HACKETT CONFIRMS THE STATE OF PROCUREMENT HAS NOT CHANGED … No Need to Read The Full Report!

Nothing makes my point better than slide 15 on Trends in Procurement priorities in the 2026 Procurement Agenda and Key Issues Study Results sponsored (at least) by Jaggaer, SAP and Unit4 (and likely others).

Basically, every year you have the concerns of

  • supply continuity
  • cost reduction against inflationary price increase
  • strategic business advisory
  • digital transformation and the tech-du-jour (analytics to AI)
  • operating model improvements

All of the risks fall into our eight ever present risk categories:

  • Talent: Access, Acquisition & Retention, Retiring Workforce Impact
  • Disasters: (Other) Supply Chain Disruptions
  • Cyberattack: CyberSecurity Risks
  • Spend Pressure: Economic Downturn, Changing Customer Expectations, Capital Access, Competitive Alternatives
  • Supply Shortage (and Trigger Events): Trade Wars, Geopolitical Tension
  • Regulatory Compliance: Regulatory Compliance, Ethics & Privacy, Product Liability
  • Corruption: IP Loss
  • Tech-Du-Jour: AI-enabled Tech, Tech Transformation Delays, Tech Obsolescence

It’s the same-old, same-old situation when it comes to initiatives, except the tech-du-jour (AI) is nearing the top of the list, and the ecosystem is essentially the same, only the names of the players have changed. And, of course, the conclusion is, surprise surprise, to employ the tech-du-jour which, lo-and-behold, Hackett stands by and stands ready to help you with (despite the 94%+ failure rates found by MIT and McKinsey).

In other words, it’s the report we expected, and the first of many to come. (As you can expect every other analyst firm and consultancy will soon be releasing theirs, if they haven’t already. But we won’t be reading them, and for the next five years at least, neither should you.)

And, with the exception of the key shifts in concerns, issues, risks, and barriers, which could be a two page summary, it’s not a report you need to read through as very little has changed in the last decade.

THE STATE OF PROCUREMENT HAS NOT CHANGED! So Ignore all the Reports Flooding Your Feeds!

Between November of last year and January of this year, SI published a 35 part series on why you really DO NOT need to read another State of Procurement report for Five Years in order to save you the trouble of reading yet another report that was 95% the same as last year’s report, and 85%+ the same as the report you read five, if not ten, years ago.

The realty is that:

  • the barriers to success never change (just their relative criticality based upon which ones are currently your biggest obstacles)
  • the risks never change (although some go up each year while others temporarily go down)
  • the concerns never change, with the exception of the tech-du-jour which just replaces the previous tech-du-jour when the hype cycle changes

And this is because

  • the core function of Procurement HAS NOT changed since the first manual was published one hundred and thirty nine years ago, which means
  • the issues Procurement is addressing today are essentially the same fundamental issues Procurement has always been facing which means
  • the priorities have not changed either

And you don’t need to read 30 to 60 page reports to realize this. All that’s relevant is what climbed or fell on each list since last year since that tells you

  • which challenges are coming your way if they haven’t hit yet,
  • which technologies and trends are gaining hype status, and
  • how your peers see their priorities for the year

Nothing beyond that is useful, as the functions, issues, priorities, concerns, risks, and barriers are the same (although some have rapidly climbed the charts with a certain World Leader randomly removing regimes, starting special military actions, and blocking trade routes with no warning).

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

China is Leading in AI!

And the real reason why? The courts are defending labour rights and NOT allowing companies to replace workers with AI.

As per a recent posting over on “The State Council Information Office (of) The People’s Republic of China” on April 30, 2026: (Source)

“A Chinese court has ruled in favor of a human employee in a labor dispute caused by AI replacement, which experts said may send a reassuring message to labor rights protection efforts in the age of automation.”

Furthermore, this was not the first time!

On December 26, 2025, the Beijing Municipal Bureau of Human Resources and Social Security released a set of arbitration cases for 2025, including a dispute triggered by AI-driven job displacement. In that case, the arbitration panel made it clear that 𝐀𝐈 𝐫𝐞𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐚 𝐝𝐢𝐬𝐦𝐢𝐬𝐬𝐚𝐥. It found that adoption of AI technology is a voluntary move to stay competitive and not one that is mandated or acceptable as a basis for human replacement and dismissal.

Furthermore, legal scholars in China are emphasizing that 𝐭𝐡𝐞 𝐜𝐨𝐬𝐭𝐬 𝐨𝐟 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐬𝐡𝐨𝐮𝐥𝐝 𝐧𝐨𝐭 𝐛𝐞 𝐛𝐨𝐫𝐧𝐞 𝐬𝐨𝐥𝐞𝐥𝐲 𝐛𝐲 𝐰𝐨𝐫𝐤𝐞𝐫𝐬 and that while 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬 𝐦𝐚𝐲 𝐛𝐞 𝐢𝐫𝐫𝐞𝐯𝐞𝐫𝐬𝐢𝐛𝐥𝐞, 𝐢𝐭 𝐜𝐚𝐧𝐧𝐨𝐭 𝐞𝐱𝐢𝐬𝐭 𝐨𝐮𝐭𝐬𝐢𝐝𝐞 𝐚 𝐥𝐞𝐠𝐚𝐥 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤.

This is the thinking that will allow for actual progress and development.

AI is not intelligent, humans are still needed, and progress will be made when we stop accepting the BS that AI can replace us and instead only listen to and work with companies that state that appropriately designed, implemented, and/or restricted AI can augment us in our jobs and make us 3, 5, and even 10 times more effective — enabling us to be super human workers.

It might be too late for the US, but if Chinese courts continue to make rulings that indicate that 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐰𝐡𝐨 𝐛𝐞𝐧𝐞𝐟𝐢𝐭 𝐟𝐫𝐨𝐦 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐠𝐚𝐢𝐧𝐬 𝐦𝐮𝐬𝐭 𝐛𝐞𝐚𝐫 𝐜𝐨𝐫𝐫𝐞𝐬𝐩𝐨𝐧𝐝𝐢𝐧𝐠 𝐬𝐨𝐜𝐢𝐚𝐥 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬, it won’t belong before China is truly dominating the world (since the US will have no competent employees left when everything goes to hell).

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.