Category Archives: Market Intelligence

Buyers Are Not Process Operators!

In a LinkedIn post from a while back, Garry makes a very important point: many procurement operating models still treat buyers as process operators.

Run the event. Collect the bids. Populate the template. Push it through governance. Negotiate hard. Close the file. Move on.

Tech (which may include AI but doesn’t need to as you can do quite a lot with ARPA and do it better, faster, and cheaper than humans AND Gen-AI can do it) will make the traditional buyer role less central because all of this, except for the finer points of negotiation, can be done by the tech. (The brute force points, collecting all the data to defend your offer can be done by the tech.)

Once you adopt Busch-Lamoureux Exact Purchasing, it becomes easy to not only map your categories to the octants, but identify the processes you should use for sourcing and procuring those categories, as well as monitoring the procurement activities to determine if there is a situation where a human has to intervene.

It also becomes clear what you need to do at each step.

  • Sourcing: identify what needs to be sourced vs procured, what categories and items will be included in an event, what suppliers, what products, what requirements, etc. etc. etc. — all of the decisions you can’t risk automated (which can still only be automated from encoded knowledge from prior decisions)
  • CLM: key contract requirements and acceptance criteria; etc.
  • SXM: key (compliance) requirements, key risk mitigation clauses, need for no vs. internal vs. external review, etc.
  • Analysis: historical spend/volume/prices; current prices/volume requirements; predicted prices/volume requirements; opportunities for demand shaping/control; etc.
  • e-Pro: available channels and under what conditions; what gets in the catalogue; who can buy out-of-catalogue/non-preferred; processes for overrides (to budget limits; cost limits; etc.)
  • I2P: m-way match requirements and tolerances; ok-to-pay / auto-pay requirements; when early-payment discounts can be offered/applied; etc.

As Garry states, a buyer is not a buyer — a buyer is a decision architect and makes the decisions necessary for successful Procurement. A decision architect that designs how a decision should be made. An intelligent human who maps the organization’s categories to the pocket cube of Exact Purchasing, determines what can be automated, what systems will be used to automate, what qualifies as exceptions, how those exceptions will be monitored for, and how they will be alerted.

But a buyer is more than that — it’s a decision architect and relationship management. Procurement is about managing stakeholders and suppliers. Dumb systems cannot do that. Only HUMAN INTELLIGENCE can.

In an AI-Hype world, Procurement will be measured on its success, and that success will require Human Intelligence leading Procurement to glory. So acquire real pros if you want to not only survive, but thrive in, the Age of Retardation the AI-Hype is ushering in!

Are they 2026? Or 2016? Or 2006? Procurement Trends? Part II

Tom Mills recently posted a Top 10 Procurement Trends in 2026 post on LinkedIn that made me ask Really? Basically, I’ve been reading, and writing, about the majority of the “trends” for two decades. As per my recent 34-part series on you don’t need to read another state of procurement report for five years!, nothing has really changed in the last five years. In fact, not much has changed in the last ten, if not twenty, years. All that ever changes is the tech-du-jour, which particular risk is the most prominent, which particular process is the most recommended, and whether the trend is in-sourcing solutions, out-sourcing solutions, or hybrid models.

To make this oh-so-clear, we’re going to conclude Tom’s list and provide some colour commentary!

6️⃣ AI Becomes Core but our Readiness Lags

This is the only “sort of new” trend, except it has been the “sort of new” trend for three years now, but when you realize “AI” is the “tech-du-jour”, you realize that, again, nothing has changed for the past two-plus decades because the “tech-du-jour” is always the 10th trend. And for every
tech-du-jour that becomes core, our readiness lags. Over the past 25 years we’ve had these five tech-du-jours (that tend to last for around 5 years).

