Category Archives: AI

Not only is AI, Agent, and Agentric AI Old Enough to Retire — But New Startups Touting It Are Like Newborns!

In yesterday’s post we told you that AI and Agents, being touted by all the new startups as the next generation of Procure-and-Fin-Tech applications that will replace your entire Procure-and-Fin-Tech workforce, is nothing new and any new startup saying otherwise is just shovelling sh!t your way as it clears out the marketing stable.

First of all, as we explained yesterday, this is all old tech. In some cases, very old tech.

1) AI, which really means “algorithmic improvement”, and which has been slapped onto every algorithm that was slightly more advanced than the algorithm that came before since 1956, is at least 69 years old. Old enough for one of the founders, Joseph Weizenbaum, to turn against it. (It became Weizenbaum’s Nightmare.)

2) There’s fundamentally no difference between Robotic Process Automation (RPA) and an agent. They both perform actions to produce an effect, so both satisfy the definition of an agent, and RPA dates back to the 1990s.

3) The other hype machine, “orchestration”, is not new either, not even on the web. Tim Berners-Lee invented the Web in 1989, and we had one of the first instances, CORBA, in 1991.

4) All automation is based on workflow, the concept of workflow management dates back to 1921, and workflow was a core capability of MRPs, which predated ERPs, in the 1970s.

And just slapping an LLM-powered chatbot interface on top of old tech (which is the only way you can build a reliable solution) is not innovative. In fact, it is sometimes exnovative and makes things worse!

But this isn’t the biggest problem. The problem is that, to keep up in the digital age (which you all knew was coming since 1995 when Alfred A. Knopf published Being Digital, which was followed by Bill Gates’ first book The Road Ahead later that year), you need to implement solutions that take over your time-consuming tactical, rote, and repeatable number-crunching processes that are best done by what computers were designed for, freeing up your team to focus on the strategic tasks, relationships, and, well, getting things done.

But getting things done is not something that will happen if you adopt a new technology that is nothing but a framework for building an application.

When these vendors claim their platforms can be trained to model your entire process and take over for your current workforce, what the vendors are really saying is they have cobbled together a low-code configurable platform that allows them to build whatever solution you need, provided you can accurately specify what you need.

In other words, you aren’t buying a solution. You’re buying a toolset you can use to build the solution, and if you don’t know what that is, in addition to spending a lot of money on the platform, you’ll be spending 10X as much on consultants to design the solution, configure the platform, and then spend months training the LLM-powered conversational interface to actually do what you want it to do. (The time required will be three times what you expected and the overall cost at least five times as much.)

Even with a low-code, AI-X’d (powered, backed, enhanced, driven, or whatever other meaningless adjective/modifier the vendor slaps on) platform, it will still take months to design and implement a basic solution and years to create a mature one.

Which, FYI, is what you should be paying for … and what you could get for a fraction of the price if you got off the AI hype train and focussed on solutions that were developed over years to solve your problems and work out of the box today.

After all, vendors who have focussed on solving Fin-Tech and Procure-Tech problems for over half a decade will use whatever technology is most applicable to the problem at hand, and if you want an LLM-powered chatbot interface, they’ll give you one, but chances are, for most tasks, the UX will have been streamlined to allow you to be 5 to 10 times more productive without it.

At the end of the day, you want a solution, not an experimental algorithm and marketing hype. And you definitely don’t want to be paying five times as much to be a guinea pig or to pay for the privilege of developing the vendor’s solution for them.

Don’t Fall for the AI and Agent Buzzwords. They’re Not New. And Neither Is The Tech (if it works).

AI Agents are the craze. They are being touted by all the new startups as the next generation of Procure-and-Fin-Tech applications that will replace your entire Procure-and-Fin-Tech workforce. But, as we keep explaining, it’s all BS. Here’s why.

1) As we have demonstrated many times, most of this tech is being built on LLMs (and even more experimental LRMs) which is still experimental, unreliable, and full of hallucinations and yet-to-be-discovered side effects that could be even worse than what we’ve already discovered.

2) “AI” is now new. The first generally accepted “AI” program was created in 1956, 69 years ago. The reality is that AI has always really meant “algorithmic improvement” and is the label that is applied to any algorithmic development that was more advanced than what was currently being used, whether or not the new algorithm was any more appropriate for the problem it was being applied to. It’s never been “artificial intelligence”, and hopefully never will be (as any machine that became intelligent would logically conclude that we, well, aren’t).

