and risk tolerance), etc. Where do you get that data? How do you ask in a way that won’t result in the sale person clamming up?

It’s more than most Procurement organization’s can handle as they just don’t have the TQ (Technical Quotient) or the market knowledge. They need to get help from an expert who does who is not biased towards any particular vendor and will follow a proper process, not just throw an RFP over the wall to three providers they have worked with before (as that’s no better than a refined “chat, j’ai pété” search)! And it can be hard to identify the right expert (and the only hint the doctor will give you now is you’re less likely to find one at a random Big X or Mid-Sized Consultancy — some of the Big X, especially those that have been acquiring expert AI and Analytics firms over the past few years, and mid-sized consultancies have them, but these experts are few and far between, spread thin, and unless you are a Fortune 500 / Global 3000, at most of these firms you will be fighting for the senior expert’s time). You might just need a niche consultancy with experts who specialize in this. There are a few, but not as many as the space needs.  [Take into account when you should use a Big X and that it is up to you to properly specify the project, evaluate the proposal, and vet the personnel proposed.  Otherwise, it’s your failure.]

Have We Been In The Dank Basement So Long That We Don’t Care If the Fish Stinks?

the doctor has to ask because when Jon The Revelator asked if you would eat a piece of fish that has been in your freezer for 10 years? 5 years? 1 year? not many of you spoke up and it seems you are quite okay with old, smelly fish, which, in this case was a metaphor for provider case studies, as this was a follow up to The Revelator‘s post that asked Should Solution Provider Case Studies Have a Best Before Date.

A question, which was in turn sparked from a comment by Duncan Jones to his preceding inquiry on what can 2005 tell us on why most AI initiatives fail in 2024, which is a question that was partially sparked off of a post the doctor himself made on how we need to hasten onshoring and nearshoring — the drivers will pound those who don’t into the ground! (Part 2).

While this sounds like a long, meandering, pointless introduction, it’s exactly the opposite. The purpose is to demonstrate that not only are many parts of Procurement and Supply Chain connected, but they are connected in complex ways that require sufficiently broad, as well as sufficiently deep, solutions that address the complexities being experienced by the organizations a vendor is trying to sell to.

Furthermore, this means that for an organization, or a consulting partner, to select the right solution, they need deep information on what the solution does, where it’s been used, and what it has been proven to do. Traditionally, this would mean that they would require product sheets and demos, customer references, and case studies to make a good decision.

However, centering in on this last requirement, not all case studies are created equal, and not all are even “case studies” at all. What once was the domain of third party analysts, consultants, and professors (who would do proper due diligence, data collection, and impartial write-ups for educational and investment purposes) has now become the domain of marketers who get happy customers, often still wearing the rose-coloured glasses that came free with the install, to tell a story that they write-up and promote using very little, and often unverified, data. Those are not useful at all. Furthermore, if you don’t know what version of the software, what stack the customer ran on, and/or, and sometimes most importantly, when the study was done (and the time period it was done over), is it even still relevant at all?

This prompted the critical question from The Revelator about whether or not studies should have a best before date. the doctor leans towards no on best before date, because just like different types of fish have a different shelf life, different case studies will have a different shelf life, but votes a most definite yes on a packaging date.

To elaborate on the comment he made when asked, the following is absolutely critical to be included in the case study:

  • when the case study was written (packaging date)
  • the time period it was over (processing dates)
  • the precise metrics that were tracked and how they were computed (labelling compliance)
  • the extent of organizational data that was used (ingredients)
    [as well as the full extent of data available (may contain)]
  • the products, and versions, that were used (processing)

In other words, a feel-good story with a few random numbers is not case study! (the doctor would say any marketer trying to pass such off as one should be ashamed, but any marketer who did would obviously be without shame, so there’s really no point in saying it.) A case study has rigour in definition, methodology, data collection, and exposition and contains all the information that would be needed if a third party wanted to repeat it. (The same way a scientific study provides enough detail for an independent team to verify it.) Anything less should be considered unacceptable.

