Category Archives: Technology

The Gen AI Fallacy

For going on 7 (seven) decades, AI cult members have been telling us if they just had more computing power, they’d solve the problem of AI. For going on (seven) 7 decades, they haven’t.

They won’t as long as we don’t fundamentally understand intelligence, the brain, or what is needed to make a computer brain.

Computing will continue to get exponentially more powerful, but it’s not just a matter of more powerful computing. The first AI program had a single core to run on. Today’s AI program have 10,000 core super clusters. The first AI programmer had only his salary and elbow grease to code, and train the model. Today’s AI companies have hundreds of employees and Billions in funding and have spent 200M to train a single model … which told us we should all eat one rock per day upon release to the public. (Which shouldn’t be unexpected as the number of cores we have today powering a single model is still less than the number of neurons in a pond snail.)

Similarly, the “models” will get “better”, relatively speaking (just like deep neural nets got better over time), but if they are not 100% reliable, they can never be used in critical applications, especially when you can’t even reliably predict confidence. (Or, even worse, you can’t even have confidence the result won’t be 100% fabrication.)

When the focus was narrow machine learning/focussed applications and accepting the limitations we had, progress was slow, but it was there, was steady, and the capabilities, and solutions improved yearly.

Now the average “enterprise” solution is decreasing in quality and application, which is going to erase decades of building trust in the cloud and reliable AI.

And that’s the fallacy. Adding more cores and more data just accelerates the capacity for error, not improvement.

Even a smart Google Engineer said so. (Source)

Challenging the Data Foundation ROI Paradigm

Creactives SpA recently published a great article Challenging the ROI Paradigm: Is Calculating ROI on Data Foundation a Valid Measure, which was made even greater by the fact that they are technically a Data Foundation company!

In a nutshell, Creactives is claiming that trying to calculate direct ROI on investments in data quality itself as a standalone business case is absurd. And they are totally right. As they say, the ROI should be calculated based on the total investment in data foundation and the analytics it powers.

The explanation they give cuts straight to the point.

It is as if we demand an ROI from the construction of an industrial shed that ensures the protection of business production but is obviously not directly income-generating. ROI should be calculated based on the total investment, that is, the production machines and the shed.

In other words, there’s no ROI on Clean Data or on Analytics on their own.

And they are entirely correct — and this is true whether you are providing a data foundation for spend analysis, supplier discovery and management, or compliance. If you are not actually doing something with that data that benefits from better data and better foundations, then the ROI of the data foundation is ZERO.

Creactives is helping to bringing to light three fallacies that the doctor sees all the time in this space. (This is very brave of them considering that they are the first data foundation company to admit that their value is zero unless embedded in a process that will require other solutions.)

Fallacy #1. A data cleansing/enrichment solution on its own delivers ROI.

Fallacy #2. You need totally cleansed data before you can deploy a solution.

Fallacy #3. Conversely, you can get ROI from an analytics solution on whatever data you have.

And all of these are, as stated, false!

ROI is generated from analytics on cleansed and enriched data. And that holds true regardless of the type of analytics being performed (spend, process, compliance, risk, discovery, etc.).

And that’s okay, because is a situation where the ROI from both is often exponential, and considerably more than the sum of its parts. Especially since analytics on bad data sometimes delivers a negative return! What the analytics companies don’t tell you is that the quality of the result is fully dependent on the quality, and completeness, of the input. Garbage in, garbage out. (Unless, of course, you are using AI, in which case, especially if Gen-AI is any part of that equation, it’s garbage in, hazardous waste out.)

So compute the return on both. (And it’s easy to partition the ROI by investment. If the data foundation is 60% of the investment, it is responsible for 60% of the return, and the ROI is simply 0.6 Return/Investment.)

Then, find additional analytics-based applications that you can run on the clean data, increase the ROI exponentially (while decreasing the cost of the data foundation in the overall equation), and watch the value of the total solution package soar!

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.]