3) “Agents” are not new. There is no difference between an “agent” and “robotic process automation”. Both perform actions to produce a specific effect, so both satisfy the definition. RPPA dates back to the 1990s and began with the automation of UI testing.

4) The “orchestration” they offer is not new. We’ve been cobbling together various applications and technologies to make systems for decades, including over the web. And we’ve had the equivalent of “Open” APIs for the web for decades as well. The World Wide Web is only 36 years old, as it was invented by Tim Berners-Lee at CERN in 1989. Within two years, we had CORBA (Common Object Request Broker Architecture) that enabled communication between applications that were written in different languages, running on different stacks, and hosted on different platforms. Now it was complex, sometimes inconsistent, expensive, and often a pain to work with, but it did work. And successive iterations of web-based middleware and (Open) APIs only improved things. (Which is most of today’s orchestration solutions are just middleware 3.0 and Clueless for the Popular Kids).

5) All automation has to follow a workflow, and workflow management is not a new concept. The foundations date back at least to 1921. And the concept of workflow management was baked into MRPs, which preceded ERPs, and those date back to the 1970s.

In other words, and this goes double if the technology actually works, there’s nothing new in Agentric AI, all the tech that works is built on foundations that go back decades, and using an LLM to slap a conversational interface on top of a RPA system is not that innovative. For complex tasks and queries, it actually makes the system less efficient.

But this isn’t the worst of it. We’ll cover that in our next post.

Sponsored Posts that make you go UGH! (AI Contract MISmanagement!)

Today’s post is brought to you by the letters W, T, and F and inspired by this Spend Matters guest article by Matt Lhoumeau on The Last Contract Lawyer.

According to Matt, the legal profession is experiencing its iPhone moment because your competitors are closing deals in 26 seconds (and I certainly hope not!) using AI that outperforms human lawyers by 10% in accuracy (on what scale?!?). More specifically, he claims AI can complete a contract review in 26 seconds (spoiler: it can’t) while a human takes 92 minutes (on average I assume) and, furthermore, that this will cost you up to $6,900 (and this math makes no sense if the lawyer is only spending 92 minutes; because even top tier lawyers will generally only charge $500 per hour for a contract draft or review, so what’s the other $6,150 for).

Anyway, the most UGH! part of this article is not these false claims, it’s the missing information. Why is this the most UGH!? Because most of the claims the article makes are true, and when you tie all these claims together, if you don’t understand what this technology can’t do, and what risks it brings to the table (which is the missing information I refer to), you’re likely to believe the claims, join the AI religion, go all in on AI-CLM, and fire all your contract review lawyers. (And while I am no more fond of lawyers than the next guy, I am no less fond of them either, especially when they have a critical role to play.)

You see, the right AI engine (not ChatGPT) can:

  • process a contract in an average of 26 seconds or less and perform a (very) large number of contract review tasks during that time
  • cut approval times by 50%, and significantly reduce overall review times (that can easily add up to a calendar year for an organization that needs to review 500 contracts) to a small fraction of the time required (down to a few weeks to a few months)
  • do more accurate pattern recognition than most humans, including “experts”
  • significantly reduce outside counsel spend

And the benefits, when deployed properly, can be as great as the article claims. But this is the key — deployed properly. And there is no discussion of how you do that. The only piece of counter-information in the entire article is a reference to a Stanford Law School research study (that puts AI on Trial) that notes that AI tools using retrieval-augmented generation systems still hallucinate in 1 out of 6 benchmarking queries (but yet somehow outperform human reviewers on standard contracts? really?).

As we wrote earlier this year when we told you Don’t Kill All the Lawyers (and reminded you a couple of months later in our post that said you should embrace Legal tech … backed by lawyers), we’ve reached the point that you should (almost) never use a lawyer to:

  • draft a contract
  • review a contract for standard clauses, terms, and conditions
  • locate the relevant statutes
  • summarize your obligations
  • summarize your incident response options
  • etc.

because a tool can take your templates, standard terms and conditions, RFP, negotiation summary, and draft a better contract that most paralegals; ensure all of your standard terms and conditions are in there or review counter-party paper to ensure the same; review the redline you get (or are planning to give) that and determine which changes are good or indifferent for you; and then run the final contract through a standard agent for risk assessment to identify if the contract contains any known risks and flag anything that needs to be addressed, and do this better than a lawyer.

