A longitudinal read of how operational-readiness language entered the procurement-tech discourse — and what the archive documents about when the conversation actually began.
The 2026 statement
On May 13, 2026, GEP Worldwide published Why Supply Chain AI Readiness Is Now Measured at Scale, a LinkedIn piece anchored to recent findings from The Supply Chain AI Readiness Report, a study GEP conducted in partnership with the University of Virginia’s Darden School of Business surveying 180 executives. The post is structurally significant for what it surfaces about how the procurement-tech discourse has evolved. The load-bearing observations are worth reading directly.
“These are not model problems. They are operational ones.”
“AI exposes operational weaknesses that were easier to work around before.”
“The strongest organizations increasingly approach AI as an operational redesign effort rather than a standalone technology deployment.”
“Only 5% of organizations surveyed reported operating AI at scale, despite widespread experimentation and investment activity.”
The framing is precise and the analytical contribution is structurally sound. GEP is articulating that the gap between AI experimentation and AI operationalization is not a technology problem but an operating-model problem — that organizations standardize workflows, clarify governance, and improve data discipline before scaling further, or they stall.
The post deserves engagement on its own terms. It is one of the clearer institutional articulations of what the broader procurement-tech market is now beginning to recognize. The framing — “AI readiness is becoming less about experimentation and more about operational consistency” — operates at the level the moment requires.
What makes the post most interesting, however, is not what it says about AI readiness in 2026. What makes it most interesting is what the Procurement Insights archive documents about when this conversation actually began.
The 2023 record
On March 3, 2023, a strategy meeting was held between Sourcing Industry Group (SIG) and GEP to discuss a proposed white paper. The meeting included GEP’s senior team. Transcribed in real time, here some of the key points introduced during the session:
“It’s not a technology problem anymore. It’s now a positioning problem of understanding and how you can assist them to leverage that technology so they begin to grow vertically versus horizontally.”
“One thing that came out of the pandemic is that the majority of companies acknowledge they had technology, but they were stuck. They’ve only been leveraging ten to twenty percent of those.”
“The majority of CPOs aren’t satisfied with the results of their digital transformation strategies.”
“They’re not just looking for providers, they’re looking for partners, especially at the mid-market level.”
Eleven days later, on March 14, 2023, the synopsis document formalizing the proposed white paper’s thesis was drafted with me serving as lead writer on the project. The working title was “Beyond Technology: What Is Your Digital Readiness Within A People, Process, Technology Framework.” The synopsis articulated the thesis in language that warrants careful reading against the 2026 GEP positioning:
“It is not simply the access to technology that leads to successful outcomes, but understanding how to effectively integrate it into how people work in the real world, redefining the processes that drive results and leveraging the technology to make the leap from digital promise to digital realization finally.”
The synopsis was reviewed by GEP, with comments returned on March 20, 2023. The white paper itself did not advance to final production. The strategic conversation it represented, however, was documented contemporaneously and is part of the framework’s nineteen-year Procurement Insights archive.
What the 2023 record actually contains
The substantive observations the framework articulated in that March 2023 conversation are worth surfacing explicitly, because each one maps onto positions GEP’s 2026 post now occupies.
In 2023, the framework articulated that technology was no longer the differentiator. The 2026 GEP post articulates that “the question now is not whether AI can improve sourcing, forecasting, supplier management, or contracting. In many organizations, it already is.” Same observation. Different technology generation.
In 2023, the framework articulated that companies had technology but were stuck — leveraging only ten to twenty percent of capability. The 2026 GEP post articulates that “only 5% of organizations surveyed reported operating AI at scale, despite widespread experimentation and investment activity.” The percentages are different. The underlying utilization-gap dynamic is the same.
In 2023, the framework articulated that CPOs were dissatisfied with digital transformation outcomes — not because the technology failed, but because the operational integration into how people work was missing. The 2026 GEP post articulates that “AI exposes operational weaknesses that were easier to work around before. A fragmented approval process may slow adoption. Inconsistent workflows between business units create exceptions the system cannot easily resolve.” Same diagnosis. Different vocabulary.
In 2023, the synopsis articulated digital readiness within a people, process, technology framework. The 2026 GEP study is literally titled The Supply Chain AI Readiness Report.
