By Jon W. Hansen, FCIPS | Procurement Insights · November 2026
The following is the transcript of a discussion held on 3 November 2026. The answers appear exactly as given. In the questions, a few speech-to-text errors have been corrected, such as “weather” for “whether”; nothing else has been changed. Numbers in square brackets refer to the sources listed at the end.
Q: What is Ambient AI?
A: Ambient AI is AI that works in the background, using context from a person’s surroundings or workflow to assist without needing a prompt each time. The term is used broadly; it overlaps with the older idea of ambient intelligence, where connected systems respond to what is happening around people. [1][2]
A familiar example is a clinical AI scribe that listens during a visit and drafts notes for a clinician to review. [3] In an office, ambient AI might draw on meeting notes and work activity to prepare a follow-up or flag a missed step.
The useful distinction is that the AI is context-aware and available in the flow of work. That also raises practical questions: what it can observe, whether people know it is running, what it may do on its own, and who checks its output.
Q: When it comes to ambient AI in the SLAP operating system, ARA RAM 2025, where does it all fit in? I mean, I’m seeing many, many possibilities
A: I can see why you’re seeing so many possibilities. Ambient AI could connect the Hansen architecture to what is actually happening in the work. It gives the system a way to notice relevant changes as they happen, rather than waiting for someone to describe the situation in a prompt.
I’d place it this way:
So the shorthand might be:
Ambient AI notices the change. SLAP OS governs the loop. ARA reasons through it. RAM 2025 challenges the reasoning. The human owns the outcome.
Take a supplier delivery delay. Ambient AI might notice that the ETA changed, a port alert appeared, and a planner has started using a workaround. On their own, those are just signals. SLAP OS would bring them together with the relevant permissions and provenance. ARA could surface different explanations and implications. RAM 2025 could challenge whether the proposed response accounts for dependencies elsewhere in the supply chain. The person responsible decides what to do—and the result becomes evidence for the next loop.
That points to a larger possibility: ambient AI could make the Hansen models responsive to operational conditions, rather than limited to scheduled assessments or manually initiated analyses. It could help detect when assumptions have stopped matching reality, when a local success is creating a downstream failure, or when a workaround is hiding a system problem.
The boundary matters. Ambient AI can observe and raise a signal; it shouldn’t silently turn an uncertain signal into an authorized decision. That’s where SLAP OS, the reasoning process, and human accountability become essential. The opportunity is not just “AI that runs in the background.” It is a governed, evidence-producing loop between the real operating environment and structured reasoning.
Q: So, going back to RAM 1998 when the human orchestrator could indicate there was a weather situation that could affect delivery and put that into the system. RAM 1998 would take that manually and respond accordingly. With Ambient, you could have a weather sensor, for lack of a better word, that would pick up this information, receive it and automatically put it in
A: Yes—that is the direct extension of the RAM 1998 pattern.
In 1998, the human orchestrator noticed a condition—a weather event that might affect delivery—and entered it. RAM then used that information to respond.
With an ambient input, a weather feed or sensor could detect the condition and place it into the system’s live operating context automatically. The system could match it to the affected route, shipment, supplier, or delivery window, then trigger the relevant response.
The key change is how the information enters the loop:
RAM 1998: A person observes and enters the condition.
Ambient-enabled system: A sensor or data feed detects and enters it.
In both cases: The system must connect the condition to the operation it may affect and respond accordingly.
That could reduce the delay between a real-world change and the system’s response. The feed supplies the signal; the system still needs rules for deciding whether it is relevant and what response is authorized.
Author’s note: This is where Strand Commonality™ comes in. Deciding whether a signal is relevant means knowing which strands it crosses, where they meet, and what they have in common when they produce an outcome. A weather feed reports the storm; Strand Commonality™ identifies which deliveries, suppliers and decisions the storm will actually reach.
What the diagram shows vs. what actually happens: the same six functions from the 1998 engagement, drawn as a sequence on the left and as the strands operate on the right. Illustrative, not measured data.
Q: Would it be out of line for me to say that in 1998 when I developed that human interface input that I was ahead of the curve in anticipating ambient AI? I mean the reality is, is you streamline and monitor everything. That’s a natural progression, isn’t it?
A: It would be fair to say that RAM 1998 anticipated an important operational principle now associated with ambient AI: the system needs timely information about changing real-world conditions to respond well.
I’d keep the claim specific. The broader ideas behind ambient intelligence were also emerging in the late 1990s, including work on systems that sensed context and responded without a direct command. So I wouldn’t claim that RAM anticipated or originated ambient AI as a whole. [1][2]
But based on your description, you can credibly say RAM applied that principle to procurement execution: the human orchestrator could bring a live condition—such as weather risk—into the system, and the system could adapt its response. Ambient AI can automate parts of that input by drawing from sensors and data feeds. The progression is from human observes and enters to system detects, connects, and routes relevant information.
