Inaccuracy and Inconsistency: Early forecasts were often unreliable, especially beyond 24 hours. Limited scientific understanding, primitive instruments, and a lack of real-time data led to frequent errors, undermining public confidence.
Lack of Transparency: Forecasts were issued without clear explanations of uncertainty or confidence levels. The public did not understand the limitations or reasoning behind predictions, making errors seem arbitrary.
Communication Gaps: Forecasts were sometimes outdated or not updated promptly (e.g., railway signal discs not changed daily), leading to confusion and skepticism about their usefulness.
Perceived Disconnect from Local Experience: Users, such as sailors and farmers, often felt that official forecasts ignored their practical knowledge or local conditions, fueling mistrust in the “scientific” approach.
Resource and Technology Constraints: Underfunded meteorological services struggled to process and disseminate data efficiently, resulting in delays and gaps in information
As I read through the above list, it occurred to me that we could reasonably replace the words “forecasting” and “meteorological” with terms like “Generative AI” and “Agentic AI.” In fact, looking at the table below, the first four obstacles and concerns for weather forecasting are the ones that currently exist for Regular, Generative AI, and Agentic AI. YES runs the table of all four.
Summary
Similarities: Both early weather forecasting and AI technologies have faced mistrust due to inaccuracy, lack of transparency, and a perceived disconnect from user needs.
Differences: AI technologies introduce new dimensions of mistrust—bias, privacy, autonomy, and ethical risks—that were not present in the context of weather forecasting. Agentic AI, in particular, raises concerns about loss of human control and unintended consequences, while Generative AI is scrutinized for its potential to spread misinformation and disrupt creative industries.
What is even more telling to me is the “mistrust” percentages listed in the following table. You will note that the 1975 public trust level for weather forecasting was 45% compared to 35% and 30% for Generative AI and Agentic AI, respectively, in 2025. In short, the level of skepticism is usually high for most new technological breakthroughs.
“In essence, while the roots of mistrust—accuracy, and transparency—are shared, the scope and stakes of public skepticism have expanded dramatically with the rise of AI.“
Even though trust in AI is inevitable, eventually coming relatively close to weather forecasting by 2075, why, as the quote above reports, has skepticism “expanded dramatically with the rise of AI?”
WHAT WAS I THINKING?
Now, at this point, you may be wondering, “Why is Hansen, a procurement guy, writing about the weather and AI in a procurement blog?” It is a fair question, for which I will provide you with a straightforward answer: What do weather sensors have to do with AI?
“Modern weather forecasting accuracy has dramatically improved due to several technological advancements in sensor design, deployment, and data integration.”
How does this relate to the design, deployment, and integration of Generative AI and Agentic AI?
Let’s consider the following:
What Generative AI and Weather Forecast Models Have in Common
Summary: Generative AI is to linguistic reality what weather models are to atmospheric reality. Both build likely futures from chaotic, multivariable pasts.
What Weather Sensors and Agentic AI Agents Have in Common
Summary: Weather sensors and agentic AI agents are decentralized inputs that collectively shape real-time situational awareness and response.
Combined Insight:
In a procurement context, Generative AI = macro-pattern modeler, while agentic AI = micro-action executor. Just like weather models rely on sensors for accuracy, GenAI needs agentic feedback loops to stay grounded.
NOW, THIS IS WHY I TRUST THE PROMISE AND POWER OF AI
The above graph is the enhanced layered diagram integrating the Hansen Models into the Generative AI + Weather Forecasting framework:
Hansen Model Overlay:
Agent-Based Model – Layered atop data collection – Enables dynamic feedback and micro-resistance sensing
Strand Commonality Model – Aligns with the processing layer – Detects recurring patterns across agents, data points, and supplier interactions
Metaprise Model – Sits at the strategic foresight and orchestration layer – Powers high-level, adaptive enterprise-wide decisions
This visual shows how Hansen Models enhance each system layer, making AI systems:
More responsive (Agent-based)
More pattern-aware (Strand Commonality)
More strategically aligned (Metaprise)
TODAY’S TAKEAWAY
When the weather person tells you on Sunday that there is a 75% chance of a major thunderstorm with hail on Monday, do you plan an outing to the beach, have a picnic at a local park, or look at getting in a round of golf at your club that day?
If your answer is no, you have just taken an essential step towards trusting the future of AI in procurement. I may have a slight advantage, because after 40 years, my confidence in the Metaprise, Agent-based, and Strand Commonality models to elevate AI to the next, practical level is as sure as the sun rising on the Tuesday morning after the storm.
30
BONUS COVERAGE
I wonder how much Duke Energy, Walmart, and Estee Lauder think about the weather?
Company Profiles:
Insights:
Walmart: Slightly lower fit, but notable strength in Strand Commonality (scale and repetition) and room for improvement in Agent-Based integration.
Duke Energy: Highest alignment, especially with Metaprise (enterprise foresight) and Agent-Based Modeling (due to operational complexity and regulatory layers).
Estée Lauder: Strong in Strand Commonality—consistent with a pattern-heavy procurement environment (brands, suppliers, packaging).