The 2023 synopsis also surfaced the operating-model ordering question explicitly. The document framed the central question facing mid-market companies as identifying which of three approaches was most effective: process, technology, people — or technology, process, people — or people, process, technology. The framing surfaced that the ordering of the three operating-model elements matters structurally — that placing technology first produces different outcomes than placing people first. The 2026 GEP post articulates the same structural observation in different vocabulary: “the strongest organizations increasingly approach AI as an operational redesign effort rather than a standalone technology deployment. Instead of layering automation onto existing processes, they standardize workflows, clarify governance, and improve data discipline before scaling further.” That is the people-process-technology ordering versus the technology-first ordering. Same observation. Three years apart. Different technology generation.
This is not coincidence. It is what longitudinal documentation looks like when the same underlying operating tensions persist across technology generations and finally become impossible to ignore.
The longer archive thread
The 2023 SIG/GEP synopsis is not where the conversation actually began. It is one node in a documented archive thread that traces back nineteen years through Procurement Insights, and earlier than that through the foundational engagements that shaped the framework’s analytical infrastructure.
In September 2007, the Procurement Insights post Yes Virginia! There Is More to e-Procurement Than Software articulated the substrate thesis directly:
“Growing realization that process, and not technology, is the main force behind successfully achieving results in terms of efficiency and spend rationalization. Specifically, it is through process understanding and refinement combined with the ability to adapt to how the real world operates on the frontlines that credible targets are established and ultimately met.”
That sentence — articulated in September 2007 — is structurally the same observation GEP is now articulating in May 2026. The 2007 piece also documented the “75 to 85% e-procurement initiative failure rate” and surfaced that the Virginia eVA program’s effectiveness “has little to do with the technology and more to do with the methodology the Virginia brain trust employed. It is when technology is seen as the primary vehicle to drive results that it becomes ineffectual and mostly irrelevant.” Same observation. Nineteen years apart. Different technology generation.
Three months later, in December 2007, the post Is Cisco Really Driving 21st Century Supply Chain Innovation? extended the substrate thesis referencing a third-party article on Cisco’s supply chain leadership. The piece quoted Cisco’s senior director of Supply Chain Management on standards-based partner interfaces requiring “a sharing of common goals, common processes and common vision,” with the framework’s structural observation: “Note the absence of a reference to common technologies.” The same piece documented that “initiatives in which change or compliance management is a core element fail 85% of the time.”
Both 2007 pieces drew on the Acres of Diamonds paper the framework had published in the fall of 2004, which articulated the agent-based modeling framework: “A true centralization of procurement objectives requires a decentralized architecture that is based on the real-world operating attributes of all transactional stakeholders starting at the local or regional level. In other words, your organization gains control of its spend environment by relinquishing centralized functional control in favor of operational efficiencies on the front lines.”
The framework was also articulating these principles directly to procurement industry audiences during this period. In June 2005, the framework delivered the keynote presentation at the Power Transmission Distributors Association (PTDA) Canadian Conference in Calgary — a documented industry event with 200 attendees, including 52 distributor delegates from 20 companies and 91 manufacturer delegates from 47 companies. The keynote articulated the substrate-versus-technology thesis directly to that audience: “It wasn’t just the technology. You’ve got to go beyond that and understand the process in terms of how things work.” The keynote documented the 75 percent e-business initiative failure rate as a recurring industry pattern. It articulated the agent-based versus equation-based modeling distinction explicitly — “agent-based modeling, which reflects the unique operating attributes of all transactional stakeholders and understanding how they work separately. Only then can you understand how you can bring them together to work as an efficient tool.” The Metaprise™ framework, the Hansen Strand Commonality™ framework, the dynamic flux commodity distinction, and the documented operational outcomes from the DND engagement (51 percent to 97.3 percent delivery performance improvement, 23 buyers reducing FTEs to 3 within 18 months) were all surfaced in the 2005 keynote as documented institutional articulation of the framework’s analytical infrastructure.
The framework’s analytical infrastructure traces back further still. The 1998 Department of National Defence engagement — Government of Canada SR&ED-funded — produced the foundational proof case through the diagnostic question “what time of day do orders come in?” that surfaced operational reality the central body was not seeing. The DND engagement improved delivery performance from 51 percent to 97.3 percent in three months and sustained that performance for seven years.
