Category Archives: Economics

“The Suite Was Supposed to Settle Everything.”

This is a requested response to Paul Marty’s inquiry: Is Source-to-Pay Dying.

We’ve heard the “one app to rule them all myth” since the late 60s when IBM said the MRP would control the operations and no other app would be needed. This was rekindled in the mid 80s when Oracle said the ERP would control the organization and no other app would be needed. Then in the late 90s when the web would provide a common interface to everything. Then the late noughts / early tens when the Suite would solve everything. It’s the myth that keeps on giving.

The reality is that any new monolith application model only solves all the problems that came before, not the problems you have now, and not the problems that are coming.

That doesn’t mean suites don’t have value, or that suites won’t provide more value than any other alternative, just that it’s not a panacea. Determining whether it provides more or less value requires understanding the true total value realized (taking all costs and benefits into account) compared to the true total value of the alternatives, many of which are unproven.

“Is The Economic Value Moving Somewhere Else?”

First off, let us note that, fundamentally, the new AI orchestration craze is no different, as it ultimately depends on a protocol that can bind everything together. This isn’t new either. Remember CORBA? Probably not, as most of you probably don’t even know what it is. Look it up. At the dawn of the dot com era, it was going to solve everything. It made all the same promises MCP is making now.

The reality is that the fundamental truths of technology have not changed.

> If it sounds too good to be true, it is.

> Technology can’t advance any faster than discovery (either mathematical computing theory or practical scientific discoveries that allow for new engineering that can build better machines to support better models), and that has, and will, always be slow. How many hundreds of thousands (or millions of years) has it taken us to compute a theoretical warp bubble, and how many decades/centuries more before we can achieve it? (We’ve went from conjecture with the introduction of Star Trek 60 years ago to theoretical possibility 5 years ago … but now we need to create the energy source, the materials that could withstand it, etc. etc. etc.)

> … businesses not only need to make money every year, but are punished by the capitalistic economy if they don’t; so even if all they have is the same old sh!t, they still need to sell it, and the best way to do that is to slap a new coat of paint on the old Mustang and tell you it’s a brand new model (even though the engine hasn’t changed in almost a decade)

This doesn’t mean that AI orchestration doesn’t provide value, just that it’s not the ultimate answer and that you have to truly understand it’s true cost vs its true benefits and, where autonomy is granted, true risks before you can make a decision.

“What if the employee does not need to enter the procurement system at all?”

That doesn’t change the fact you still need a procurement system.

Just because an intake layer can work across applications, and just because an AI agent can complete a routine workflow without ever displaying the myriad of screens suite vendors spent years (if not decades) refining, that doesn’t mean that the core functions go away. Either those have to be part of the intake/orchestration system (in which case it’s really a suite with a new kind of interface) or you still need the underlying best-of-breed / suite systems, which are only accessed through their APIs.

In the latter case, the true economic value remains in the best-of-breed/core systems … and the only value of the intake/orchestration/AI agent is the cost of user time reduction (i.e. 5 FT AP agents to manually process all the invoices vs 1 FT AP agent to manually deal with the exceptions). So if the cost of the software is greater than the manpower saved, it’s negative value.

This assumes that the agent is deterministic and will do all processing according to user defined rules and in user defined bounds. If you are using LLM-powered agents and giving them autonomy, there is no guarantee they will follow all rules and stay without bounds — autonomy means that they can decide only 10K over their approval limit is ok because it appears to be a known supplier, onboard an uncertified supplier, issue a new PO to a supplier who is late on other critical deliveries, etc. which can result in significant losses.

Which brings us to the most important question:

“ONE INTERFACE > MANY SYSTEMS > WHO DECIDES?”

This is a key question for multiple reasons:

1) You can’t cost / benefit a system without knowing.

As above, if the decision is ultimately human, you can accurately cost the system and determine it’s fixed (workforce reduction) benefit as well as it’s expected benefits from (weighted) average results and track true benefits over time.

