Open Gen-AI Isn’t Just Dumbing Your Business, It’s Killing the Planet!

Open Gen-AI is not just one of the most dangerous technologies we’ve ever invented* (as it lulls the uninformed into a false sense of security who will depend on it to make increasingly more critical decisions that could have increasingly more disastrous consequences), it’s also about to pose the biggest threat to planetary survival!

As it is, an average Data Center requires at least 10X the energy consumption of an average American home per square meter, with Open Gen AI data centers (which require ultra dense servers with cores running flat out all the time) requiring even more energy than that. However, whereas traditional AI models, including traditional Deep Learning Neural Nets which can be optimized post-training to often 10% of their original size using techniques developed by MIT researchers (including those described in this article) are now smaller and more stable than they used to be, these models just keep expanding exponentially in a futile quest to have them do more and now require models thousands of times bigger (and more energy intensive) than traditional models, often to generate output that wouldn’t even net a C grade in a high school class!

Think about that and read this article by Kate Crawford on Nature on how AI’s environmental costs are soaring (which notes that even OpenAI’s CEO has finally admitted that the AI industry is heading towards an energy crisis as there just isn’t enough power to keep up with the exponential energy demands [with ChatGPT already requiring more power than 33,000 average American homes … think about that, if you shut down just TWO Open Gen-AI models, you could power an entire small city]) before needlessly throwing a solution you don’t understand at a problem you don’t even have (when a better process would eliminate that problem and replace it with a smaller, different, problem that traditional technology and a human with just a bit of training could completely solve).

Because Open Gen-AI is just NOT ready for prime time, and just because these companies raised Billions of dollars on false promises that it would be ready years or decades sooner than AI development has traditionally taken, that doesn’t make it our responsibility to adopt the technology before it’s ready.

* And if a man afraid of nothing acknowledges this, we really should listen! (See this article.)

A Truly Great Article on Transforming Legacy Procurement

If you’re a new occasional reader, you might think that one of the doctor‘s primary goals is to just rip big analyst firms and publications apart when they publish ridiculous results (based on ridiculous surveys) or ill-conceived articles with little to no good Procurement content (if we’re lucky), or wrong content (if we’re not) that, as far as the doctor is concerned, would have been just as good if they unleashed an intern with no knowledge of procurement on Chat-GPT (and you all know what the doctor thinks of that!).

However, that’s just because, as Procurement is hitting the limelight (as a result of all the supply chain disasters we’ve been facing that they have been expected to deal with), coverage has increased significantly (to capitalize on the hot topic), and most of it is, frankly, NOT that good. However, every now and again there is a truly tremendous article published under the radar, and when the doctor finds one of those, he’s very happy to bring your attention to it. Especially when it’s written by a practitioner who obviously gets it.

In her article on From Tactical to Strategic: Transforming Legacy Procurement, the author reminds us that the majority of large scale transformations fail, that a major challenge for older companies is that they have no comprehensive view into global spend, that e-Procurement systems offer many fixes, but also that if they are not optimized for your specific business needs, you could be missing out on opportunities for better supplier partnerships and cost leadership.

This does not mean that you should build your own (overly) customized system, or insist that the systems support your current processes (before determining if those processes are better than the processes supported out-of-the-box by the new systems that have been developed based on typical best practices of the industries the vendor serves), but that the solution has to be appropriate to your industry and support some customization where you need it for specific products, services, or processes that make your business unique (but only those — don’t reinvent the wheel already there where you’re the same as everyone else).

The author then goes on to outline a three-phase approach to identifying, selecting, implementing, and, most importantly, maximizing adoption of the platform — which is an ultimate key to success.

the doctor highly recommends you read this article on going From Tactical to Strategic: Transforming Legacy Procurement.

Fraud and Waste are Not the Same Thing — And You Cannot Overcome them Equally

A recent article in BusinessDailyAfrica on how firms can overcome fraud and wastage in technology procurement had some good advice, but it missed some key points, especially since you can’t treat fraud and wastage equally if you want to truly combat fraud and wastage in real time.

The article notes that when it comes to the adoption of new technologies, organizations allocate substantial budgets that provide fertile ground for funds to be siphoned through fraud, which is sort of true, but usually what happens is a plethora of change orders and upsells at multiples of what the organization should be paying, which is not fraud when the vendor delivers, but severe wastage.