The conversation GEP is now articulating in 2026 was articulated openly and actively to procurement industry audiences in 2005. The substrate thesis was formalized in the framework’s 2004 Acres of Diamonds paper. The foundational proof case operated in 1998. The 2007 Procurement Insights archive launch and the 2023 SIG/GEP synopsis were intermediate nodes in a documented archive thread that extends back twenty-eight years through verifiable institutional articulation.
Documented thread across three structurally distinct periods, anchored by the 2005 PTDA Canadian Conference keynote, the 2023 SIG/GEP synopsis, and GEP’s 2026 AI Readiness Report.
What changed between 2023 and 2026
The substantive question worth asking is not why is GEP saying this now? GEP’s evolution is empirically defensible and analytically sound, and the 2026 GEP study with the University of Virginia’s Darden School of Business represents serious institutional work. The substantive question is why did it take three years for this conversation to become unavoidable across the broader market?
AI did not create the operating tensions that GEP’s and others’ 2026 posts engage with. AI accelerated the consequences of leaving them unaddressed.
The framework’s Compounding Technology Shadow Wave™ work articulates this mechanism precisely. The four technology waves operating across the contemporary enterprise — Wave 1 spreadsheets, Wave 2 BYOD, Wave 3 SaaS, Wave 4 AI — are not discrete technology categories with their own discrete governance problems. They are operating simultaneously, at saturation, with AI now running on top of the cumulative stack. AI is the first wave structurally capable of learning from, interacting with, and accelerating all the previous waves at machine cadence.
In 2023, the operating tensions GEP’s 2026 post engages were already visible. Fragmented workflows. Inconsistent governance. Shadow processes. Disconnected data environments. Unresolved operating assumptions. They were visible enough that the framework articulated them in the SIG/GEP white paper synopsis and GEP’s senior team reviewed and engaged the framing.
What was different in 2023 is that organizations could still work around these tensions. The Wave 3 SaaS sprawl was creating drag but not yet acute drag. The Wave 1 spreadsheet persistence was creating fragmentation but not yet acute fragmentation. The operating-model gaps were producing the dissatisfaction CPOs reported, but the dissatisfaction had not yet metabolized into the kind of board-level financial pressure the 2026 environment now produces.
What changed is Wave 4 AI deployment landing on top of the unresolved substrate from the prior three waves. The autonomous-execution pressure that agentic AI introduces does not absorb the operational weaknesses GEP names. It amplifies them at machine speed. “Inconsistent workflows between business units create exceptions the system cannot easily resolve” is true at human speed. At machine speed, the exceptions cascade. The same fragmented workflow that produced manageable drag in 2023 produces acute drag in 2026.
AI didn’t create these weaknesses. It removed the human-speed buffer that used to hide them.
That is why only 6 percent of enterprises see clear financial returns from AI deployment despite 88 percent using AI in some form. That is why GEP’s study found only 5 percent of organizations operating AI at scale in procurement and sourcing. The technology is not failing. The operating substrate underneath the technology is producing the drag that the technology, however sophisticated, cannot resolve from its position downstream.
What this surfaces about the procurement-tech discourse
The procurement-tech discourse is undergoing a structural evolution worth naming explicitly. The vocabulary GEP’s 2026 post deploys — operating models, workflow consistency, governance, data discipline, operational readiness, organizational redesign — is the vocabulary the framework’s archive has been documenting across nineteen years of contemporaneous publication. It is not new vocabulary. The framework was articulating it from the beginning. What has changed is the industry’s contextual timing — the technology generations accumulated, the substrate compounded, the consequences became impossible to work around, and the industry’s analytical infrastructure caught up to what the framework had been documenting in real operational engagements since 1998.
The contextual shift is what AI produced. The substrate condition GEP’s 2026 post engages was always present in the framework’s documented engagements — the DND delivery performance failure in 1998, the Virginia eVA results in 2007, the Cisco supply chain observations in 2007, the e-procurement initiative failure rates documented across the archive. The framework was articulating concrete operational reality from the beginning. The industry has now caught up to the contextual conditions that make those articulations operationally urgent at scale.
The conditions that made the substrate condition visible at scale — the AI-readiness pressure, the board-level financial articulation requirement, the institutional documentation now emerging of how often AI projects stall before reaching measurable returns — are recent. The discourse is now arriving at conclusions that were structurally visible in 2023, and earlier than 2023 for practitioners who had been tracking the recurring pattern across multiple technology waves.