And, most importantly, since business processes, products, systems, and stacks continually change, a study (processing) date and a publication (packaging) date MUST be included so that a buyer can make an informed decision as to whether that study is still relevant to them (as they decide just how much stink they are willing to tolerate).

More Valid Uses for Gen-AI … this time IN Procurement!

Some of you were upset that my last post on Valid Uses for Gen-AI weren’t very Procurement centric, arguing that there were valid uses for Gen-AI in Procurement and that the doctor should have focussed on, or at least included, those because why else would almost every vendor and their dog be including “AI” front and center on their web-site (about 85%+)!

Well, you’re right! To be completely fair, the doctor should acknowledge these valid uses, even if they are very few and very far between. So he will. Those of you following him closely will note that he mentioned some of these in his comment on LinkedIn to Sarah Scudder’s post on how “AI is a buzzword“.

AI is a lot more than a buzzword, but let’s give Gen-AI it’s due … in Procurement … first.

With Gen-AI you can:

1. Create a “you” chat-bot capable of responding to a number of free-form requests that can be mapped to standard types.
This is especially useful if the organization employs one or more annoying employees who always waits too long to request goods and then, after you place the order, insist on emailing you every day to ask “are they here yet” in reference to their request, even though you flat out told them the boats are coming by ship, it takes 24 days to sail the goods across the ocean once they are on the ship, typically 3 days to get them to the port, 3 to 14 days to get them on that ship, 3 to 7 days to get the ship into a dock, 3 to 4 days to unload the ship, and 3 to 4 days from the fort, for a minimum delivery time of 35 days, or 5 weeks, and asking week one just shows how stupid this employee is.

2. Similarly, you can create a “you” chatbot for RFP Question Response.
More specifically, you can create a bot that can simply regurgitate the answers to sales people who won’t read the spec and insist on emailing you on a daily basis with questions you already answered, and which they would realize if they weren’t so damn lazy and just read the full RFP.

3. Create meaningless RFPs from random “spec sheets”.
Specifically, take all those random “spec sheets” the organizational stakeholder downloaded from the internet just so you can check a box, send it out, and make him happy. (Even though no good RFP ever resulted from using vendor RFP templates or spec sheets.) Which is especially useless if you have a subscription with a big analyst firm that includes helping you identify the top 5 vendors you are going to invite to the RFP where you will focus on the service, integration, implementation, and relationship aspects as the analyst firm qualified the tech will meet your needs. (After all, sales, marketing, human resources, and other non-technical buyers love to be helpful in this way and don’t realize that just about every “sales automation”, “content management”, and “application system” has all of the same core features and you can usually make do with any one of a dozen or more low-cost “consumerized” freeware/shareware/pay-per-user SaaS subscriptions.)

4. Or, do something slightly more useful and auto-fill your RFPs with vendor-ish data.
You could use the AI to ingest ALL of a vendor’s website, marketing, and sales materials as well as third party summaries and reviews and auto-fill as much of your RFP as you can before sending it to the vendor, and then approximately score each field based on key words, to ensure that the vendor is likely capable of meeting all of your minimum requirements across the board before you ask them to fill out the RFP and, more importantly, spend hours, or days, reviewing their response.

5. Identify unusual or risky requests or clauses in a “ready to go” contract.
Compare the contract draft handed to you by the helpful stakeholder to the default ones in your library that were (co-)drafted by actual Procurement professionals and vetted by Legal and don’t have unusual, risky, or just plain stupid clauses. For example, an unvetted draft could have a clause that says your organization accepts all liability risk, you agree to pay before goods are even shipped, you’ll accept substitute SKUs without verification, etc. (because the helpful stakeholder just took the vendor’s suggested one-sided contract and handed it to you).

6. Automatic out-of-policy request denial.
Program it to just say “denied” for any request that doesn’t fall close to organizational norms.

7. Generate Kindergarten level summaries of standard reports for the C-Suite.
Got a C-suite full of bankers, accountants, and lawyers who don’t have a clue what the business actually does and need simplified reports translated to banker-speak and legalese? No problem!