But if the decision is ultimately the system, you can’t truly estimate the benefits as you don’t know what it will do, how many mistakes it will make, and what the cost to clean them up will be — and if it uses LLM technology, you can’t even cost the system as you don’t know how the token/compute cost is going to increase over time as you don’t know the token growth that will be required or how soon you’re going to have to pay true compute costs (the big AI companies can’t keep losing $4 for every $1 of revenue, and with today’s energy and water costs, the true compute cost is about 5X what most of the players are charging … and this will only increase over time.)

2) You can’t quality control without absolutely knowing who decides what, what the range of options are, where, and how to encode the checks.

Which means that:

“THE CENTRAL QUESTION … IS WHO OWNS THE REASONING THAT DETERMINES WHAT PROCUREMENT SHOULD DO”

Especially when the answer to:

“Are Twenty Agents Really Twenty Opinions?”

is NO!

I know Jon W. Hansen might disagree with me here, since he uses multiple systems to arrive at his (starting) algorithmic analysis of the marketplace regularly, but agents reason off of the data they are given.

When you interconnect them, they share “data” and “reasoning” continuously. Also, even though they are ultimately sophisticated probabilistic models no human can fully understand, the core is similar to deep multi-level feedback neural networks. This means that common “data” and “reasoning” increases the weighting on that “data” and “reasoning”, increases similar output, which increases similar input to the minority models, which causes that “data” and “reasoning” to become more heavily weighted in them. Or, in simple terms, a majority of agents will bully the minority of agents into their viewpoint until there is one opinion, not twenty.

Even if all 20 agents start out different, as they share “data” and “reasoning”, there will be some commonality, and as the “data” and “reasoning” flows back and forth, the weighting of the commonality will increase until it snowballs into a majority, given enough exchanges (over time).

While some “experts” will try to tell you this all but certainly guarantees you will get the best results once the agents agree, that is ONLY true if the common “data” and “reasoning” is accurate. Some agents will have bad / fake / hallucinated (wrong) data and others will share faulty “reasoning” because they don’t actually reason. (They predict arguments … think of them as a super powerful fancy version of next word suggestion on your iPhone, except they are predicting answers to questions and sometimes even the steps involved to get there!)

While individual agents sometimes work well if they have enough raw question/answer input/output previous step/next step data top work from (and that’s how they create mathematical “proofs” — they can scan millions of research papers and proofs, build up all the associations as input/output pairs, and then see if there is a way to chain them all together from a question to an answer where the output of one step in one paper is the required input of a step in a paper in another paper, possibly published in an unrelated journal in an unrelated discipline), they sometimes don’t work very well at all!

They’ll tell you to eat one rock a day. Or that strawberry only has 2 rs. Or, because they don’t understand reality, when asked, they will tell you that “the woodchuck could carry 57,600 pounds of wood in a day” when asked “How much wood could a woodchuck chuck if the woodchuck was 10 lbs?” (I didn’t make this up, DeepSeek did!) (And, even worse, it gave some extremely ridiculous “reasoning” to back it up which might fool someone who didn’t think twice because the logic was pretty good, while the data it worked on was ridiculous [and obviously hallucinated]. I should also point out that this was a result “I” totally expected because there shouldn’t be much “data” on “how much wood a wouldchuck could chuck if a woodchuck could chuck wood” on the internet [which is it’s training data], because we all know a woodchuck can’t chuck wood but the AI doesn’t know that.)

Thus, whether you use one agent or twenty, you will get one opinion, and one result, which could be a total hallucination if none of the systems had enough correct “data” and examples to “reason” on.

Given that, as Paul Martyn points out, the best decisions are made when there is

  1. A diversity of experiences and information to draw on
  2. Independent thought by the participants (that can provide truly different views to the ultimate decision owner)
  3. Decentralization of knowledge for risk mitigation

and that today’s AI, which is NOT intelligent, cannot be trusted to always make the best decision as

  1. all agent systems have access to the same data on your network and the web
  2. agents cannot think, only produce “outputs” based on different weightings of “inputs”
  3. agents with minority data/reasoning can, and will, have at least their reasoning, if not their data, overwritten by the majority

This brings us to the following conclusions:

1. Traditional Suite and BoB solutions ARE still needed, even with orchestration and agents (especially since the later should be restricted to either fully deterministic processes or processes where the cost to fix/clean up after an occasional bad decision by software is less than the cost of having a team of people do that process manually)

2. The New Hotness Hype will always take sales away from established players, as the lazy and greedy look for that panacea solution that does not, and cannot exist … but when those solutions fail to achieve the result, some of those sales will return (as the only other option will be to build the missing parts into the acquired system, and why waste money trying to extend a system that’s fundamentally broken?)