A bigger concern is, as the article notes, manipulation of procurement processes encompasses practices such as bid rigging or collusion with service providers, kickbacks and bribery, false invoicing, misrepresenting specifications and capabilities of products and services, channelling payments through shell companies solely to facilitate bribery, conflict of interest, and disguising procurements to bypass processes, which has nothing to do with the tech budget, and which happens whether or not the company implements new tech or not, whether the decisions are ill-considered or not, whether the decisions are rushed or not, etc.

The reality is this: if a company has a lot of money and fraudsters believe it, or its processes, can be exploited for fraud, they’ll try. And while adequate planning, centralization of tech decisions, robust implementation of strategies, and controls can curb fraud and wastage, that’s not always enough.

The only way to minimize and prevent fraud is

  1. identify each type of fraud attempt that your organization is likely to get hit with
  2. for each type of fraud,
    1. identify processes that can be exploited, and change them to minimize exploitation
    2. implement specialized technology or algorithms to look for it and alert people to the potential — in real time (before money changes hands)
    3. educate your people on what valid payment requests look like, what typical fraud looks like, and when to ask questions and/or escalate it up the chain (possibly all the way to the CFO if necessary)
    4. anytime a fraud slips through, besides trying to immediately stop-payment, immediately do a post-mortem to figure out the root cause and update the process, technology, or detection methodology; fraudsters are always upping their game, so you need to always be upping yours

And when the doctor says you have to identify and target each type of fraud (scheme/scam) separately, he means it. There’s no one-size-fits-all for fraud, but there are technologies, techniques, and targeted theorem tabulations that can rather reliably progressively prevent frequent frauds.

Nor is it as simple as just throwing a bunch of analytics at the problem, as this recent article that purports to prevent procurement fraud with analytics that was published as a think tank article in SupplyChainBrain (which, as you can guess, really upset the doctor when think tank articles in Supply Chain Brain should be the best of the best and this was barely acceptable). Apparently the doctor will have to include Procurement fraud in his list of topics for his Source-to-Pay+ series because the state of information being provided to you is, for the most part, sorry and sad.

But waste is entirely different. As we alluded above, that typically takes the following forms:

  • frequent change orders during implementation, usually billed at excessively high day rates as they have to “divert resources” or “work overtime”
  • unnecessary customizations or real-time integrations that are an extensive amount of work (and cost) when out-of-the-box or daily flat-file synchs are more than sufficient
  • extensive “process evaluation” or “process transformation” processes that are well beyond what you need to eat up consulting hours
  • extensive “best practice” education when your practices are good enough for now and/or those best practices are already encoded in the system and just following the default process gives you the same education
  • additional seats or licenses you really don’t need (but you are convinced somehow that you do) (which don’t get used and just sit on the v-shelf)
  • etc.

Basically, you go in for a penny, and they take you on a joyride that costs a pound. They deliver the minimum at each step of the way so you can’t technically accuse them of fraud, but they end up making sleazy used car salesmen look good!

Finally! A “Think Tank” Article that Gets It Right!

the doctor has been reading a lot of “think tank” and “thought leader” articles lately that are completely off the mark. Some are so bad that he’s wondering if the publications are paying interns who know nothing about the space to use “chat j’ai pété” (Chat-GPT) to hallucinate content for them. (And, as you’ve seen, some are so bad and/or make him so angry that he just has to rant about them. Our space don’t get no regard at all as it is. The last thing we should be doing is providing anyone who takes the time to read about it with misleading or wrong information).

All that being said, Supply Chain Brain recently published an article on 2024 Predictions: A New Era of Strategic Supply Chain Design by Donald Hicks, the CEO of Optilogic. In it, he makes six predictions for the new era of strategic supply chain design in 2024.

The first five predictions were good.

1. A shift from short-term to strategic thinking.

COVID demonstrated that we’ve reached the end of short-term JIT thinking, and the recent geopolitical turmoil since has only heightened that reality. Any company that wants to survive has to go back to focus on mid-to-long term strategic thinking that will help it mitigate the plethora of risks it is being hit with and assure supply.

2. An end of the age of unlimited cheap suppliers.

Especially since the majority of these were based in China. As the author notes, China-US relations are deteriorating fast and the Chinese economy is underperforming. Moreover, as a result of COVID, logistics are uncertain and considerably more expensive from China (due to less carrier space, as many ships were scrapped during COVID for insurance settlements, and the need to sail around the capes, due to the Red Sea situation and the prolonged Panamanian drought). So, companies need to start looking elsewhere, and since they let their best suppliers in Mexico and South America wither and die, there aren’t many good options at the moment.