The framework’s nineteen-year archive documents this. The March 2026 Oracle response — “AI doesn’t break broken processes. It perfects them” — operates at the same structural layer as GEP’s 2026 “these are not model problems, they are operational ones.” The July 2011 archive observation — “85% of all eProcurement initiatives fail” — operates at the same structural layer as the contemporary AI deployment failure pattern that institutional research is now documenting at scale. The 1998 Department of National Defence engagement that produced the framework’s foundational proof case operated by asking the diagnostic question — what time of day do orders come in? — that surfaced operational reality the central body was not seeing. Same structural mechanism. Different technology generation.
The framework has been articulating this pattern explicitly since the 2005 PTDA Canadian Conference keynote, the September 2007 Virginia eVA piece, the December 2007 Cisco piece, and the foundational 2004 Acres of Diamonds paper that grounded these articulations in the agent-based modeling framework traced back to the 1998 DND engagement. The framework was articulating it in the 2023 SIG/GEP synopsis. The framework is articulating it now through the Compounding Technology Shadow Wave™ vocabulary, the Phase 0™ pre-commitment substrate diagnostic, the Hansen Deflator Formula™ measurement instrument, and the Hansen Optionality Loss Estimate™ methodology still in development.
What GEP’s 2026 post demonstrates is that the broader market is now arriving at the same recognition. The conversation has evolved to the point where operational readiness can no longer be ignored, because the AI-acceleration consequences have made the prior workarounds structurally untenable.
The procurement community’s opportunity
This is the moment the procurement community has the opportunity to engage substantively. GEP’s 2026 post is one institutional articulation among several that have surfaced in the past six months. Eric Kimberling on agile in ERP operates at the same analytical layer. Microsoft Dynamics 365’s December 2025 Convergence statement that “when data is fragmented across systems, agents can’t act with autonomy” operates at the same layer. The Tealbook–Supplier.io Atlas merger and its supplier data validation architecture operates at the same layer. The trilogy of analytical work the Procurement Insights archive published this week — consolidated in the trilogy executive summaries — operates at the same layer.
The structural condition is consistent across vendor categories, deployment scales, and technology generations. The methodology that surfaces it operates consistently. The diagnostic instrument that addresses it operates upstream of the technology commitment in every case.
The procurement community has the opportunity to engage that recognition substantively or to watch the conversation happen without procurement’s substantive contribution. For CPOs, that means bringing substrate questions, not just tool questions, into the next AI or procurement-tech investment review: where are workflows, governance, and data discipline already failing at human speed that AI will simply accelerate?
As more CIOs begin to recognize the impact of the shadowy waves, are procurement professionals and providers ready to join the conversation?
What the archive demonstrates
The Procurement Insights archive is what makes this longitudinal observation defensible. Nineteen years of contemporaneous, independent publishing — zero vendor sponsorships — produces the kind of documented continuity that allows the framework to surface the archive thread underneath GEP’s 2026 positioning with verifiable evidence rather than retrospective claim.
The archive does not exist to demonstrate that the framework was right earlier than the market. The archive exists to demonstrate that the operating tensions producing AI failure today are the same operating tensions that produced ERP failure in 2011, BPO disappointment in the late 2000s, eProcurement underperformance documented against the 2007 recorded successes of Virginia eVA and Cisco, and supply chain fragmentation across every technology generation that the archive documents. The pattern is recurring. The substrate is durable. The technology generations come and go. The substrate condition persists.
That is what GEP’s 2026 post is now engaging. That is what the broader market is now confronting. That is the structural reality the framework has been documenting since the 1998 DND engagement, since the 2004 Acres of Diamonds paper formalizing the agent-based modeling framework, since the 2005 PTDA Canadian Conference keynote articulating the substrate thesis to a documented procurement industry audience, since the 2007 articulation that process, and not technology, is the main force behind successfully achieving results, through the 2023 SIG/GEP white paper synopsis, and through the contemporary Compounding Technology Shadow Wave™ vocabulary that names the structural condition explicitly.
The technology evolved. The underlying operating tensions did not. AI simply made them impossible to ignore.
That is what readiness is now being tested against. And that is the conversation the procurement community has the opportunity to engage at the analytical depth the moment requires.
—30—
Compounding Technology Shadow Wave™ · Phase 0™ · Implementation Physics™
Hansen Models™ · Founder: Jon W. Hansen · hansenprocurement.com
Procurement Insights
What Three Years of Documented Continuity Reveals About AI Readiness — And Why the Market Is Now Confronting What Was Already Visible in 2023
Posted on May 14, 2026
0
A longitudinal read of how operational-readiness language entered the procurement-tech discourse — and what the archive documents about when the conversation actually began.