3. “The Market is Not Dying”, as Paul Martyn states, but it is shifting. Suites will become the transaction engines and systems of record in organizations that experiment with new interfaces, orchestration, and AI agents. Hybrid suites, which embed orchestration and intake, will take over in organizations that want modern connectivity and interface but want to limit the number of vendors they need to deal with. And traditional suites will gain traction in highly regulated industries and governments where everything has to be 100% predictable and in-line with thousands (if not millions) of pages of policy regulations!

As Jason Busch likes to say, “many things can be true at once” and as long as those things are not mutually exclusive, they will be in the market.

(P.S. Thanks for taking up my day with no warning, Paul!)

Have We Lost The Economy of Information?

Twenty years ago, we were forming GPOs and Purchasing Consortiums to take advantage of Economies of Information. Ten years ago, we were not only rolling out suites that focussed heavily on consolidating spend (and performance) data globally (with the likes of Coupa and Sievo boasting about how much spend they had managed and normalized) to take advantage of the economy of information global spend data gave us, but building best in class analytics solutions to take advantage of all of the data they (could) gather(ed).

In the age of (predictive) analytics, which preceded the current age of AI Hype, the importance of data finally started to become recognized and you had a number of startups hit the scene providing next-gen data feeds. (Near) Real-time commodity indices, market price data, risk data, company financial data, carbon data, energy rates, water rates, regional overheads, average process time, average GPO and transaction rates, average performance data, etc. Any organization that wanted to build a best-in-class should cost model, best in class performance model, etc. The information was available, there was an economy for information, and the economy of information was right around the corner.

Let’s step back and define what we meant by this. On economies of information, twenty years ago we wrote:

The consortium of the future offers the benefit of expertise more so than it offers the benefit of scale. Eventually, especially with constantly rising raw material prices, the best practices employed by a competent consortium will squeeze all of the fat out of the supplier’s margins and the best price will be obtained. Once this occurs, the consortium will use its expertise to assist its members in advancing purchasing technology, reducing wasteful consumption, and improving the application of the goods and services they purchase. Since a consortium has access to all of the knowledge of its members, it can tap this knowledge to identify the best potential suppliers with the best potential products and services to meet member needs. Furthermore, this gives it a much better chance of identifying and qualifying low risk suppliers.

In other words, with a fact-based outlook on reality, consortiums could help take Procurement to the next level. Then, when the data-stream startups made all of that same information easily available as plug and play data feeds into your suite through standard APIs, the true economy of information hit Procurement for those who wanted it and Procurement could make insight-based and fact-based decisions and get better.

But now that we’re a few years into the age of AI Hype, I believe we’ve lost the economy of information. There a few reasons for this:

  • we’ve replaced data feeds with LLM chatbots like clod and chat, j’ai pété and assume they have access to the same data, and, most importantly, the same ability to run predictive analytics on that data
  • despite claims to the contrary, the LLMs are getting worse by the day … now that the majority of data on the internet is AI generated slop, being cross fed into other LLMs, regurgitated with compounding errors, we are not only losing the core data in the tsunami of slop but the meaning of that data as well
  • with LLMs being cheaper than data feeds, the data feeds have been ignored, a number have went out of business, and the rest are floundering

There’s no information without actual, verified, facts and intelligent interpretation, and the majority of that has been lost in the age of AI hype.

If too many real data providers, as well as applications that deterministically and intelligently integrate and analyze real data and real facts, go out of business, there will be no solid foundations for real information, and, thus, no solid foundations for economies of information — and then we’ll be back to the Procurement dark ages.

Technology has never advanced Procurement. Only facts, data, process and decision improvement based on intelligent interpretation has.