3. Demand for vendor transparency.

In addition to customers becoming more discerning, as the author notes, there are more supply chain regulations that need to be adhered to globally, more sustainability regulations, more denied party regulations, and so on. Companies need to know who they’re dealing with; that all supply chain, sustainability, and regulatory requirements are met; and that any desires of its customers can be met.

4. Market turmoil and the rise of new leaders.

This year is projected to witness down rounds, market turmoil, and a reassessment of strategies. Most definitely. VC went too hot and heavy before COVID trying to force unicorns where the foals weren’t even breeding stock, and then lost heavy in the SVB failure; and PE, trying to get a piece of the payments, online collaboration, and/or FinTech market during COVID paid ridiculous multiples for rather basic offerings that weren’t even complete — and that would never demand the price tag the investors expected. As a result, these PE firms are now looking at payback timeframes of a decade or more, if they’re lucky. This means that cash is sparse, investments will be sparser, and some companies (that overspent and can’t get the valuation) will not survive.

5. Digital Twin Skepticism.

Every supply chain technology vendor is clamouring to tell you about their digital twin capability, but the term “digital twin” is a marketing creation that can’t live up to its ambitious name. Companies don’t always have all the data (or quality data) relating to supplier orders and timelines, inventory levels and factory production in separate operational systems, much less a single location.

There’s no digital twin without complete data, and there’s no complete data. Modern manufacturing companies and direct buyers are figuring this out and not falling for outlandish claims anymore.

The sixth prediction was absolutely fantastic!

6. Artificial intelligence exhaustion, and a return to old-school evaluation.

Hear, hear! Smart companies are getting fed up of the ridiculous claims made by new Open/Gen-AI companies and the paltry results that were delivered, if any. They’re also fed up of the high-price tags relative to the limited value they’re received from “AI” so far.

Thus, rather than relying on the mere claim of being AI-enabled, companies should be expected to showcase their capabilities, substantiate their claims with proof, and provide clear reasons for belief, signalling the return to a more traditional approach to purchasing decisions.

Hear, hear!

Another “think tank” article on digitizing procurement that’s off-the-mark!

A recent article in Supply Chain Brain noted that you should be seizing the opportunity for digitizing procurement and the doctor completely agrees. Nothing should be paper based in Procurement today. There’s no excuse for it.

And yes, multiple developments in supply chain are converging to create an unprecedented digital opportunity for procurement professionals. Furthermore, if you work on mastering and combining emerging and maturing technologies in strategic ways since procurement teams are in a position to reshape how they work, and create value across the supply chain, you can revolutionize Procurement and business performance.

But digitizing, by definition, means moving processes from scrolls to systems, from the dark basement to the illuminated screens. It DOES NOT mean that:

  • you use Gen-AI or even machine learning
    there may be tasks where you apply point-based ML, but that comes after the digitization of an appropriate process
  • you use cognification to illuminate (concealed) processes
    especially when it could illuminate you should never have digitized the process in the first place
  • you accelerate workflow through automation
    you automate what you can, and while that includes the acceleration of tactical paperwork processing and thunking, sometimes humans have to step back and think about the data received, insights produced, and options available before making a decision … you don’t accelerate whatever amount of time it takes a human to make a good decision (and, instead, focus on automating and accelerating any non-strategic tactical “thunking” tasks that prevent them from focussing their brain power where it’s really needed)
  • you go straight to content personalization
    when the users might not even know how to use the baseline systems (and, in the process, create a nightmare for the support personnel)

Digitizing Procurement starts by:

  • understanding what processes you are using now
  • understanding if they are appropriate or they should be optimized
  • identifying off-the-shelf best-of-breed modules, mini-suites, suites, and/or
    intake-to-orchestrate platforms and implementing them
  • identifying key points where RPA, ML, or other advanced techs can make the process even more efficient
  • then identifying the right advanced tech to use

Not starting with it. You should never try to run a race before you can walk. The only “impactful opportunity” identified in the article you should start with is

  • adopting ecosystem thinking to enhance data

At the end of the day, nothing works well without good data. So get the data right, and everyone aligned to get the data right, and that will get you further, and help you do better, than any piece of modern tech you can try to throw at the problem.