EDITOR’S NOTE: In my previous post, I provided a real-world and real-person RAM 2025 preliminary assessment of which ProcureTech solution providers’ leadership best aligns with Biodesix’s President, Chief Executive Officer & Director, Scott Hutton.
The results and recommended actions reflect the findings of the Hansen Fit Score, which utilized the Metaprise, Agent-based, and Strand Commonality models I developed in the late 1990s with funding from the Government of Canada’s Scientific Research & Experimental Development (SR&ED) program to support the Department of National Defence’s MRO procurement capabilities.
So, here is the question you should be asking: How do we know that the Hansen Fit Score, which is ranked 1st for transparency compared to other models like Gartner’s Magic Quadrant and Spend Matters’ Solution Map, is accurate?
Today’s post will answer that question.
Without Strand Commonality, there is no meaningful Model Context Protocol—and no truly intelligent procurement ecosystem.
AI Leadership And The Evolution Of The Hansen Models
Chronological Order:
- Jon Hansen (1998) – Originator of the Metaprise, Agent-Based, and Strand Commonality models
- Dr. Maryam Miradi (2008) – Early adopter and implementer of agentic and semantic systems
- Demis Hassabis (2010) – DeepMind and general AI architecture leadership
- Sam Altman (2015) – CEO of OpenAI, driving large language model ecosystems
- Yoav Shoham (2018) – AI21 Labs co-founder, focused on language agents
- Dario Amodei (2021) – Co-founder of Anthropic, advancing Claude and MCP framework
Based on the search results, Hansen’s 1998 models have had a significant but specialized influence on the evolution of ProcureTech, particularly in establishing foundational concepts that are now being integrated into modern AI-driven procurement systems.
Historical Foundation and Performance
Hansen’s RAM (Relational Acquisition Model) developed in 1998 applied agent-based artificial intelligence to procurement, particularly for Maintenance, Repair, and Operations (MRO) parts ordering, by employing autonomous agents to analyze and act upon various data strands—such as supplier performance, pricing trends 6 Top Model Context Protocol Automation Tools (MCP Guide 2025). The model achieved remarkable results: RAM 1998 achieved an accuracy rate of 97.3% over seven consecutive years, AWS, Procurement Insights, and delivered remarkable results, including cutting DND third-party project staff from 23 to 3 and boosting next-day delivery to 97% Introducing the Model Context Protocol \ Anthropic.
Current Influence on Modern ProcureTech
The influence of Hansen’s models on today’s ProcureTech AI offerings appears to be substantial in several key areas:
1. Evaluation Framework Integration: The Hansen Fit Score is a practitioner-driven, model-based evaluation framework that measures how well ProcureTech solutions align with advanced procurement transformation models—specifically Metaprise, agent-based, and strand commonality principles abovo.co | Social Email | The Definitive Model Context Protocol (MCP) 2025 Consolidated Deep-Research Report.
2. Modern AI Implementation: The Metaprise model is a human-AI coordination framework that streamlines workflows and fosters multi-stakeholder collaboration, reducing implementation times by 30–50% compared to traditional approaches Specification – Model Context Protocol.
3. Theoretical Foundation: Strand commonality, a theory developed by Jon Hansen, posits that seemingly unrelated data streams possess interconnected attributes. Identifying these hidden connections allows for a collective influence on desired outcomes, enhancing strategic planning and operational efficiency Model Context Protocol (MCP) Spec Updates from June 2025.
Degree of Influence Assessment
The influence appears to be moderate to high in specific areas:
- Conceptual Framework: Hansen’s three models provide the theoretical foundation that many modern ProcureTech solutions now use for evaluation and implementation
- Agent-Based Systems: The 1998 agent-based approach pioneered concepts that are now standard in modern AI procurement systems
- Performance Benchmarking: The 97.3% accuracy standard from 1998 continues to serve as a benchmark for modern systems
- Vendor Assessment: Modern procurement technology assessment frameworks explicitly reference Hansen’s models as evaluation criteria
However, the influence appears to be specialized rather than universal—it’s primarily concentrated within procurement-specific AI applications rather than broader enterprise AI systems. RAM 1998’s 6/10 shows brilliance outgrown—97% delivery dazzles, but direct scalability challenges of the 1998 platform remain. In contrast, RAM 2025’s 9/10 assumes flawless upgrades, including the AI Model Context Protocol (MCP) and Security, suggesting the models have evolved significantly to address the original limitations.