The 2026 statement
On May 13, 2026, GEP Worldwide published Why Supply Chain AI Readiness Is Now Measured at Scale, a LinkedIn piece anchored to recent findings from The Supply Chain AI Readiness Report, a study GEP conducted in partnership with the University of Virginia’s Darden School of Business surveying 180 executives. The post is structurally significant for what it surfaces about how the procurement-tech discourse has evolved. The load-bearing observations are worth reading directly.
“These are not model problems. They are operational ones.”
“AI exposes operational weaknesses that were easier to work around before.”
“The strongest organizations increasingly approach AI as an operational redesign effort rather than a standalone technology deployment.”
“Only 5% of organizations surveyed reported operating AI at scale, despite widespread experimentation and investment activity.”
The framing is precise and the analytical contribution is structurally sound. GEP is articulating that the gap between AI experimentation and AI operationalization is not a technology problem but an operating-model problem — that organizations standardize workflows, clarify governance, and improve data discipline before scaling further, or they stall.
The post deserves engagement on its own terms. It is one of the clearer institutional articulations of what the broader procurement-tech market is now beginning to recognize. The framing — “AI readiness is becoming less about experimentation and more about operational consistency” — operates at the level the moment requires.
What makes the post most interesting, however, is not what it says about AI readiness in 2026. What makes it most interesting is what the Procurement Insights archive documents about when this conversation actually began.
The 2023 record
On March 3, 2023, a strategy meeting was held between Sourcing Industry Group (SIG) and GEP to discuss a proposed white paper. The meeting included GEP’s senior team. Transcribed in real time, here some of the key points introduced during the session:
“It’s not a technology problem anymore. It’s now a positioning problem of understanding and how you can assist them to leverage that technology so they begin to grow vertically versus horizontally.”
“One thing that came out of the pandemic is that the majority of companies acknowledge they had technology, but they were stuck. They’ve only been leveraging ten to twenty percent of those.”
“The majority of CPOs aren’t satisfied with the results of their digital transformation strategies.”
“They’re not just looking for providers, they’re looking for partners, especially at the mid-market level.”
Eleven days later, on March 14, 2023, the synopsis document formalizing the proposed white paper’s thesis was drafted with me serving as lead writer on the project. The working title was “Beyond Technology: What Is Your Digital Readiness Within A People, Process, Technology Framework.” The synopsis articulated the thesis in language that warrants careful reading against the 2026 GEP positioning:
“It is not simply the access to technology that leads to successful outcomes, but understanding how to effectively integrate it into how people work in the real world, redefining the processes that drive results and leveraging the technology to make the leap from digital promise to digital realization finally.”
The synopsis was reviewed by GEP, with comments returned on March 20, 2023. The white paper itself did not advance to final production. The strategic conversation it represented, however, was documented contemporaneously and is part of the framework’s nineteen-year Procurement Insights archive.
What the 2023 record actually contains
The substantive observations the framework articulated in that March 2023 conversation are worth surfacing explicitly, because each one maps onto positions GEP’s 2026 post now occupies.
In 2023, the framework articulated that technology was no longer the differentiator. The 2026 GEP post articulates that “the question now is not whether AI can improve sourcing, forecasting, supplier management, or contracting. In many organizations, it already is.” Same observation. Different technology generation.
In 2023, the framework articulated that companies had technology but were stuck — leveraging only ten to twenty percent of capability. The 2026 GEP post articulates that “only 5% of organizations surveyed reported operating AI at scale, despite widespread experimentation and investment activity.” The percentages are different. The underlying utilization-gap dynamic is the same.
In 2023, the framework articulated that CPOs were dissatisfied with digital transformation outcomes — not because the technology failed, but because the operational integration into how people work was missing. The 2026 GEP post articulates that “AI exposes operational weaknesses that were easier to work around before. A fragmented approval process may slow adoption. Inconsistent workflows between business units create exceptions the system cannot easily resolve.” Same diagnosis. Different vocabulary.
In 2023, the synopsis articulated digital readiness within a people, process, technology framework. The 2026 GEP study is literally titled The Supply Chain AI Readiness Report.
