Category Archives: AI

AI Hasn’t Changed the Value Curve of Spend Analysis — It’s Accelerated the Need for Tools to Address It

In our last post on how AI increases the need for manual spend analysis, we noted that the claims that AI negates the need for manual spend analysis is all lies, damn lies, and statistics (of the worst kind).

AI throws more “opportunities” at you than you can process with spreadsheets and traditional AI tools, and it does so faster than an entire team of analysts working in the basement, and, unlike the opportunities coming from the analysts which are at least plausible, the “opportunities” coming from the AI may be completely hallucinatory and a complete waste of time (compared to the opportunities from the junior analysts that may not be worth the effort but are actually real).

So you need to be able to investigate and verify them very, very quickly — which means you need a tool that can:

  • rapidly suck in a dataset, or multiple
  • allow for rapid (re)-classification and (re)-categorization on the fly, using both pre-defined and on-the-fly rules, as well as Gen-AI for suggestions (with quick acceptance or rejection)
  • allow for the quick construction of cubes (and sub-cubes) and views (and sub-views) to verify spend, variances, and potential opportunities against market prices
  • allow for easy generation of derived dimensions and derived cubes with what-if scenarios
  • allow for easy visualization and cross-comparison of scenarios to identify and confirm true opportunities

And then when the opportunities don’t pan out, you need to be able to quickly

  • replicate the data set and rapidly (re)-classify and (re)-categorize on the fly, using both pre-defined and new on-the-fly rules
  • create new cubes (and sub-cubes), views and sub-views on the recategorized data
  • create new derived dimensions (and derived sub-cubes) based on what-if scenarios
  • cross-compare and visualize the new scenarios to identify real opportunities

Or you will never realize any value from spend analysis, especially if you are using AI. The only difference now vs. 20 years ago when you desperately needed a good spend analysis tool was that 20 years ago you didn’t have any opportunities unless you had the time to look for them. Now, you don’t have any opportunities unless you have the time to dig through all of the fake opportunities generated by AI to weed out the real ones from the hallucinations. The value curve is the same, only the ends have switched positions.

AI Increases the Need for Manual Spend Analysis

Everyone with an AI tool is proclaiming how their tool negates the need for manual spend analysis and, beyond that, how it negates the need for sourcing (because their Agentic solution can create the event and execute it automatically), and how it can even handle the contract generation, negotiations, and signing. How it will save you so much time and money that you will never need to do manual spend analysis again.

And it’s all lies, damn lies, and statistics — of the worst kind — since it usually involves probabilistic Gen-AI that often hallucinates market conditions, best practice, and true innovation.

It’s true that an agentic solution can run pre-packaged spend analysis against all of your spend once it has been classified, identify the products and categories with the greatest variance, compare the average price paid to current market prices, suggest opportunities, and push the products into the sourcing tool (and invoke the sourcing agent).

But here are the problems:

  • even assuming it’s ranking opportunities by the savings today, that doesn’t mean it’s the greatest savings tomorrow; there could be contracts in place, declining demand, or rising inflation in the category
  • similarly, products or categories passed over could be the greatest opportunities due to increasing demand, lack of long term contracts, or stagnation in pricing
  • without verification, the “market prices” could be quite off actuals as it could be “public” pricing only and private is quite cheaper, especially with volume discounts
  • the opportunities are not real unless they are with suppliers you can actually source from (and auto-identification is not actual product verification)
  • and just getting auto-responses to auto-requests doesn’t guarantee you get the responses from the suppliers with the best products and prices

And that’s just the beginning.

Here are the missing pieces.

  • it’s working on a predefined categorization (yours, the vendor’s, or, even worse, a random AI one); sometimes the best opportunities come from a categorical reorganization that allows you to bundle the right mix of products for the suppliers you’re inviting
  • suppliers don’t auto-bid in response to auto-RFI requests, vendors do — and for strategic categories, you need strategic suppliers
  • you can use agentic CLM to assemble clauses or send and retrieve documents and even do an initial analysis, but since the review is based on Gen-AI LLMs that can hallucinate as often as not, a review can be helpful, but it’s not a real review
  • the data ingested for product matchings may or may not be accurate, especially since suppliers or distributors can put want they want on their site, or, more importantly, leave out key details

It’s great to use AI to find potential opportunities, but you can’t use AI to verify, and you definitely can’t use AI to capture them. Manual spend analysis has just become more important than ever, as has a tool that allows you to do it as efficiently as AI appears to. Fortunately, a such tools do exist — although you may have to work hard to find one.

The State of Spend Intelligence is The Same Today as it Was 20 Years Ago. There is NONE!

In this age of AI hype, every vendor with an “Agentic AI” or an “AI Employee” is claiming their LLM-based garbage app is going to provide you with the “spend intelligence” you need for your Procurement success. The reality is that it is as much hogwash today as it was 20 years ago when I wrote that There’s No Such Thing as Spend Intelligence.

The words I wrote exactly 20 years ago today still stand.

There is no such thing as a spend intelligence solution.

Let’s start with the definition of intelligence.

Intelligence is a most complex practical property of mind, integrating numerous mental abilities, such as the capacities to reason, plan, solve problems, think abstractly, comprehend ideas and language, and learn.