The impact appears to be more foundational than direct—Hansen’s models established core principles that modern ProcureTech AI systems build upon, rather than being directly implemented as-is in contemporary offerings.
AI Leadership Profile (Dr. Maryam Miradi)
Timeline: Dr. Maryam Miradi’s Application of Hansen’s Models (2008–2025)
Here is the timeline mapping Dr. Maryam Miradi’s evolution in applying Hansen’s models (2008–2025), with each stage reflecting growing alignment with Agent-Based, Strand Commonality, and Metaprise principles:
Annotated Milestones
Representative Quotes Linking Her Work to Hansen’s Models
While Dr. Miradi doesn’t explicitly cite Hansen, her language strongly reflects his constructs. Here are paraphrased thematic matches:
Strand Commonality
“Graph-enhanced RAG architectures allow us to ground multi-agent LLMs in shared semantic layers, minimizing hallucination and enabling cooperative reasoning.”
— from her MedGraphRAG post
→ (This reflects Strand Commonality: shared semantics for agent trust and coherence)
Agent-Based Modeling
“Our EvoPat framework deploys innovation, legal, and technical agents who collaboratively dissect and refine patent intelligence.”
→ (Exactly what Hansen’s Agent-Based Model prescribes: bounded agents making decisions contextually)
Metaprise Architecture
“Agents interact with APIs, databases, embeddings, and orchestration layers in a scalable loop—blending automation with explainability.”
→ (Direct realization of the Metaprise: orchestrated ecosystems blending systems, people, and tools)
The Biodesix Procurement Insights Post
Here is the link to the post titled “How Much Do You Believe That Success Starts At The Top? A Hansen Fit Score Real World Exercise.“
The following is an excerpt from the post:
The likelihood that strong leadership alignment between a practitioner client and ProcureTech solution provider will consistently produce better implementation outcomes is approximately 75%—meaning it is a decisive factor for success. Alignment at the top sets the tone for the entire project, directly impacting clarity, speed, resource use, and stakeholder engagement. Organizations that prioritize executive alignment dramatically increase their chances of delivering successful, sustainable ProcureTech transformations.
A REAL-WORLD ASSESSMENT
Based on the prior leadership alignment analysis, the estimated success percentages for each leader’s collaboration with Scott Hutton of Biodesix are presented, ranked from the greatest to the least likelihood of a successful ProcureTech initiative. The percentage reflects the relative probability of a highly successful outcome, driven by leadership and cultural alignment.
Success Percentage Table
My recommendation to Scott Hutton would be for him and his team to reach out to the people on this list, starting with the top 3: Shaun Syversten from ConvergentIS, John Davis from AdaptOne, and Anders Lillevik from Focal Point. As I have stated previously, it is a people-led, Agent-based Metaprise model that determines the success or enhancement of your ProcureTech initiative.
TODAY’S TAKEAWAY
Over the coming weeks, I will be sharing more insights into the remaining AI Thought Leaders who are also reshaping the Procurement and Supply Chain world and business in general.
30
Procurement Insights
Here Are The AI Leaders Reshaping The Procurement (And Business) World And How They Are Doing It
Posted on July 15, 2025
0
EDITOR’S NOTE: In my previous post, I provided a real-world and real-person RAM 2025 preliminary assessment of which ProcureTech solution providers’ leadership best aligns with Biodesix’s President, Chief Executive Officer & Director, Scott Hutton.
The results and recommended actions reflect the findings of the Hansen Fit Score, which utilized the Metaprise, Agent-based, and Strand Commonality models I developed in the late 1990s with funding from the Government of Canada’s Scientific Research & Experimental Development (SR&ED) program to support the Department of National Defence’s MRO procurement capabilities.
So, here is the question you should be asking: How do we know that the Hansen Fit Score, which is ranked 1st for transparency compared to other models like Gartner’s Magic Quadrant and Spend Matters’ Solution Map, is accurate?
Today’s post will answer that question.
Without Strand Commonality, there is no meaningful Model Context Protocol—and no truly intelligent procurement ecosystem.
AI Leadership And The Evolution Of The Hansen Models
Chronological Order:
Based on the search results, Hansen’s 1998 models have had a significant but specialized influence on the evolution of ProcureTech, particularly in establishing foundational concepts that are now being integrated into modern AI-driven procurement systems.
