Category Archives: Knowledge Management

Turst is Real Procurement Currency — And That’s Why AI CANNOT Do Procurement!

A couple of months ago Garry addressed a point made by the Peter Smith, the Bad Buying Bard, which boiled down to an issue more important than anything technical where AI is concerned … and that point is Trust.

In his original post, Gary asked if AI would change Procurement. However, after reading Peter’s comment, he realized the real question is whether Procurement is trusted enough that the organization will accept Procurement setting the rules around how AI is used. As Garry notes, that’s the crux.

When it comes to trust, it’s not whether or not the suppliers trust Procurement that’s the real issue, it’s whether Procurement is trusted internally. If Procurement is not trusted, it will be bypassed, ignored, and even sabotaged. This includes the (mis)use of AI. If Procurement is not trusted, it will not have any authority, and the organization will not heed their warnings (based on logic and the research they are used to doing), charge ahead with AI, and become yet another failure contributing to the 94%+ failure rate (while costing the organization millions upon millions of dollars and wiping out any savings Procurement may generate, especially if the C-Suite dictates an AI-first solution for Procurement).

Furthermore, you can’t use tools that you cannot trust. And you can’t trust any Gen-AI Procurement platforms built on hallucinatory LLMs. Since hallucinations are a core feature, results can’t be guaranteed, and LLMs can’t even be counted on to follow explicit instructions (and will corrupt your documents and data even when explicitly told not to), you can’t use Gen-AI/LLM-based AI.

And, unless your data is clean, categorized, up-to-date, and easily accessible through modern APIs, “classic” AI won’t work either. Good Procurement Pros will remind you that you can’t jump straight to AI. Just like you couldn’t expect a tribesmen from a culture with no written word who never set foot in modern civilization to begin reading lessons on the works of Shakespeare accessible only on a modern tablet, you can’t jump decades of technology. Or process.

Successful Procurement requires:

  1. getting your processes in order
  2. getting the supporting data in order
  3. implementing classic technology with high-degrees of deterministic, dependable, determination

And then, and only then, do you sit down, identify where there are still inefficiencies and/or a lot of tactical bit-pushing work, and try to figure out where AI will actually help. This means that most organizations are still years behind where they need to be to successfully implement any AI. In the classic Hackett journey to best-in-class, which will take an average large multi-national 8 years, it will be at least 4 years before the organization is far enough along on any process to consider advanced AI. (For a mid-size, this journey can be reduced to 6 years, and then it’s 3 years before Procurement is ready for advanced AI. It’s always People, Process, and Data before AI!)

AI is NOT Failing Because of a Lack of Forward Positioned Data

Lack of forward positioned data is NOT the problem.

(It is a problem, but not the biggest one!)

An AI agent making 1000X the decisions IS!

Right now, while the big AI players have achieved 80% to 90% “accuracy” on their carefully designed synthetic benchmarks, when applied to real world problems, accuracy in many domains drops to 25% (or worse, as at most 20% of code generated by an AI survives into a production application once it gets reviewed by a senior developer who finds a plethora of security issues, boundary condition errors, and code that, frankly, just doesn’t solve the problem at all).

THIS MEANS THAT THE AI IS MAKING 750X MORE WRONG DECISIONS THAN THE HUMAN!

That’s a LOT of mistakes.

Meanwhile, give an expert human

a) always available forward positioned data and Augmented Intelligence applications to process it (so all the data the expert human needs to make the decision is at her fingertips)

b) A-RPA (Automation) software that is best-of-breed and capable of immediately executing any decision the human makes (possibly using the forward positioned data and appropriate augmented intelligence outputs)

And that human will make 100X the decisions she’s making now, and get 95% of them correct. So if you hire 10 humans, you will have 25X less errors (5% vs 75%).

When you consider ten humans will cost considerably less than AI when you consider the rapidly rising token costs and the costs of dealing with the 25X increase in errors the AI will bring, Augmented Intelligence powered by Forward Deployed Data and a small team of humans will be a LOT more productive than you ever thought possible.

The state of global procurement is dire!

Supply Chains are Broken.