Supply Management in the Decade Ahead: Same Old, Same Old — Only the Pendulum Swings Part I

Almost 20 years ago, back in 2007, I wrote a 2-part series on Supply Management in the Decade Ahead: The Eight Major forces (Part 1 and Part 2) where I discussed the various external forces that will impact a company’s supply chain in the years ahead, as verified by CAPS, AT Kearney, and their clients.

And just like you don’t need to read another state of procurement report for five years! because, except for the tech-du-jour, nothing has really changed in the past five, ten, and even twenty years; you don’t really need to do an extensive study or survey to realize that the core supply chain issues are more or less the same, it’s just where the pendulum happens to be in it’s swing on each major issue.

Today we discuss the first four.

Global Competition Contraction

Twenty years ago, we were dealing with the rise of China hitting full swing as a result of the outsourcing craze that started to pickup in the eighties (as a result of pushes by McKinsey and counterparts), as well as other secondary markets becoming more accessible — and a host of global options for just about everything you could buy.

While this hasn’t changed, since the world’s dependence on China is still at an all time high (from about 5% of Global GDP in 2005 to almost 20% in 2025), and there are more and more companies popping up every year to satisfy your needs, their relative availability to you has changed. With canals and straits being shut down on a regular basis, sanctions coming fast and furious, tariffs costing out supply, and so on, the competition you can access in some countries (especially the United States and Europe) is shrinking by the day. The pendulum is swinging back from Globalization to Deglobalization/Contraction, and, as usual, you have to keep track of where the pendulum is and what direction it is (still) swinging in.

Merger, Acquisition, & Supply Market Consolidation

To meet the onslaught of new competition, companies headquartered in developed economies are still attempting to increase in size, scale and market power to ensure they survive stagflation and recessions. Others, in financial (di)stress, need to do whatever they can to survive. This will thus force many companies to continue the merger and consolidation trend that has been ongoing in fits and spurts for the past few decades.

Similarly, in the supply chain software space, paralleling the procurement software space, due to the impacts of the Age of AI Hype, we will see a lot of M&A as the bigger companies, with their legacy SaaS, acquire smaller “AI” players to add the “AI” propaganda to their own marketing, and the smaller legacy companies, struggling to sell their SaaS, put themselves up for (fire)sale to “AI” and I2O (Intake to Orchestrate) providers who need real capabilities and larger companies trying to complete their suites.

Increased Government Regulation

Governments continue to do what they do best — create red tape for the private sector, sometimes with the best of intent, sometimes to fatten their pockets, and sometimes just to keep busy. Sometimes the regulations help competition, sometimes they protect consumers, and other times they just add cost and process for absolutely nothing. Either way, they won’t stop.

This continues to lengthen your supplier / partner research and contract negotiation cycles, which must discuss relevant government regulations, privacy legislation, DEI (must [not] have), AI, and who’s on the hook when (import/export) tariffs get introduced out-of-the-orange. In addition, government actions to support or restrict economic development, such as tax incentives and trade restrictions, will continue to have a large impact on supply strategies.

Technology Advances

(Proclaimed) technology breakthroughs continue to cause major changes in how products and services are provided, especially in the software and control systems spaces. Properly applied technology improvements (i.e. real AI vs. Gen-AI LLMs) will revolutionize design, planning, and production systems and continue to lower total cost of production and the consumer’s total cost of ownership. Aggressive early adopters who overpay for the hammer when they don’t have any nails (just screws) will drive up costs while driving down efficiency and organizational capability (as they lay off workers they actually need and freeze hiring) are increasing their costs significantly and reducing their consumer base. If they can’t rapidly correct, they’ll end up being part of the new M&A frenzy (if not the next bankruptcy), while those who can successfully adapt modern, proven, tech with a plan will win big.

For the rest of the decade, and possibly part of the next decade, the winners will continue to be those that adopt the right SaaS solutions (because, while we may be in the SaaSpocalypse, it’s only going to weed out those overcharging for commodity software and capability, and the companies that survive the next few years will be those that offer true value. The reality is that even agentic (and AI) is still SaaS, it’s just SaaS that requires less (to no) human intervention [beyond maintenance by the provider] for the tasks it is assigned.

The New Market Dilemma IV: Buyers Still Win the Battles!