The 2023 synopsis also surfaced the operating-model ordering question explicitly. The document framed the central question facing mid-market companies as identifying which of three approaches was most effective: process, technology, people — or technology, process, people — or people, process, technology. The framing surfaced that the ordering of the three operating-model elements matters structurally — that placing technology first produces different outcomes than placing people first. The 2026 GEP post articulates the same structural observation in different vocabulary: “the strongest organizations increasingly approach AI as an operational redesign effort rather than a standalone technology deployment. Instead of layering automation onto existing processes, they standardize workflows, clarify governance, and improve data discipline before scaling further.” That is the people-process-technology ordering versus the technology-first ordering. Same observation. Three years apart. Different technology generation.
This is not coincidence. It is what longitudinal documentation looks like when the same underlying operating tensions persist across technology generations and finally become impossible to ignore.
The longer archive thread
The 2023 SIG/GEP synopsis is not where the conversation actually began. It is one node in a documented archive thread that traces back nineteen years through Procurement Insights, and earlier than that through the foundational engagements that shaped the framework’s analytical infrastructure.
In September 2007, the Procurement Insights post Yes Virginia! There Is More to e-Procurement Than Software articulated the substrate thesis directly:
“Growing realization that process, and not technology, is the main force behind successfully achieving results in terms of efficiency and spend rationalization. Specifically, it is through process understanding and refinement combined with the ability to adapt to how the real world operates on the frontlines that credible targets are established and ultimately met.”
That sentence — articulated in September 2007 — is structurally the same observation GEP is now articulating in May 2026. The 2007 piece also documented the “75 to 85% e-procurement initiative failure rate” and surfaced that the Virginia eVA program’s effectiveness “has little to do with the technology and more to do with the methodology the Virginia brain trust employed. It is when technology is seen as the primary vehicle to drive results that it becomes ineffectual and mostly irrelevant.” Same observation. Nineteen years apart. Different technology generation.
Three months later, in December 2007, the post Is Cisco Really Driving 21st Century Supply Chain Innovation? extended the substrate thesis referencing a third-party article on Cisco’s supply chain leadership. The piece quoted Cisco’s senior director of Supply Chain Management on standards-based partner interfaces requiring “a sharing of common goals, common processes and common vision,” with the framework’s structural observation: “Note the absence of a reference to common technologies.” The same piece documented that “initiatives in which change or compliance management is a core element fail 85% of the time.”
Both 2007 pieces drew on the Acres of Diamonds paper the framework had published in the fall of 2004, which articulated the agent-based modeling framework: “A true centralization of procurement objectives requires a decentralized architecture that is based on the real-world operating attributes of all transactional stakeholders starting at the local or regional level. In other words, your organization gains control of its spend environment by relinquishing centralized functional control in favor of operational efficiencies on the front lines.”
The framework was also articulating these principles directly to procurement industry audiences during this period. In June 2005, the framework delivered the keynote presentation at the Power Transmission Distributors Association (PTDA) Canadian Conference in Calgary — a documented industry event with 200 attendees, including 52 distributor delegates from 20 companies and 91 manufacturer delegates from 47 companies. The keynote articulated the substrate-versus-technology thesis directly to that audience: “It wasn’t just the technology. You’ve got to go beyond that and understand the process in terms of how things work.” The keynote documented the 75 percent e-business initiative failure rate as a recurring industry pattern. It articulated the agent-based versus equation-based modeling distinction explicitly — “agent-based modeling, which reflects the unique operating attributes of all transactional stakeholders and understanding how they work separately. Only then can you understand how you can bring them together to work as an efficient tool.” The Metaprise™ framework, the Hansen Strand Commonality™ framework, the dynamic flux commodity distinction, and the documented operational outcomes from the DND engagement (51 percent to 97.3 percent delivery performance improvement, 23 buyers reducing FTEs to 3 within 18 months) were all surfaced in the 2005 keynote as documented institutional articulation of the framework’s analytical infrastructure.
The framework’s analytical infrastructure traces back further still. The 1998 Department of National Defence engagement — Government of Canada SR&ED-funded — produced the foundational proof case through the diagnostic question “what time of day do orders come in?” that surfaced operational reality the central body was not seeing. The DND engagement improved delivery performance from 51 percent to 97.3 percent in three months and sustained that performance for seven years.
