The World Continuously Changes — Can Your Framework Keep Up?

One of our big themes for the past two years is that Procurement Has Not Changed, and has essentially not changed since the first known manual for public Procurement was published back in 1887. (Handbook of Railway Supplies).

Moreover, not only are the core goals, and processes, roughly the same, the core tech you need hasn’t changed much for two decades. As per our series on how you don’t need to read another state of procurement report for five years and myth-busting 2025 2015 procurement predictions and trends, the only thing that ever changes is the hype-du-jour and the tech-du-jour. Every other barrier to success, risk, concern, and tech is the same year after year after year.

That being said, your supply chains are breaking daily. That’s because, while the requirements for supply chain management haven’t changed, you never implemented the right systems to manage your supply chains (and the up-front procurements) to begin with — systems that understand that the world continuously changes (because many people can’t be happy with what they got — whether they got it good or bad — or the status quo) and you need to monitor and keep up.

As per many of our posts this year, including an updated checklist for major international procurements, most of your systems were built during the age of globalization with opening borders, minimal conflicts, reasonable levels of natural disasters, and efforts to secure major maritime routes were continuous. Now we are in an age of isolationism, borders are closing, sanctions are significantly restricting trade, conflicts are breaking out regularly with not only regional, but global, impacts. Major maritime routes are being cut off. Natural disasters are off the charts. Etc. (That’s also why old strategies for supply chain management are new again. And most of the 2008 trends are still relevant in 2026. See the series.)

As per the series Bob Ferrari (of Supply Chain Matters) on why Direct Sourcing Solutions Don’t Work for Direct, part of the problem is that your procurement solutions weren’t built to be supply chain aware. The other part of the problem is most supply chain software was built to support production with respect to S&OP needs, not real world monitoring, management, and mitigation.

A solution needs to support all of the long-term strategic, mid-term tactical, and short-term operational requirements from initial scoping and planning through final fulfillment which means that it’s not just point-based network design planning, strategic sourcing, supplier collaboration, demand management and EOQ, network management and source allocation, logistics and routing, warehousing, and delivery to customer. It’s also continuous market monitoring, context-based decision making, evaluation, and reconsideration; risk identification, monitoring, management and mitigation; re-orchestration and network updates; etc.

It’s the multi-level integrated framework Bob and I presented last year, which he presents again in his recent article on Materials Sourcing and Planning Decision Making Process Frameworks where he notes that the global wide supply network environment has contused to become ever more volatile and uncertain with each passing week and this requires be a more effective data management, data harmonization and analytics infrastructure. Your business and supply networks need to be ready. Our framework gives you the foundation (and, more importantly, notes that Gen-AI is just an interface to the real capabilities, it is not the miracle cure-all panacea that the silicon snake oil purveyors are making it out to be).

You are strongly encourage to read Bob’s article on Materials Sourcing and Planning Decision Making Process Frameworks and study the framework within, and if you haven’t, go back and read our series on why Direct Sourcing Solutions Don’t Work for Direct. It will be worth your time.

In the Age of AI, the Analyst is King!

Never forget this!

(Click here to learn why DPGLabs.ai is best tool a VENDOR has to judge their perceived place in the ProcureTech market if that’s what you’re looking for.)

This rant is inspired by Dr. Elouise Epstein who, in this LinkedIn post, said the most wrong and dangerous thing she ever said. (And this SI article is an archive of my immediate rebuttal, found in this LinkedIn post.)

Saying that in “in the Age of AI, analysts are superfluous” is akin to saying that, “in the Age of the Internet, the Journalist is unnecessary” … and we all know how false that was, and still is. (Because the claim that the internet allowed everyone to be a journalist was not only far from the truth, but also ignored the fact that many people misuse the internet to spread rumours, conspiracy theories, and false facts that, when combined with FUD (fear, uncertainty, doubt), caused others to spread and amplify those same false facts. And if Gen-AI has proven one thing, it’s that it can spread and amplify falsehoods at the speed of light.)

(Now, this is assuming Dr. Epstein believes that an analyst does more than sticking random logos on a worthless map. But if that’s her definition of analyst, then maybe she’s right.)