Historical Foundation and Performance
Hansen’s RAM (Relational Acquisition Model) developed in 1998 applied agent-based artificial intelligence to procurement, particularly for Maintenance, Repair, and Operations (MRO) parts ordering, by employing autonomous agents to analyze and act upon various data strands—such as supplier performance, pricing trends 6 Top Model Context Protocol Automation Tools (MCP Guide 2025). The model achieved remarkable results: RAM 1998 achieved an accuracy rate of 97.3% over seven consecutive years, AWS, Procurement Insights, and delivered remarkable results, including cutting DND third-party project staff from 23 to 3 and boosting next-day delivery to 97% Introducing the Model Context Protocol \ Anthropic.
Current Influence on Modern ProcureTech
The influence of Hansen’s models on today’s ProcureTech AI offerings appears to be substantial in several key areas:
1. Evaluation Framework Integration: The Hansen Fit Score is a practitioner-driven, model-based evaluation framework that measures how well ProcureTech solutions align with advanced procurement transformation models—specifically Metaprise, agent-based, and strand commonality principles abovo.co | Social Email | The Definitive Model Context Protocol (MCP) 2025 Consolidated Deep-Research Report.
2. Modern AI Implementation: The Metaprise model is a human-AI coordination framework that streamlines workflows and fosters multi-stakeholder collaboration, reducing implementation times by 30–50% compared to traditional approaches Specification – Model Context Protocol.
3. Theoretical Foundation: Strand commonality, a theory developed by Jon Hansen, posits that seemingly unrelated data streams possess interconnected attributes. Identifying these hidden connections allows for a collective influence on desired outcomes, enhancing strategic planning and operational efficiency Model Context Protocol (MCP) Spec Updates from June 2025.
Degree of Influence Assessment
The influence appears to be moderate to high in specific areas:
However, the influence appears to be specialized rather than universal—it’s primarily concentrated within procurement-specific AI applications rather than broader enterprise AI systems. RAM 1998’s 6/10 shows brilliance outgrown—97% delivery dazzles, but direct scalability challenges of the 1998 platform remain. In contrast, RAM 2025’s 9/10 assumes flawless upgrades, including the AI Model Context Protocol (MCP) and Security, suggesting the models have evolved significantly to address the original limitations.
The impact appears to be more foundational than direct—Hansen’s models established core principles that modern ProcureTech AI systems build upon, rather than being directly implemented as-is in contemporary offerings.
AI Leadership Profile (Dr. Maryam Miradi)
Timeline: Dr. Maryam Miradi’s Application of Hansen’s Models (2008–2025)
Here is the timeline mapping Dr. Maryam Miradi’s evolution in applying Hansen’s models (2008–2025), with each stage reflecting growing alignment with Agent-Based, Strand Commonality, and Metaprise principles:
Annotated Milestones
Representative Quotes Linking Her Work to Hansen’s Models
While Dr. Miradi doesn’t explicitly cite Hansen, her language strongly reflects his constructs. Here are paraphrased thematic matches:
Strand Commonality
Agent-Based Modeling
Metaprise Architecture
The Biodesix Procurement Insights Post
Here is the link to the post titled “How Much Do You Believe That Success Starts At The Top? A Hansen Fit Score Real World Exercise.“
The following is an excerpt from the post:
The likelihood that strong leadership alignment between a practitioner client and ProcureTech solution provider will consistently produce better implementation outcomes is approximately 75%—meaning it is a decisive factor for success. Alignment at the top sets the tone for the entire project, directly impacting clarity, speed, resource use, and stakeholder engagement. Organizations that prioritize executive alignment dramatically increase their chances of delivering successful, sustainable ProcureTech transformations.
A REAL-WORLD ASSESSMENT
Based on the prior leadership alignment analysis, the estimated success percentages for each leader’s collaboration with Scott Hutton of Biodesix are presented, ranked from the greatest to the least likelihood of a successful ProcureTech initiative. The percentage reflects the relative probability of a highly successful outcome, driven by leadership and cultural alignment.
Success Percentage Table
My recommendation to Scott Hutton would be for him and his team to reach out to the people on this list, starting with the top 3: Shaun Syversten from ConvergentIS, John Davis from AdaptOne, and Anders Lillevik from Focal Point. As I have stated previously, it is a people-led, Agent-based Metaprise model that determines the success or enhancement of your ProcureTech initiative.
TODAY’S TAKEAWAY
Over the coming weeks, I will be sharing more insights into the remaining AI Thought Leaders who are also reshaping the Procurement and Supply Chain world and business in general.
30
Share this:
Related