Vendors and Consultants are but a small portion of the industry, and the economy … as Buyers, you work for organizations that compose the majority. The only way we’re truly going to get back to business-as-usual is if you use the vision provided by the vendors looking ahead to solutions (and not looking back to outdated manuals or just offloading their work to hallucinatory Gen-AI LLMs) to identify what clarity you need, bring in consultants to help you realize it (and the significant ROI that accompanies it), and then use your newfound “savings” to procure the best-of-breed sourcing, procurement, and supply chain visibility technology offered by the visionary vendors, as this is the technology that will help you increase productivity and significantly reduce your costs across the board and survive the AI Hype Induced Market Crash to come.

We don’t know how bad the crash is going to be, and it might be a long road to recovery that requires a significant effort and initiative on your part (depending on the size, complexity, and focus of your organization), but the starting point is clear. So here’s a simple step-by-step guide to get you on the right path.

  1. Do a real process and spend analysis.
  2. Bring in process and category experts to get you real time savings on your most time-consuming processes and your most profitable tier-1 categories.
  3. Implement workflow and process orchestration and e-Procurement systems to realize the savings.
  4. Adopt e-Sourcing to streamline and maximize the savings potential on your tier-2 categories.

REAL ANALYSIS

We’re not talking about a simple time tracking exercise on a step-wise basis based on progression points, we’re talking about real end-to-end process analysis in terms of how much actual human time is spent on each step (not just when the steps happen), why, if the process can be redesigned to take out the time-consuming steps, and, if not, if the steps can be redesigned to take less time. It’s about real efficiency, not just a bit of streamlining with thoughtless automation.

And we’re talking about real spend analysis — not just loading your AP data into a UNSPSC cube and running out of the box reports on your top 10 vendors, top 10 categories, and top 10 departments. Even if you don’t know the exact amounts, a simple internal survey will tell you those with uncanny accuracy. And it’s definitely not throwing random spend data into your LLM of choice and asking for it to find “savings”. That’s just going to run the same dumb reports and give you the “obvious” recommendations that you already know, and that for one reason or another, you can’t do because of existing contracts, specific product needs, etc.

We’re talking about loading all of your spend-related data -— AP, Invoice, Contract, Third-Party Price Indices, etc. — in a real spend analysis product that will let you slice and dice it any way you can think of so that you can identify (a) where you have made overpayments and extract refunds and (b) identify the top categories with with the most savings potential. If you haven’t done this before, you’ll want to bring in an expert. There are a few providers in this space that typically find tens of thousands, and sometimes hundreds of thousands, and occasionally millions, of dollars in overpayments within a day. The ROI is well worth the investment.

CATEGORY EXPERTS

This is especially important in categories like energy, telecommunications, utilities, and SaaS that require significant expertise that you might not have. While you might be able to negotiate a 15% cost decrease in a buyer’s market if you’re well informed, a seasoned veteran who has been negotiating these deals day in and day out for a decade (or two) will find a way to save you 30%. And when many of the firms will work on contingency, i.e. you don’t pay until the new contract is cut for an amount less than what you’re paying now, the ROI will be significant.

IMPLEMENT MODERN E-PROCUREMENT

Up to 60% of negotiated savings never materialize at many companies. If you don’t implement state-of-the-art end-to-end e-procurement systems orchestrated with your sourcing, supplier, and supply chain solutions, with price control capabilities (contract integration, punch-out price verification, authorizations for off-contract spend), you too could lose 60% of the savings you negotiated.

ADOPT NEXT GENERATION E-SOURCING

While you’ll still want to bring in the big-guns for the big savings opportunities, as the ROI will be many times what the big-guns cost you, there will be a large number of tier-2 categories where the savings opportunities, though substantial, won’t be as significant if you have to pay high-powered consultants. These are the categories where you get your best returns if you can run the events quickly, and efficiently, in house. And this is what modern e-Sourcing execution platforms allow you to do … especially on categories where you need to go back to market regularly because the volatility is too high to risk long term contracts.

The key is a modern sourcing execution platform that will let you incorporate the right amount of automation, market intelligence, optimization, analytics, and suggestion. It’s all about allowing the buyer to spend just the right amount of time to extract the value and not a second more.