There are multiple problems with her statement, and more problems than can fit in the 3K post character limit of LinkedIn, so we focussed on the biggies, as that should be more than enough to point out how Gen-AI has made the Analyst more critical, not less.

1) We are not in an age of true AI. We are in an age of hallucinatory Gen-AI.

This means that, even if all the data available is mostly correct, the AI can still get it wrong.

2) DPG Labs is NOT using evidence, they are using content from various URLs and weighting it (and, if the weighting is high enough, calling it evidence).

This means that if a vendor floods third party sites with unmarked advertorials filled with fake or misleading content, the vendor profile that Gen-AI creates is sure to be wrong.

3) Most vendors and customers blast success stories and hide failures.
(No one wants to admit they blew millions of dollars.)

These only come out in private conversations and the only “evidence” comes a few years when the customer switches to a different platform much sooner than the norm.

etc.

This means that no auto-generated profile can be considered reliable without human review. (And the odds of correctness range from 95%+ for the major vendors where there is lots of correct content to work on to 50%- for new vendors with little content or a lot of misleading/inaccurate content.)

But even if the profile is mostly right, tech cannot do the following:

4) Judge organizational fit.

That goes beyond a score. (That’s why Xavier Olivera and I designed the SpendMatters, now Hackett, Solution Map as a TechMap. We assessed what most organizations couldn’t, gave them average unbiased customer scores against that assessment, and let the organizations focus on what they could assess — their needs, the vendor’s culture, service, relationship approach, etc. — and not what a static map can’t do.)

5) Give you a true timeline and success rate.

Vendors over promise and under deliver, as they all assume perfect scenarios that never materialize. Dumb Gen-AI can just assess statements, not fact, and can’t understand what factors are involved in the 3 month best-case implementation, the typical 6 month implementation, and worst-case 12 month implementation for organizations of similar size, complexity, and geography in the vendor portfolio (where even the best case 3 month is still twice as long as what they promise).

6) Help you decide if you can trust the vendor.

This is probably the most important point. Do they have what they claim, can they deliver it, and are they committed to delivering it to you on time, schedule, and budget. And it’s not just their word, or a third party survey on a select customer sub-set, it’s based on realistic assessments of performance and the individuals assigned to your project.

etc.

That being said, DPGLabs.ai is the best tool a VENDOR has to judge their perceived place in the market.

It’s also the best foundation out there on which to build a true next-generation analyst offering. If this existed a decade ago, the efficiency gains it would have enabled would have allowed us to cover 80%+ of the vendors in depth at Spend Matters (vs 20%-) on Solution Map as it would have slashed our research and writing time* and allowed us to focus on verification, gaps, and uniqueness.

I described why the offering is the best tool VENDORS have, as well as how it could be the best tool to build a new analyst offering in the comments (of the original post) in detail in this LinkedIn post, where I have included the content below for easy access.

But even though the offering has a lot of value, it’s still nowhere close to a replacement for a true analyst.

The true value of DPG Labs.

After 20 years as an analyst, I’ve learned a lot.

This includes the fact that a lot of vendors don’t know how to position themselves, and know even less about how they are positioned in the market.

To this day, I still see new vendors regularly misusing purchasing, procurement, and sourcing when describing their offerings and wondering why they aren’t showing up in the right lists, maps, or articles.

I see vendors over-focused on the hype-du-jour (AI, Agentic, Intake to / Orchestration, Analytics, etc.) who completely ignore descriptions of rock solid capabilities or unique offerings that would differentiate them from the 40 to 100 other offerings that sound almost the same. (See the SI Mega Map if you don’t believe how many competitors you really have! The 7EB V1 edition has 888 vendor logos.)

I see vendors who have no idea how the market, and more importantly, how the search engines / AI tools used by the market see and position them.