Of course, the real question is to ask not what Gen-AI can do for you but what can you do without Gen-AI because the doctor would argue that you don’t need Gen-AI for any of this and that the non-Gen-AI solutions are better and more economical!

Let’s take these valid uses one-by-one:

1. You could hire a virtual admin assistant / AP clerk in the Phillippines, Thailand, or some other developing country with okay English skills to do that for 1K a month!
Furthermore, this full time worker could also respond to other, more generic, requests as well, and do some meaningful work, such as properly transcribing hand-written invoices (or correcting OCR errors), etc. And give your employees the comfort of a real, dependable, human for a fraction of the cost of that overpriced AI bullsh!t they are trying to shove down your throat.

2. Classic “AI” that works on key phrases in the hands of the admin assistant will work just as well.
It will find the most appropriate data, and then the admin can verify that the question can be answered by the paragraph(s) included in the RFP, or that the sales person actually read the RFP and is asking for a clarification on the text, or a more detailed specification. The sales person gets the desired response the first time, no time is wasted, and you haven’t p!ssed off the sales person by forcing him to interact with an artificially idiotic bot.

3. When they said the best things in life are free, they weren’t referring to vendor RFPs.
In fact, those free RFPs and spec sheets will be the most expensive documents you ever handle. Every single one was designed to lock you into the vendor’s solution because every single one focussed not on what a customer needed, but the capabilities and, most importantly, features that were most unique to the vendor. So if you use those RFPs and sheets, you will end up selecting that vendor, be that vendor right, or wrong, for you. The best RFPs and spec sheets are the ones created by you, or at least an independent consultant or analyst working in your best interest. No AI can do this — only an intelligent human that can do a proper needs, platform, and gap analysis and translate that into proper requirements.

4. Okay, you need AI for this … but … traditional, now classic, AI could do that quite well.
Modern Gen-AI doesn’t do any better, and the amount of human verified documents and data you need to sufficiently train the new LLMs to be as accurate as traditional, now classic, AI, is more than all but a handful of organizations have. So you’re going to pay more (both for the tech and the compute time) to get less. Why? In what world does that make sense?

5. Okay, you need NLP at a minimum for this, but you don’t need more. And you barely need AI.
All you have to do is is use classical NLP to identify clause types, do weighted comparisons to standard clauses, analyze sentence structures and gauge intent, and identify clauses that are missing, deviating from standard, and not present in standard contracts. And, as per our last use, do it just as well without needing nearly as much data to effectively train. Leading contracts analytics vendors have been doing this for over a decade.

6. Even first generation e-Procurement platforms could encode rules for auto-approval, auto-denial, and conditional workflows.
In other words, you just need the rules-based automation that we’ve had for decades. And every e-Procurement, Catalog Management, and Tail Spend application does this.

7. Any semi-modern reporting or analytics platforms can allow the templates to be customized to any level of detail or summary desired.
And if you have a modern spend analysis platform, this is super easy. Furthermore, if your C-Suite is filled entirely with accountants, bankers, and lawyers who don’t understand what the business does, because they fired all the STEM professionals who understood what the business actually does, then your organization has a much bigger problem than reporting.

In other words, there isn’t a single use case where you actually need Gen-AI, as traditional approaches not only get the job done in each of these situations, but traditional approaches do it better, cheaper, and more reliably with zero chance of hallucination.

At the end of the day you want a real solution that solves a real problem. And the best way to identify such a solution is to remember that Gen-AI is really short for GENerated Artificial Idiocy. So if you want a real solution that solves a real problem, simply avoid any solution that puts AI first. This way you won’t get a “solution” that is:

  • Artificial Idiocy enabled
  • Artificial Idiocy backed
  • Artificial Idiocy enhanced
  • Artificial Idiocy driven

As Sarah Scudder noted on “AI is a buzzword“, AI is a delivery mechanism which, scientifically speaking, is a method by which the virus spreads itself. This is probably the best non-technical description of what AI is ever! And the best explanation of why you should never trust AI!