Dimensions Added:
ROI Impact: How much financial return is likely from Hansen Model adoption
Strategic KPI Alignment: Fit with procurement performance goals (cycle time, cost savings, supplier risk)
Change Management Alignment: Compatibility with frameworks like ADKAR, Kotter’s 8-Step, and others
Company Highlights:
Company
ROI Impact
Strategic KPI Alignment
Change Mgmt Alignment
Duke Energy
92
89
91
Walmart
75
77
74
Estée Lauder
86
83
88
Insights:
Duke Energy continues to demonstrate the strongest overall alignment, particularly in KPI integration and organizational readiness for change.
Estée Lauder is closely aligned across all dimensions—ideal for transformation at scale, yet with the nuance of the consumer sector.
Walmart shows value in KPI patterning (Strand Commonality) but could improve in change enablement and agentic foresight.
Procurement Insights
What Does The Weather Have To Do With Procurement And AI?
Posted on July 5, 2025
0
Early forecasts were often unreliable, especially beyond 24 hours. Limited scientific understanding, primitive instruments, and a lack of real-time data led to frequent errors, undermining public confidence.
Forecasts were issued without clear explanations of uncertainty or confidence levels. The public did not understand the limitations or reasoning behind predictions, making errors seem arbitrary.
Forecasts were sometimes outdated or not updated promptly (e.g., railway signal discs not changed daily), leading to confusion and skepticism about their usefulness.
Users, such as sailors and farmers, often felt that official forecasts ignored their practical knowledge or local conditions, fueling mistrust in the “scientific” approach.
Underfunded meteorological services struggled to process and disseminate data efficiently, resulting in delays and gaps in information
As I read through the above list, it occurred to me that we could reasonably replace the words “forecasting” and “meteorological” with terms like “Generative AI” and “Agentic AI.” In fact, looking at the table below, the first four obstacles and concerns for weather forecasting are the ones that currently exist for Regular, Generative AI, and Agentic AI. YES runs the table of all four.
Summary
Both early weather forecasting and AI technologies have faced mistrust due to inaccuracy, lack of transparency, and a perceived disconnect from user needs.
AI technologies introduce new dimensions of mistrust—bias, privacy, autonomy, and ethical risks—that were not present in the context of weather forecasting. Agentic AI, in particular, raises concerns about loss of human control and unintended consequences, while Generative AI is scrutinized for its potential to spread misinformation and disrupt creative industries.
What is even more telling to me is the “mistrust” percentages listed in the following table. You will note that the 1975 public trust level for weather forecasting was 45% compared to 35% and 30% for Generative AI and Agentic AI, respectively, in 2025. In short, the level of skepticism is usually high for most new technological breakthroughs.
“In essence, while the roots of mistrust—accuracy, and transparency—are shared, the scope and stakes of public skepticism have expanded dramatically with the rise of AI.“
Even though trust in AI is inevitable, eventually coming relatively close to weather forecasting by 2075, why, as the quote above reports, has skepticism “expanded dramatically with the rise of AI?”
WHAT WAS I THINKING?
Now, at this point, you may be wondering, “Why is Hansen, a procurement guy, writing about the weather and AI in a procurement blog?” It is a fair question, for which I will provide you with a straightforward answer: What do weather sensors have to do with AI?
“Modern weather forecasting accuracy has dramatically improved due to several technological advancements in sensor design, deployment, and data integration.”
How does this relate to the design, deployment, and integration of Generative AI and Agentic AI?
Let’s consider the following:
What Generative AI and Weather Forecast Models Have in Common
Summary: Generative AI is to linguistic reality what weather models are to atmospheric reality. Both build likely futures from chaotic, multivariable pasts.
What Weather Sensors and Agentic AI Agents Have in Common
Combined Insight:
NOW, THIS IS WHY I TRUST THE PROMISE AND POWER OF AI
The above graph is the enhanced layered diagram integrating the Hansen Models into the Generative AI + Weather Forecasting framework:
Hansen Model Overlay:
– Layered atop data collection
– Enables dynamic feedback and micro-resistance sensing
– Aligns with the processing layer
– Detects recurring patterns across agents, data points, and supplier interactions
– Sits at the strategic foresight and orchestration layer
– Powers high-level, adaptive enterprise-wide decisions
This visual shows how Hansen Models enhance each system layer, making AI systems:
TODAY’S TAKEAWAY
When the weather person tells you on Sunday that there is a 75% chance of a major thunderstorm with hail on Monday, do you plan an outing to the beach, have a picnic at a local park, or look at getting in a round of golf at your club that day?
If your answer is no, you have just taken an essential step towards trusting the future of AI in procurement. I may have a slight advantage, because after 40 years, my confidence in the Metaprise, Agent-based, and Strand Commonality models to elevate AI to the next, practical level is as sure as the sun rising on the Tuesday morning after the storm.
30
BONUS COVERAGE
I wonder how much Duke Energy, Walmart, and Estee Lauder think about the weather?
Company Profiles:
Insights:
Walmart: Slightly lower fit, but notable strength in Strand Commonality (scale and repetition) and room for improvement in Agent-Based integration.
Duke Energy: Highest alignment, especially with Metaprise (enterprise foresight) and Agent-Based Modeling (due to operational complexity and regulatory layers).
Estée Lauder: Strong in Strand Commonality—consistent with a pattern-heavy procurement environment (brands, suppliers, packaging).
Dimensions Added:
Company Highlights:
Insights:
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