What David Bush and his team have built at DPG labs is the perfect answer to that. By correlating and cross-referencing to a common vocabulary all of the external data and coverage on you against a standard functional map definition (that captures the questions buyers are likely to ask), you can see which categories / verticals / etc. the market is putting you in and which vendors you are being put up against. If those aren’t the vendors you expect to / want to be put up against, this means one or more of the following is true:

  • you’re not describing your offering appropriately
  • the coverage you’re getting is not focussing on the key / unique / valuable aspects of your offering (and you have to change your analyst relations / marketing / partnership approach)
  • you’re (trying to) compete in the wrong market

and you have rock solid proof you can take to the C-Suite that you need to market / sell / partner different if you want to succeed.

Up until now, analysts could only tell you this … and you/the C-Suite could ignore us (by claiming it was our opinion — which it was, but our expertise meant we were right the vast majority of the time). But this tool is essentially doing what pros using AI (enhanced) tools are doing to research you, and showing you where you stand.

It’s the one offering that I would have loved to create when I was at Spend Matters, but never could (because, even 3 years ago, the AI models just weren’t there to support it). And it’s the one tool you need the most. (Plus, market clarity will ensure you score better on the right maps since the analysts will have a better understanding of you before the first interaction, which will help them ask the right questions, focus the demos, and give you the right coverage.)

And even though you can use it to create a shortlist as a potential customer, you can’t use it for final selection. I explained why in this comment, where I also reiterated how great it would be as the foundation for a next-gen analyst firm. The core rationale is the following.

We have to remember:

  1. publication is NOT evidence, it could be opinion
  2. probabilistic assessment of language is not human assessment of language
  3. news coverage is often still marketing, especially when it’s an advertorial or there is paid coverage
  4. disgruntled customers can spread lies as well as truth
  5. even “verified” sources can be hacked or altered or replaced on the internet
  6. … and when (correct) coverage is insufficient, errors (and hallucinations) multiply in Gen-AI (frontier models)

Your (Gen-) AI tools don’t know any of this! Unless every profile you build is reviewed by an expert human AFTER an expert human review of the solution (i.e. a real analyst with real knowledge and experience), which DPGLabs is not doing (as they don’t have a real analyst team), you can’t be sure it’s accurate and not free of hallucinations.

P.S. This means the only profiles I’d trust on DPGLabs are the human reviewed profiles that Dr. Elouise Epstein reviewed, which would be the profiles of the vendors she knows well, and that’s a small minority!

* Not that Gen-AI produces good content, but, as per above, when enough material exists, it can create great summaries that allow an analyst to get a reasonable expectation of a vendor offering before the demo, produce summaries for the vendor to self-correct, and produce the auto-gen fact sheets, allow the analyst to focus their efforts and writings on the true analysis, opinions, and insights that you really need.

If You Can’t Do The Job Without Tech …

Then you can’t do the job with tech.

I’ve said it before and I’ll say it again. If you can’t do the job without tech, then you can’t do the job with tech.

All tech does is automate tasks and workflow processes and allow you to speed them up by a factor of 10 to 1,000,000 plus (depending on the tasks and workflow processes).

It doesn’t make tasks or processes better or worse (unless you Gen-AI, which typically makes it worse) — just faster (unless, of course, you change the process when you implement the tech and make the task or process better or worse).

Now, you’re probably asking why it’s not enough to design the process and simply install the tech. That’s because there’s a difference between designing a process and executing a process. Anyone can design a process at a high level given a highly set of requirements. Not anyone can execute. Among those, even less can execute it effectively.

Only those who can execute it effectively are fit to do the job, regardless of what tech the organization has or does not have. That’s because, if you don’t know how to do the job without tech (and certainly without AI), you don’t understand what the job really is. Business was conducted for thousands of years without modern tech, and computers only became generally available in big business in the 80s and mid-size businesses in the 90s and small businesses in the 2000s. That means everything you do today with modern tech was once done without modern tech (and, 150 years ago, without any tech that we would consider modern at all).

That understanding means you understand the core of the function. You know what has to be done, how, where the time suck is, what can be automated, how, and why. You also know how to verify correct automations has been implemented, and where the results require human interpretation, review, and decisions — and, if necessary, make those interpretations, review, and decisions.