  • WWW
  • SaaS
  • The Fluffy Magic Cloud
  • Predictive Analytics
  • AI

7️⃣ Data Quality and Governance as a Prerequisite

For all advanced tech, data quality has ALWAYS been central and paramount. Ever since the introduction of optimization, and in our space, strategic sourcing decision optimization (SSDO), data quality was key. With traditional (MILP) optimization, one value in one million can tank an entire model (because if a decimal point error makes one product 50X cheaper, then the allocation will obviously go to the wrong supplier). Moreover, if there are capacity constraints, minimum allocations, maximum supplier counts, etc., this will result in cascading incorrect assignments and allotments across the entire model. Then came should cost modelling, and again, without good data quality and governance, it didn’t work. Then spend analysis, which needed proper market baselines. And now AI, which is garbage in, hazardous waste out. Even with perfect data you can still get hallucinations, so you definitely don’t want even the slightest error!

8️⃣ Orchestrated Procurement Ecosystems

In Procurement, which has NOT fundamentally changed since the first manual was written 139 years ago, the story remains the same — only the names have changed! AI may be the tech-du-jour, but orchestration is the term-du-jour. But it’s not new. The automated coordination, management, and sequencing of multiple distinct processes, systems, or components to achieve a unified, higher-level goal has been a goal of Procurement for decades — except back in the 2000s the term-du-jour was “metaprise”. (And Jon W. Hansen can also fill you in on the history here.)

9️⃣ Talent as the Transformation Multiplier

We’ve been talking about this for decades. I wrote a 7-part series 20 years ago when I first started SI. Talent is not only necessary, but it’s the way you truly succeed. Talent that designs better processes, selects better technologies, and, most importantly, makes better decisions that allows the organization to be more strategic and more effective is not only transformation, but a transformation multiplier.

🔟 Procurement as an Enterprise Value Driver

Ever since AMR first started covering the space in the early 2000s, we’ve been told that Procurement is the Enterprise Value Driver. That strategic sourcing, when utilizing the right technology (namely optimization and analytics) would consistently identify year-over-year savings of 12%. That m-way matching, which ensured the payment matched the invoice matched the PO matched the contract would prevent (often unrecoverable) overspend. That spend analysis can identify real value drivers. The whole space was defined as a value driver. Nothing has changed.

The GruntMaster 6000 was engineered for longevity and has a long memory. And his long memory tells him that the more things (are purported to) change, the more they stay the same!

Are they 2026? Or 2016? Or 2006? Procurement Trends? Part I

Tom Mills recently posted a Top 10 Procurement Trends in 2026 post on LinkedIn that made me ask Really? Basically, I’ve been reading, and writing, about the majority of the “trends” for two decades. As per my recent 34-part series on you don’t need to read another state of procurement report for five years!, nothing has really changed in the last five years. In fact, not much has changed in the last ten, if not twenty, years. All that ever changes is the tech-du-jour, which particular risk is the most prominent, which particular process is the most recommended, and whether the trend is in-sourcing solutions, out-sourcing solutions, or hybrid models.

To make this oh-so-clear, we’re going to review Tom’s list and provide some colour commentary!

1️⃣ The CPO as Enterprise Architect

Back in the first major age of responsible sourcing in the early 2000s, the message was that the CPO had to be an enterprise architect to be responsible. To make this abundantly clear, SI did a 12-part series on the “Responsible Sourcing Supplier Workbook” released by the John Lewis Partnership which was the best example of how Procurement could architect a responsible enterprise!

2️⃣ Procurement as Business Storyteller

I remember going to Ariba Live a decade ago, and they opened with the SAP Storyteller. The reason – their solution (which never fully integrated Procuri that they had bought almost a decade prior) was going on 15 years old (while Coupa was still revolutionizing its platform and telling its own tall tales and BravoSolution was acquiring like mad [just before it became Jaggaer]) and there was less and less reason to buy Ariba’s outdated tech … until they told the whole story of what was possible when Ariba was fully integrated in the SAP ecosystem (and what could be possible — forget reality, just believe and buy).