The conversation GEP is now articulating in 2026 was articulated openly and actively to procurement industry audiences in 2005. The substrate thesis was formalized in the framework’s 2004 Acres of Diamonds paper. The foundational proof case operated in 1998. The 2007 Procurement Insights archive launch and the 2023 SIG/GEP synopsis were intermediate nodes in a documented archive thread that extends back twenty-eight years through verifiable institutional articulation.
Documented thread across three structurally distinct periods, anchored by the 2005 PTDA Canadian Conference keynote, the 2023 SIG/GEP synopsis, and GEP’s 2026 AI Readiness Report.
What changed between 2023 and 2026
The substantive question worth asking is not why is GEP saying this now? GEP’s evolution is empirically defensible and analytically sound, and the 2026 GEP study with the University of Virginia’s Darden School of Business represents serious institutional work. The substantive question is why did it take three years for this conversation to become unavoidable across the broader market?
AI did not create the operating tensions that GEP’s and others’ 2026 posts engage with. AI accelerated the consequences of leaving them unaddressed.
The framework’s Compounding Technology Shadow Wave™ work articulates this mechanism precisely. The four technology waves operating across the contemporary enterprise — Wave 1 spreadsheets, Wave 2 BYOD, Wave 3 SaaS, Wave 4 AI — are not discrete technology categories with their own discrete governance problems. They are operating simultaneously, at saturation, with AI now running on top of the cumulative stack. AI is the first wave structurally capable of learning from, interacting with, and accelerating all the previous waves at machine cadence.
In 2023, the operating tensions GEP’s 2026 post engages were already visible. Fragmented workflows. Inconsistent governance. Shadow processes. Disconnected data environments. Unresolved operating assumptions. They were visible enough that the framework articulated them in the SIG/GEP white paper synopsis and GEP’s senior team reviewed and engaged the framing.
What was different in 2023 is that organizations could still work around these tensions. The Wave 3 SaaS sprawl was creating drag but not yet acute drag. The Wave 1 spreadsheet persistence was creating fragmentation but not yet acute fragmentation. The operating-model gaps were producing the dissatisfaction CPOs reported, but the dissatisfaction had not yet metabolized into the kind of board-level financial pressure the 2026 environment now produces.
What changed is Wave 4 AI deployment landing on top of the unresolved substrate from the prior three waves. The autonomous-execution pressure that agentic AI introduces does not absorb the operational weaknesses GEP names. It amplifies them at machine speed. “Inconsistent workflows between business units create exceptions the system cannot easily resolve” is true at human speed. At machine speed, the exceptions cascade. The same fragmented workflow that produced manageable drag in 2023 produces acute drag in 2026.
AI didn’t create these weaknesses. It removed the human-speed buffer that used to hide them.
That is why only 6 percent of enterprises see clear financial returns from AI deployment despite 88 percent using AI in some form. That is why GEP’s study found only 5 percent of organizations operating AI at scale in procurement and sourcing. The technology is not failing. The operating substrate underneath the technology is producing the drag that the technology, however sophisticated, cannot resolve from its position downstream.
What this surfaces about the procurement-tech discourse
The procurement-tech discourse is undergoing a structural evolution worth naming explicitly. The vocabulary GEP’s 2026 post deploys — operating models, workflow consistency, governance, data discipline, operational readiness, organizational redesign — is the vocabulary the framework’s archive has been documenting across nineteen years of contemporaneous publication. It is not new vocabulary. The framework was articulating it from the beginning. What has changed is the industry’s contextual timing — the technology generations accumulated, the substrate compounded, the consequences became impossible to work around, and the industry’s analytical infrastructure caught up to what the framework had been documenting in real operational engagements since 1998.
The contextual shift is what AI produced. The substrate condition GEP’s 2026 post engages was always present in the framework’s documented engagements — the DND delivery performance failure in 1998, the Virginia eVA results in 2007, the Cisco supply chain observations in 2007, the e-procurement initiative failure rates documented across the archive. The framework was articulating concrete operational reality from the beginning. The industry has now caught up to the contextual conditions that make those articulations operationally urgent at scale.
The conditions that made the substrate condition visible at scale — the AI-readiness pressure, the board-level financial articulation requirement, the institutional documentation now emerging of how often AI projects stall before reaching measurable returns — are recent. The discourse is now arriving at conclusions that were structurally visible in 2023, and earlier than 2023 for practitioners who had been tracking the recurring pattern across multiple technology waves.