  • Terrorists in the Red Sea.
  • The Strait of Hormuz is effectively closed.
  • Piracy is back off the Ivory coast.
  • Climate change is leading to Panamanian droughts and reduced Canal capacity.
  • Natural Disaster / Storms are on the rise and traversing the Capes is riskier than ever.
  • China’s Zero Tolerance policy means complete port shutdown on the detection of a single virus.
  • Sanctions cut off entire countries.

Old Guard Insight is gone.

  • AMR was swallowed by Gartner, who lost the last of their great analysts.
  • Harte Hanks gutted Aberdeen.
  • Forrester saw (well-deserved retirements).
  • Even the IDC Outsourcing greats moved on!
  • Spend Matters is gone. (Rest in Peace)
  • A space that once had almost 200 independent blogs/analyst (firms) now has barely 20.
    (SI once hosted a resource site that tracked each and every one.)(New) Tech is only causing chaos!

    We’ve went through 5 generations of tech-du-jour in the last 25 years.

        1. World Wide Web
        2. SaaS
        3. Fluffy Magic Cloud
        4. Predictive Analytics
        5. AI

    Not one solved the problems they promised — and the current tech, AI, is failing faster than ever before (with a tech failure rate already at an all time high of 88%). (6% of companies are seeing a return on their AI investments. That’s all!)

    It’s our darkest moment in Procurement and Supply Chain to date.

    We need guidance more than ever. We need the masters!

    We need to call for the return of the Enterprise Irregulars.

    Most of you won’t remember — but the greats in our space came back together back in the 2006 to 2008 time-frame and launched the portal that would collectively change our space before each of them went off to form their own ventures and change a part of the space on their own. Some of those parts survive, some don’t. But we need them back together. If you agree, echo the call!

    Linked In Post

Ontologies Could Have Saved Us — But in the Age of Gen AI, They Might Just Ruin Us!

What is an Ontology?

Philosophically, an ontology is the study of being, existence, and/or reality that is designed to investigate not only what entities exist but how they can be categorized.

In computer science and, more specifically, the data age, an ontology is a formal, machine readable, specification of entities, their properties, and their relationships within a domain that is used to structure information in a way that systems can share and structure it.

In the early days of semantic technology, an ontology was used to structure data in a meaningful way to allow sophisticated models to process, and make sense of, natural language with relatively high degrees of accuracy. It was usually expressed in a formal ontology language that allowed for detailed entity, relationship, part of speech, and even concept definitions. They were often defined in such a way they could be organized into interconnected libraries which formally organized knowledge into large, connected, corpuses that could be deterministically processed (hallucination free) and completely understood by any application that was capable of processing the language the ontologies in the library were encoded in.

And this was the true beginning of the semantic web, which was also known as Web 3.0, which was still in its infancy in the 2010s, but starting to take off by early (early) adopters (with almost 2% of web domains containing semantic markup circa 2014).

But then five things happened.

1. SaaS exploded, and so did the need for data, and the ability to consume it in standard formats.

2. GPT-1 was released in 2018 and the Gen-AI craze began shortly thereafter, leading us down the hallucinatory hole of incessant inanity that every consultant thought could power everything.

3. This led to the agentic craze, which increased the demand for data (and the desire to consume it in structured formats).

4. Every SaaS provider, and their dog all of a sudden needed multiple, steady, streams of data in standard formats to power their agentic applications.

5. In response, every data provider responded by adopting a simple data standard, calling it an ontology, even if all they were serving up was average scope 3 carbon data by country and factory type.

And now the term has no meaning since it’s the term used by every SaaS vendor and data supplier to essentially describe their data file structure. No formality. No relationships. No underlying structure that allows the machine to actually reason. Just another random data file blended into the data soup that feeds the hallucinatory engine that will tell us to go over the cliff like lemmings (and lead countless to their deaths as they cognitively surrender to what the AI tells them to do).

What could have been our saving grace (if Web 3.0 research had continued and true ontologies of ontologies had been created) might soon be the source of our demise as Gen-AI blends together mismatched data with flawed reasoning and produces the digital equivalent of toxic waste.

