Category Archives: Technology

Have all the Big X fallen for Gen-AI? Or is this their new insidious plan to hook you for life?

Note the Sourcing Innovation Editorial Disclaimers and note this is a very opinionated rant!  Your mileage will vary!  (And not about any firm in particular.)

Almost every single Big X and Mid-Sized Consulting firm  is putting “Gen-AI” adoption in their top 10 (or top 5) strategic imperatives for Procurement, and its future, and that it’s essential for analytics (gasp) and automation (WTF?!?).

It’s absolutely insane. First of all there are almost no valid uses for Gen-AI in business (unless, of course, your corporation is owned by Dr. Evil), and even less valid uses for Gen-AI in Procurement.

Secondly, the “Gen” in “Gen” AI stands for “Generative” which literally means MAKE STUFF UP. It DOES NOT analyze anything. Furthermore, automation is about predictability and consistency, Gen-AI gives you neither! How the heck could you automate anything. You CAN NOT! Automation requires a completely different AI technology built on classical (and predictable) machine learning (where you can accurately calculate confidences and break/stop when the confidence falls below a threshold).

Which begs the question, have their marketers fallen for the Gen-AI marketing bullcr@p hook, line, and sinker? Or is this their new insidious plan to get you on a never-ending work order? After all, when it inevitably fails a few days after implementation, they have their excuses ready to go (which are the same excuses being given by these companies spending tens of millions on marketing) which are the same excuses that have been given to us since Neural Nets were invented: “it just needs more content for training“, “it just needs better prompting“, “it just needs more integration with your internal data sources“, rinse, lather, and repeat … ad infinitum. And, every year it will get a few percentage points better, but if it gets only 2% better per year, and the best Gen-AI instance now is scoring (slightly) less than 34% on the SOTA scale, it will be (at least) 9 (NINE) years before you reach 40% accuracy. In comparison, if you had an intern who only performed a task acceptably 40% of the time, how long would he last? Maybe 3 weeks. But these Big X know that once you sink seven (7) figures on a license, implementation, integration, and custom training, you’re hooked and you will keep pumping in six to seven figures a year even though you should have dropped the smelly rotten Gen-AI hot potato the minute you saw the demo (and asked them for a more traditional enterprise application they can deliver with guaranteed value).

So, maybe they aren’t misled when it comes to Gen-AI. Maybe they are just shrewd financial managers because it’s their biggest opportunity to hook you for life since they convinced you that you should outsource for “labour arbitrage” and “currency exchange” (and not materials / products you can’t get / make at home) and other bullsh!t arguments that no society in the history of the world EVER outsourced for. (EVER!) Because if you install this bullcr@p and get to the point of “sunk cost”, you will continue to sink money into it. And they know it.   Or do they?

In our view, the sad reality is that while one or two financial managers may have gone deep enough down the Gen-AI rabbit hole to figure this out, most of them likely just don’t see the downside for them or their clients.  Given all the hype the creators of these Gen-AI* models are pushing, with prolific examples only of success cases and upside, with very little education on the realities (because few of us are highlighting all of the risks of Gen-AI and failures when misapplied), maybe all they are seeing are promises that are just too good to ignore.

So, please, ignore the Gen-AI until you’ve validated a use case and instead remember When You Should Use Big X. Every solution and services provider has strengths and weaknesses. Please use them for their strengths, be successful, and increase the project success rate. (Post-Edit: As of 2024, technology project failure is at an all-time high. We don’t want to see any more of it!)

*Remember that AI, and Gen-AI in particular, is a fallacy.

Don’t Zip Through the Zip-sponsored Spend Matters authored Intake and Procurement RFP! [2024] (Collected Links)

Don’t Zip Through the Zip-sponsored Spend Matters authored Intake and Procurement RFP!

Please note this is NOT coverage of Zip. See this post for Zip solution coverage!

BONUS

BONUS 2

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.)