3️⃣ Strategic Supplier Partnerships over Transactional Buying

State-of-Flux (SoF) was founded 24 years ago because strategic supplier partnerships were the key to success! Aravo (US) and SoF (UK) were the first to recognize this and this message has been consistent for decades, coming into the forefront whenever significant supply disruptions occur due to natural, or man-made, disasters. This goes back to the 80s when the recession, plant fires, and the lingering after-effects of the 70s steel crisis led to part shortages and cost hikes that could (only) be mitigated with strategic supplier partnerships. This situation reared its ugly head again as the web, and SaaS, exploded, we had new semiconductor (and RAM) shortages due to demand (and plant fires), multiple man-made and natural disasters had global consequences (9/11 attacks, Indian Ocean Tsunami, Hurricane Katrina, etc.), and market losses surged (dot com bust, 2008 financial crisis), leading to the rise of SXM software as a key category in Procurement in the early 2000s.

4️⃣ Outcome-Based Procurement

That’s the whole point of GPOs. Outcomes is only the price model du jour because the AI vendors couldn’t sell their solutions using a SaaS model with true cloud computing costs being passed on to them by their hosting (and AI) providers! So they have to convince you to buy into their “outcome”-based model. (And that’s why, now, outcomes is a dirty word.)

5️⃣ Strategic Supplier Risk and Resilience Orchestration

Aravo was founded in 2000 to do this. I remember writing about them back in 2007, and Google was one of their early adopters.

To be continued …

The world is not binary, flat, or stable!

It’s multi-state, curved, and chaotic.

You need fuzzy math, fractal geometry, and non-linear differential equations to describe it.

Similarly, the supply chain world we built is not a predictable single source flatland (as the work of Edwin Abott Abott in 1884 should have made clear to you).

You need multi-state logic, multiple (supply) chains and multiple methods for managing them.

And these DO NOT fit into a 2 x 2 grid! It’s this ongoing lie that ultimately leads to failure and organizations bringing in one consultancy* after another, and one platform after another, in an attempt to fix problems which never go away.

Every distinct dimension that needs to be considered in classification and decision making is a distinct dimension that needs to be taken account in any methodology or “map” presented to you (and multiplies the number of “buckets” you need for classification). So if you have three dimensions, you need at least 2 * 2 * 2 = 8 buckets in your classification scheme (as you will have at least 2 values per dimension you differentiate on, and that’s assuming each dimension you are differentiating on is a binary decision — if it were ternary, e.g you were classifying each dimension on high, medium, low or red, yellow, green, then you would have 3 * 3 * 3 = 27 buckets).

That’s why every single analyst quadrant map that attempts to assess a vendor, product, or service on more than 2 dimensions is an ultimate failure. (That’s why SolutionMap works — it’s just tech vs customer sentiment, not innovation, service, tech, market fit, market strategy, product strategy, industry strategy, geographic strategy, product viability, pricing, track record, execution, operations, and customer experience randomly squished into two meaningless composite values using absurd average weightings that are equivalent to taking the average weight of an apple, BMX bike, and a cruise ship.)

Mathematically, this would require a 14-D hypercube with 16,384 sub-cubes. And that’s why you don’t measure everything, only what counts! But try as you might, you usually going to end up with at least 3 independent dimensions that are critical to any problem you work on. But that’s not a bad thing! [Remember, the 3-sided triangle is the most stable shape with area in flatland (where analysts and consultants still love to live in to this day), and the 4-sided tetrahedron (pyramid) you can make from 4 triangles in 3-D is one of the most fundamentally stable shapes there is (and atomic bonding proves this).]

Since, when it comes to Procurement, the 3 most critical dimensions are complexity, risk, and organizational impact of what you’re buying, proper Procurement is dictated by a pocket cube. The Busch-Lamoureux Exact Purchasing pocket cube to be precise.