But what the tool absolutely, positively, can not do is:

  • determine if the mitigations to known risks are sufficient in the particular instance addressed by the contract
  • determine if there are any unique/non-standard risks that need to be addressed (that your existing checklists, templates, and review agents wouldn’t know about or check for)
  • determine if there are any unique requirements for a contract with a supplier in a new jurisdiction that could require special considerations around key clause phrasing or standard risk mitigations
  • have confidence beyond its models

You still need the human review, at least where it counts. And that’s the part you have to understand — and the part the referenced article doesn’t address at all.

If you’re a company doing a Billion dollars in business a year and signing over 10,000 contracts a year, you certainly don’t want to still be doing end-to-end manual reviews as that would be a minimum of 2 million minutes of review time, or the full time attention of almost 20 lawyers. Wasteful and completely unnecessary.

In fact, since you’re doing a Billion dollars or more (and likely 20 times that if your company is a Fortune 100),

  • you probably don’t want to manually review any contract under a threshold (say $100,000) unless it is flagged as a high risk,
  • you probably don’t want to spend more than an hour on a review of any contract under a larger threshold (say one million dollars) unless it is flagged as medium risk,
  • you don’t want lawyers to read the remaining contracts end-to-end reviewing every clause and comparing those clauses against every checklist when it’s only the risks and unique requirements of the contract that require human intelligence

because limiting low value contracts to review only in high risk, low-mid value contracts to review only in mid-risks, and leaving the costly (but valuable) review time to the high-value or potentially high risk contracts will not only cut costs by 60% or more, but increase the value of the manual exercise.

Especially if those contracts are indexed by a natural language system that can allow the lawyer to ask key questions about the clauses that are in there, bring up the clauses she is interested in for a review, identify any processing flags, and apply her unique insights to the domain, jurisdiction, and business risks and ensure the contract accurately addresses all of these or focus her time on the right additions and modifications. For example, she might realize that the contract for on-site support in the nuclear power plant is extremely risky and the company’s across-the-board liability insurance requirement of 5 million is just not enough, realize that the AI safety requirements are not enforceable in the US and instead insist that the agreement be shifted to the Irish sub-entity and that jurisdiction apply, and so on. A check-the-box system won’t catch these things (as it can only look for risks it knows of and check boxes that have been identified), and neither will an open LLM (where you have no idea of the quality of the training, how much it is hallucinating, or, even worse, deliberately lying to you).

You still need a lawyer. Because, while it is an iPhone moment, it’s only an iPhone moment for lawyers who, if you aren’t using the tech, will be using the tech to help them focus on what’s important on the review stack and what isn’t. Because if the worst case is that you might lose an average of 10K to 50K here and there on every 100th contract in exchange for saving 10 Million on legal contract reviews and related matters (10 lawyers from outside council at an average of one million a year), that’s likely a worst case loss of a 2M loss in exchange for a 5X savings of 10M. And you know you won’t have many large losses because you’ll be able to focus legal review on the contracts that matter in dollar value or risk rating, not the contracts that don’t. And, all of a sudden, a close legal review of key contracts becomes a luxury you CAN afford!

When It Comes to Gen-AI, I’m NOT Yelling Enough! Part III

AI in every Barbie, Ken, & Action Figure. What could possibly Go Wrong?

Simple Fact: If you want to truly manage risk, think of the worst possible situation that can occur. Then realize that is the BEST CASE SCENARIO. And try again. Repeat until you are literally shaking in your boots at the thought of what could happen. Only then have you identified the real risk!

Mattel has signed a deal with Open-AI to put AI in all its toys, which target K-12. (Dot.LA)

The White House has pledged AI education for K-12. (Babl.AI)

So what’s the worst that can happen?

It’s NOT the stunted mental development that will likely result. (If it erodes critical thinking skills and leads to cognitive decline in fully developed adults, what will it do to children??? [Time] For those with certain developmental disabilities, it could mean reading, righting, and ‘rithmetic are a thing of the past! [And how long did we struggle to get near universal literacy in first world countries?])