And since spend management solutions are software, let’s review a definition for software.

Software is the (collection of) program(s) that enable a computer to perform a specific task, as opposed to the physical components of the system (hardware), where a program is the collection of source code and libraries which have been compiled into an executable or otherwise interpreted to “run” in (active) computer memory, where it can perform both automatic and interactive tasks with data.

Simply put, intelligence is a property of mind and software is a property of machine. And despite the efforts of the artificial intelligence community, I do not expect the property to cross the chasm anytime soon. Artificial intelligence is simply a collection of very sophisticated algorithms processing large data stores, instruction sets, and probabilities very quickly to come up with reasonable responses to queries – it is not thought, although it might appear to be thought since today’s computers can perform billions of calculations in a second.

The only difference is that today’s processors easily do hundreds of billions of instructions per second and with multi-threading and massive parallelization, average racks do hundreds of trillions of calculations per second and custom AI Data Center racks do quadrillions of calculations per second. But calculations do not equal intelligence. And since the models are still probabilistic, they haven’t gotten any better and, in fact, with LLMs, they’ve gotten worse!

In fact, with hallucinations a core function, not only can you not depend on the math (which these models get wrong all the time) but you can’t even be sure they’re working on the right data. We’ve went from insight to inaccuracy. That’s not intelligence. That’s idiocy … at it’s finest!

Procurement is NOT a Place of Comfort!

A while ago, Garry made a great point in one of his post on how a surprising amount of leadership is simply holding the line on reality.

And, more importantly, a surprising amount of leadership is the acceptance and confrontation of uncomfortable reality. You see, in most organizations, leaders believe it’s their job to be optimistic and keep the team happy and comfortable. They tell the team “we’re almost there … the project’s almost done” even when it’s a dumpster fire. “The pipeline is strong” when all the pipeline consists of is a list of companies who indicated they might be interested in a solution that falls in the category of solutions the company offers and they have not been vetted beyond a third party discovery call. “It’s just timing” when the RFP only gets 3 responses from 7 suppliers after extending the deadline by two weeks. “Once we hire X, it’ll be fine” when the reality is that things will get worse since “X” won’t know how to fix anything until trained (and someone will have to stop fire-fighting to train X, which will allow the fires to consume even more).

A good leader addresses the discomfort.

“Okay, things went to sh!t with this project but we succeeded in the root cause analysis, we can get back on track in two months, and, more important, we also identified three other oversights in the project plan, corrected those, and forced the vendor to upgrade capabilities that will allow us to be more productive than planned when we go live. We’ll just have to work harder to ensure the revised plan doesn’t hiccup“.

“Losing MegaCo to our biggest competitor, whom we know can’t serve MegaCo, was a big hit. We had to cut our simultaneous sales efforts in half and what’s left in RFP stages doesn’t even equal MegaCo’s potential when combined, and we know we’re only batting 50% when we get to that stage. We need to refill the pipeline with active targets fast, and we know that the leads given to us by DealSourcingCo are not well qualified based on our few conversations. Since we also didn’t have time to review many leads the past quarter, those wankers really slacked. We have to shape up, do our own pre-sales and qualification, work overtime, and re-jig the entire pipeline over the next two weeks. However, even if only a third of the “pre-qualified” leads pan out, which is the current success ratio, the good news is that we’ll have more than enough to keep us busy and get back on track for next quarter.”

“It’s not timing. If 4 / 7 potential suppliers didn’t answer the RFQ, then we really f*cked something up. We need to contact each of them, and find out why. We’re we not specific enough on our requirements? Was our guaranteed commitment too low? Was the timeframe too short for them to respond? Were we asking too much in the RFQ for no guarantee? Did we misjudge the supply/demand and they just don’t need us? However, I just licensed us a category management system where we can encode all the knowledge we gain from every sourcing exercise to make sure this situation doesn’t (unnecessarily) happen again as we will know what we need to get it right“.

“Bob quitting and taking all his knowledge was a big loss. We’re totally underwater as we don’t have his category knowledge, know which of the 10,000 spreadsheets he was using for the last events, or who really managed the relationship at the supplier. The fact my predecessor never made Bob properly track anything left me with a hole I’ve ben struggling to figure out how to fill, especially since every time I pushed for better data organization he kept being more insistent he just didn’t have the time with his workload.
However, I did license an AI tool that will scan all his files, attempt a categorization by category, and extract the likely supplier, contact, products, and prices. I did license a category management tool that all of this data, once reviewed by an independent expert, will be pushed into.
And I did go out and find a few independent consultants who are real veterans with 20 years of category experience and engage them for the next quarter to sit down and help us get everything organized. (Independent, not fresh-faced known-nothing MBAs from a big consulting co.) The next three months will be hell, but then things will be better than they ever will before because we’ll all know where all the key category knowledge is at all times and we’ll be able to bring Bob’s replacement on and have that person be effective day one. For now it sucks, but if we can hit our targets, I’m going to expand your bonus pool by giving you some of mine“.

That’s leadership. Telling the team as it is, making sure they understand it’s not going to be comfortable for a while, but that they will get through, you’ll be there with them, and you’re doing whatever you can to make it possible.

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!)