The framework’s nineteen-year archive documents this. The March 2026 Oracle response — “AI doesn’t break broken processes. It perfects them” — operates at the same structural layer as GEP’s 2026 “these are not model problems, they are operational ones.” The July 2011 archive observation — “85% of all eProcurement initiatives fail” — operates at the same structural layer as the contemporary AI deployment failure pattern that institutional research is now documenting at scale. The 1998 Department of National Defence engagement that produced the framework’s foundational proof case operated by asking the diagnostic question — what time of day do orders come in? — that surfaced operational reality the central body was not seeing. Same structural mechanism. Different technology generation.
The framework has been articulating this pattern explicitly since the 2005 PTDA Canadian Conference keynote, the September 2007 Virginia eVA piece, the December 2007 Cisco piece, and the foundational 2004 Acres of Diamonds paper that grounded these articulations in the agent-based modeling framework traced back to the 1998 DND engagement. The framework was articulating it in the 2023 SIG/GEP synopsis. The framework is articulating it now through the Compounding Technology Shadow Wave™ vocabulary, the Phase 0™ pre-commitment substrate diagnostic, the Hansen Deflator Formula™ measurement instrument, and the Hansen Optionality Loss Estimate™ methodology still in development.
What GEP’s 2026 post demonstrates is that the broader market is now arriving at the same recognition. The conversation has evolved to the point where operational readiness can no longer be ignored, because the AI-acceleration consequences have made the prior workarounds structurally untenable.
The procurement community’s opportunity
This is the moment the procurement community has the opportunity to engage substantively. GEP’s 2026 post is one institutional articulation among several that have surfaced in the past six months. Eric Kimberling on agile in ERP operates at the same analytical layer. Microsoft Dynamics 365’s December 2025 Convergence statement that “when data is fragmented across systems, agents can’t act with autonomy” operates at the same layer. The Tealbook–Supplier.io Atlas merger and its supplier data validation architecture operates at the same layer. The trilogy of analytical work the Procurement Insights archive published this week — consolidated in the trilogy executive summaries — operates at the same layer.
The structural condition is consistent across vendor categories, deployment scales, and technology generations. The methodology that surfaces it operates consistently. The diagnostic instrument that addresses it operates upstream of the technology commitment in every case.
The procurement community has the opportunity to engage that recognition substantively or to watch the conversation happen without procurement’s substantive contribution. For CPOs, that means bringing substrate questions, not just tool questions, into the next AI or procurement-tech investment review: where are workflows, governance, and data discipline already failing at human speed that AI will simply accelerate?
As more CIOs begin to recognize the impact of the shadowy waves, are procurement professionals and providers ready to join the conversation?
What the archive demonstrates
The Procurement Insights archive is what makes this longitudinal observation defensible. Nineteen years of contemporaneous, independent publishing — zero vendor sponsorships — produces the kind of documented continuity that allows the framework to surface the archive thread underneath GEP’s 2026 positioning with verifiable evidence rather than retrospective claim.
The archive does not exist to demonstrate that the framework was right earlier than the market. The archive exists to demonstrate that the operating tensions producing AI failure today are the same operating tensions that produced ERP failure in 2011, BPO disappointment in the late 2000s, eProcurement underperformance documented against the 2007 recorded successes of Virginia eVA and Cisco, and supply chain fragmentation across every technology generation that the archive documents. The pattern is recurring. The substrate is durable. The technology generations come and go. The substrate condition persists.
That is what GEP’s 2026 post is now engaging. That is what the broader market is now confronting. That is the structural reality the framework has been documenting since the 1998 DND engagement, since the 2004 Acres of Diamonds paper formalizing the agent-based modeling framework, since the 2005 PTDA Canadian Conference keynote articulating the substrate thesis to a documented procurement industry audience, since the 2007 articulation that process, and not technology, is the main force behind successfully achieving results, through the 2023 SIG/GEP white paper synopsis, and through the contemporary Compounding Technology Shadow Wave™ vocabulary that names the structural condition explicitly.
The technology evolved. The underlying operating tensions did not. AI simply made them impossible to ignore.
That is what readiness is now being tested against. And that is the conversation the procurement community has the opportunity to engage at the analytical depth the moment requires.
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Compounding Technology Shadow Wave™ · Phase 0™ · Implementation Physics™
Hansen Models™ · Founder: Jon W. Hansen · hansenprocurement.com
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