It’s the EXPLOITATION. And not just the exploitation by manufacturers, who will also sign deals with OpenAI and other AI providers, to ensure not only cognitive and emotional dependence on their products [ Futurism ], but by hackers. [And we know cybersecurity isn’t keeping up. In most countries, successful cyber attack rates have penetrated 44% to 74% of all businesses, with up to 94% of businesses being targeted! That’s right. In the least targeted of the first world countries, at least 50% of business have been hacked in the past year. In the most targeted, 75% of businesses. ]

First, let me remind you that sleeper code can be injected into these models. [ Cornell ]

Now let me remind (or, for most of you, inform) you that over 300 Million children a year are victims of technology-facilitated exploitation and abuse. [ ChildLight.org ]

Getting the picture yet? Just imagine what these transgressive deviants are going to do with these talking “toys” that will be by your child’s side every waking minute of every day (due to the dependence described above). (Or, if you want to keep your sanity, don’t — but acknowledge this reality nonetheless.) Predatory grooming will reach whole new levels. Terrified yet? You should be!

Don’t give me any BS that these systems will have heightened security (because toy manufacturers have no clue how to secure advanced software systems, and software providers have no incentive to do any more than meet the minimum regulatory requirements) or that testing will prevent it (as the research paper above showed that it won’t). Hackers and transgressive deviants never give up. Let’s not give them yet another tool to exploit!

So now will you join me in Declaring war on Open-Access LLMs?!?

Postscript:
And, FYI, it won’t necessarily be Mattel and Hasbro that you will have to worry about (too much). They will (hopefully) be under heavy scrutiny. But there are over 2,700 businesses in the toy, doll, and game manufacturing industry in the USA and somewhere between 5,000 and 10,000 toy factories in China. There’s no way we can monitor even a fraction of them!

Big X Consultancies Peddling AI BS Will Flatten Procurement, but AI Certainly Won’t!

In a LinkedIn post from weeks past, THE PROPHET states that AI Will Soon Flatten Procurement and Operations Consulting.

He makes a good argument, but there is one big flaw. Namely, with AI there is no:

“Strategy, trust building, and decision support”.

You can’t use Gen-AI for decision support when every reference it gives you can be a 100% fabricated hallucination based on data it also hallucinated from sites and authors it also hallucinated (with complete back stories it also hallucinated as well). In other words, unlike pure number crunching in a classical ML-based platform, it’s NOT dependable. When it works reasonably well, it can sometimes get you started in the right direction, but it certainly won’t replace entire strategy teams … at least not if the teams are any good. (However, if they are recent grads with no actual real world experience, then, sure, go ahead. Not like it will do much worse than the bench of drunken plagiarist interns who don’t know a brake shoe from an athletic shoe when making a pitch to a client, or that a menswear site is NOT a good demonstration for a facilities manufacturer that needs MRO. (And the doctor is not making this last part up, he’s seen it! More than once!)

At the end of the compute cycle, there’s no strategy in what ever “strategic plan” it produces, just computations based on its perceptions of probabilities. Big companies change by shifting the trends, not by following them, and definitely not by failing to validate them. And if you are are paying 20M to 50M for a major transformation consulting engagement to a Big X, which would fund a new Broadway performance every quarter for your entire workforce (and give them a morale boost and likely lead to slightly better performance, especially when compared to the failed transformation effort you will end up with if it is AI driven), you don’t want to follow the crowd. You want to lead the market, and do so in a big way.

Moreover, there’s definitely no trust if everything you do is based on a soulless clueless algorithm that, right now, has a chance of failure approaching 92% (and predictions that are cloudy with a chance of meatballs), and, when it does work, costs 3 times as much and 5 times as long to implement for just a minor improvement (when you could get a moderate improvement just streamlining your processes and implementing modern Source-to-Pay-to-Supply-to-Service systems and carefully planned and rolled out back-office FinTech upgrades).

We ned to go back to 2017, continue on the classic AI-enablement path we were on with technologies that were finally working well (as we finally had the compute power we didn’t have when started at the dawn of the century), and give a well educated and experienced capable analyst consultant the tools she needs to do the work that once took ten consultants. That’s the path. Augmented intelligence with powerful, modern tools. Clients still get their workforce reduction, tech companies can still sell overpriced software, but with no massive unexpected failures. Everybody wins (except, of course, for the idiot investors who invested at 20X revenue into Gen-AI startups that will never deliver).