Without that understanding, you can’t properly make use of tech and you definitely can’t make use of AI. Sure some tasks might be 10 to 1,000,000 times slower (and sourcing optimization will be out of the question, just a few bid comparisons), and you shouldn’t do them by hand unless necessary, but the point is you need to be able to — otherwise, you can never judge what the system does. Iin the age of hyper-fast LLM hallucinations, only good human decisions will allow your organization to succeed.

Forget Lean Sourcing, It’s Time for Mean Sourcing!

While it sounds like lean sourcing, which can be defined as a strategic purchasing approach focused on maximizing value and minimizing waste, should be the ultimate solution to strategic sourcing, especially when you consider some of the core activities in Procurement:

  • Value Addition: Identifying exactly what the end customer values and eliminating products, features, services, or luxury materials that do not contribute to it. (Supports Value Definition and Value Stream Mapping)
  • Process Standardization: Simplifying workflows to reduce bottlenecks, speed up purchasing cycles, and lay the foundations for automation. (Supports Flow)
  • Strategic Partnerships: Cultivating close, long-term relationships with a compact, reliable supplier base rather than continually chasing the lowest bidder to streamline the supplier network. (Supports Pull)
  • Continuous Improvement: Cultivating a culture where buyers regularly refine practices and reduce inventory. (Supports Pursuit Perfection)

it should be the perfect solution. But it’s not. The problem is that Lean was born out of manufacturing, not supply chain, and not sourcing and procurement. Here are the problems with Lean in Sourcing/Supply Chain:

  • Myopic focus: Taken to extremes, not only results in a company minimizing not only product lines, and the components used, but their focus on the product lines that consumers want. This is profitable and successful in the short term, but consumer preferences for products change over time, and if you don’t keep up with shifting consumer trends, you’ll be too late to capitalize on major opportunities. (Just like Blockbuster missed out on Netflix, Kodax on digital cameras, and Xerox on the personal computer.) And if you don’t keep an eye out on new developments, you’ll miss opportunities for new integrated components and working with engineering to save money.
  • Automation First Philosophy: the whole point of process standardization was to allow for consistency, transparency, productivity, error reduction, and resource optimization, not necessarily automation — but the interpretation has been to automate everything without any thought as to whether or not humans can do it better or Human Intelligence (HI!) is needed
  • Long Term Agreements: a long term agreement is not a partnership, it’s just a long term contract; most Procurement organizations have failed to grasp what a strategic partnership is! (Japan gets it with keiretsu, but that’s about it.)
  • JIT: you want JIT in terms of factory production, especially since factories have limited space so you don’t want to pull from the warehouse too fast, but it’s one thing to JIT from a local warehouse, it’s another thing to try to JIT across global supply chains filled with fragility and unpredictability and constant disruptions

In other words, in order to succeed from lean, you have to modernize Lean for Procurement and the modern world. And get a little mean while you’re at it.

  • while the primary focus is optimizing costs against the value stream, the secondary focus is pushing strategic partners for new designs and products that will change both the value of the customer offering as well as the cost of production and service; in other words, you’re only happy with the status quo today, you expect proposals for improvement tomorrow
  • constantly push for process redesign where you can reliably use unintelligent automation (with rules-based deterministic certainty and adaptive exception management) and not hallucinatory agentic / Gen-AI for true efficiency improvements; it also keeps platform/cloud costs way down even as throughput scales by orders of magnitude
  • shift from cost focus in agreements to co-development focus — that’s the way you form true partnerships
  • migrate to balanced inventory management where you keep extra stock on hand of critical/scarce/hard-to-get materials and components sufficient to cover at least the average delay time when a disruption occurs — it can also optimize logistics costs and production costs at a different economy of scale, making up for the slightly increased inventory costs (which only need to be a fraction of their traditional inventory-cost based percentage with smart inventory management)

In each of these cases you are forcing more than just the process (which your team and suppliers will think is mean), putting cost second (which the C-Suite will think is self-centered because it’s all supposed to be savings to please the board members), and putting more burden on your internal networks (which your team will think is really mean, considering your operations and consultants have spent decades shifting to suppliers).

It might seem mean, but modernizing your practices in a resilient and collaborative fashion is what it will take to thrive in today’s global landscape.

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.