If Instead of Trying to Replace, You Redeployed People — What Could You Accomplish?

The big push for AI is not to help you, but to achieve every executive’s dream of a perfect utopia where they have 24/7/365 robotic workers they don’t have to pay, feed, or even provide safe working conditions for. Where they have endless slave labour, workers with no rights, and only have to worry about counting the virtual dollars in their endlessly increasing bank accounts.

But anyone with a working brain, who doesn’t live in a fantasy world, who hasn’t given into the cognitive surrender brought on by excessive use of Gen-AI, knows that reality is far, far, away. The algorithms are dumber than doorknobs, hallucinate to various degrees on almost every response, and are only good at sounding right, NOT being right. Intelligent humans are still needed, more than ever (as AI has NOT changed the fundamentals of Procurement. It HAS Only Strengthened Them.)

While there is very little Gen-AI can do, there is a lot traditional AI, and even more that (A)RPA (the real agentic technology) can do if properly defined, constrained, and deployed — and in many back office functions, a lot of the data analysis and processing (still) done by humans can be done by machines (and could be done by machines for at least a decade — if not two). In Procurement, we’ve had invoice technology that could automate invoice processing error free 95% to 98% of the time for over a decade, auto-reorder technology based on stock levels, forecast changes, or production schedules for over two decades, technology for automatic contract creation based on clause templates and clause libraries for almost as long, and sourcing automation since the first major sourcing platforms hit the market.

If this was properly done, and 80% of the tactical bit-pushing time that, with fire-fighting, constitutes about 90% of a Procurement professional’s time, was eliminated — imagine what could happen. All high impact and high risk categories could be strategically sourced. All complex categories could be examined in detail, BoMs and production technologies optimized, and supplier relationships (and thus supply assurance) strengthened. And that’s just the start.

Procurement would have time to examine, shape, and even divert (and eliminate) demand. From the classic example of negating the need for more printers, paper, and printer ink by just ensuring every employee had a second monitor at their desk and a tablet for mobile document receipt and review to a more modern example of elimination of expensive cell phones for non-sales on-demand employees by Whatsapp (and cheap subscription) mandates or elimination of expensive office leases in areas where most employees are/work remote most of the time and only a few hot-swap desks at a work-sharing centers (and the ability to book / rent meeting rooms for occasional meetings) is acceptable (as they all use laptops anyway), demand shaping can result in major organizational cost savings.

Moreover, Procurement could even go beyond demand shaping and reduction to true value identification by helping the departments they serve define, and redefine, what value actually is and how best to achieve that value when going to market.

A great example of this is how IKEA approached its use of AI in customer service. As per this great summary on LinkedIn by Alberto, when IKEA’s AI bot deflected 47% of calls, instead of calling it a win, firing half it’s staff, and moving on, IKEA did two things.

  1. They asked what the AI bot wasn’t helping with and what concerns still had to be handled by the customer support team.
  2. They retrained and redeployed over half of their customer support team to handle the most common inquiry, and built a ONE BILLION DOLLAR business around it. (So Far! It’s IKEA. And they’re just getting started.)

To clarify, many (potential) customers weren’t calling just about missing parts or issues understanding the assembly instructions. They were calling to ask what they should buy to meet their needs. “What works in a small living room.”

They needed basic interior design advice. So IKEA trained a significant portion of their customer service workforce as interior designers, and generated over €1 billion in additional business in the first year simply by spending the time to figure out what customers needed before they could make a purchase decision (interior design advice and the identification of products IKEA offered that would meet the design criteria) and giving them exactly what they needed.

Imagine how much value Procurement could add to the business if, instead of reducing staff with automation, the C-Suite retrained (or, if the existing staff doesn’t have the education/experience, replaced that staff with an equal amount of more senior personnel) and redeployed this suddenly freed up staff to act as an internal value identification consultancy that brings Procurement (cost management, risk mitigation, and supply assurance) best practice to the rest of the business.

Think about that before you try to replace real intelligent talent with unintelligent talentless AI (and find yourself in the bog of eternal stench that results from your lack of foresight).