So if anyone else claims their updated Kraljic matrix will work for you, just shut the door. Don’t bother arguing. If they won’t accept real-world reality, you won’t get a real-world solution. Find someone who understands the complexity and can build you a platform to address it, with as much automation as can be brought to bare. (And quite a bit can be brought to bear, as per our series on operationalizing the pocket cube.) That’s how you will succeed. The old fashioned way — define the problem, use Human Intelligence (HI) to address the problem, and design processes and systems to execute the solution as efficiently as possible. The fundamentals don’t change, and anyone who says otherwise is a scam artist trying to sell you (silicon) snake oil. Don’t buy it.

* Now big consultancies won’t tell you this because if you get it right the first time, they can’t continue to sell you consulting hours, which is their ultimate goal.

IDC Misses the Main Point Completely. Outcomes is a Dirty Word!

Sorry, Paul, but when you say MNR is directionally right here, but I think the market still understates how hard “outcomes” actually are, and reference an IDC article, you’re off. The only part that’s right is that AI price wars miss the point (that you probably shouldn’t be using [Gen-]AI to begin with).

Outcomes only matter more … to the vendors. Because the meaning of outcomes in the vendor vernacular has NOTHING to do with results, but how they can spin their story to grift you as much as possible. As I clearly explained in my series on how Outcomes is a Dirty Word, which I now have to revisit, “outcomes” is always a way to charge you more for less (and sometimes next to nothing).

And it all has to do with (Gen)-AI costing way more than what the vendors want you to believe.

As per my initial post, while once exclusively the verbiage of GPOs, who wanted you to turn over a significant share of your procurement to them (to the point you’d be dependent on them and their ever-increasing cost of service for the entire existence of your business), or recovery audit firms, who wanted you to believe their services were the only way to recover your overspend, it’s now on the tip of every snake-slit tongue of every vendor rep.

While the vendor reps want you to believe that the reason you pay for “outcomes” instead of traditional SaaS pricing is that their AI will deliver immediate, measurable, results (instead of just transaction cost reductions where it will take at least a year to measure savings), and therefore you should pay (dearly) for those outcomes up front (because a success today is a CEO pat on the head today), that’s not the real reason. (Especially when those projected savings from the auto-sourcing and procurement events will never materialize.)

The real reason they are pushing for outcome-based pricing is that (Gen)-AI compute costs are now so high (and won’t compress as the energy and cooling costs keep rising as the majority of existing data centers are on already overstrained grids) that they can’t afford to sell the solution using a traditional SaaS based pricing model — they wouldn’t even cover their compute costs! (Most of which is wasted since most of what is being “automated” by these solutions can be automated by traditional A-RPA SaaS solutions for a fraction of the cost, as long as you don’t need a natural language interface or slick UX — and you don’t!)

The reality is that the software (assisted) solution from any vendor selling on an “outcome” model isn’t worth it, and (Gen-)AI forgets what software is supposed to be about — enabling efficiency so Human Intelligence (HI!) can achieve outcomes using low-cost Augmented Intelligence solutions.

And until a new generation of AI emerges where hallucinations aren’t a core function, measurability and confidence are restored, and compute costs are inline with classic AI tech, AI models won’t become utilities. We are years away from a systems problem!

The only way to get value is, as Paul pointed out, to redesign workflows, align incentives, clean up constraints, and embed decision logic into execution and find fairly priced modern tech with orchestration and “real” AI (in the form of Augmented Intelligence built on best-of-breed analytics, optimization, and machine learning) that will allow you to make decisions 10 times faster AND 10 times better.

The vendors who ultimately win when the AI crash hits will be those that built real tech on tried-and-true analytical, optimization, and machine learning models that will, as Paul states:

  • drastically reduce cycle times,
  • minimize manual intervention (via A-RPA where the response to every exception remembered, encoded, and applied to all future instances),
  • improve overall compliance,
  • increase throughput, and, ultimately
  • allow for better decisions.

And, as Paul points out, that’s not building yet another chatbot. That’s building real systems that work!

And, FYI, Gen-AI is not feature theatre. It’s puppet theatre! And while puppet theatre may provide entertainment, it’s not a viable business model!