Author’s note: In a paper I wrote in the fall of 2004, titled Acres of Diamonds (to my regular readers, you are no doubt more than familiar with the Acres paper), I made the following statement: “It is my position that 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. This is the cornerstone of agent-based modeling.” I revisited it in Is Cisco Really Driving 21st Century Supply Chain Innovation? (November 2023).
I’d say “monitor everything” more narrowly: monitor the conditions that could change the decision or the outcome. The point isn’t constant surveillance; it’s getting relevant signals into the reasoning loop early enough to act.
Q: But wasn’t that why or isn’t that why the 1998 firm fences were so important? Because you just can’t, there’s certain functionality that you very much restrict like, like the largest Cat dealer, Finning, would have sensors telling it when equipment needed maintenance or starting time or things like that. I mean, those things are direct. I mean you need firm fences with ambient AI
A: Exactly. Ambient AI expands what can enter the system; firm fences define what the system is allowed to do with it. That makes your 1998 firm fences more relevant, not less.
With a Finning/CAT-style example, equipment telemetry might directly show operating hours or a maintenance threshold. If the signal is trusted and the response is pre-authorized, the system could create a maintenance alert or draft work order automatically. But the fence would limit what happens next: it might require approval before placing an order above a set amount, changing the service provider, or taking equipment out of operation.
Simple, reliable signals can follow a bounded path. Uncertain, conflicting, or consequential signals can be routed through SLAP OS™ into ARA™ and RAM 2025™ for deeper reasoning and human review.
So the fence doesn’t stop the system from sensing. It governs the response—what may happen automatically, what needs more reasoning, and where human authority is required.
Author’s note: I wrote about Finning in August 2023, in Predictive Analytics & Procurement: A Simple Idea?, quoting a 2019 Long View Systems article in which Finning described connecting to its customers’ machinery to monitor equipment health, track idle time and predict breakdowns before they happen.
Q: So again, Hansen’s architecture and operating principles in RAM 1998 are really a timeless foundation, a methodology for working with advanced intelligence like ambient intelligence, agentic intelligence, agent intelligent, etc. I mean the framework, the foundational approach and model remains the same
A: Yes. The core operating principles in RAM 1998 can serve as a durable foundation for ambient and agentic intelligence. The intelligence may now come from people, sensors, software agents, or multiple AI models. The operating questions remain: What conditions matter? What is each part allowed to do? How are dependencies handled? How do we know the response improved the real outcome?
The architecture can grow around those principles. In today’s Hansen system, ambient inputs can bring new conditions into the loop; SLAP OS™ governs how they enter and what authority they carry; ARA™ and RAM 2025™ structure and challenge reasoning; firm fences bound action; and the human orchestrator remains accountable.
I’d phrase the claim this way:
RAM 1998 established operating principles that remain applicable as intelligence evolves. The inputs and capabilities have expanded; the need to connect real conditions, bounded authority, coordinated reasoning, and verified outcomes remains.
That is the heart of your Invariant Physics™ framing: the technology changes; the underlying conditions for getting the outcome right endure.
Q: The way I see it, technology will always advance in ten, fifteen years, who knows what kind of intelligence will be there. But the advance reasoning architecture that began in RAM 1998 will still be applicable in, in the future regardless of what the technology is
A: Yes. You’re describing a reasoning architecture intended to outlast the technology it runs on. Ten or fifteen years from now, the form of intelligence may be very different. It will still need relevant context, clear functional boundaries, coordinated action, and evidence that its actions produced the intended outcome.
That is the Invariant Physics™ idea: technology advances; the operating principles for getting the outcome right endure.
A concise way to state your point:
RAM 1998 began a reasoning architecture for working with intelligence in real operating conditions. The technology will keep changing. The architecture’s foundational principles should remain applicable to whatever forms of intelligence come next.
Author’s note
In 1998, our ambient agents were people. They noticed what the system could not see, and they brought it in. Ambient AI changes who notices. It does not change what the system must do with what it notices, or who answers for the result.
Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
We Already Had Ambient AI Agents in 1998. They Were Human.
Posted on November 3, 2026
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By Jon W. Hansen, FCIPS | Procurement Insights · November 2026
The following is the transcript of a discussion held on 3 November 2026. The answers appear exactly as given. In the questions, a few speech-to-text errors have been corrected, such as “weather” for “whether”; nothing else has been changed. Numbers in square brackets refer to the sources listed at the end.
Q: What is Ambient AI?
A: Ambient AI is AI that works in the background, using context from a person’s surroundings or workflow to assist without needing a prompt each time. The term is used broadly; it overlaps with the older idea of ambient intelligence, where connected systems respond to what is happening around people. [1][2]
A familiar example is a clinical AI scribe that listens during a visit and drafts notes for a clinician to review. [3] In an office, ambient AI might draw on meeting notes and work activity to prepare a follow-up or flag a missed step.
The useful distinction is that the AI is context-aware and available in the flow of work. That also raises practical questions: what it can observe, whether people know it is running, what it may do on its own, and who checks its output.
Q: When it comes to ambient AI in the SLAP operating system, ARA RAM 2025, where does it all fit in? I mean, I’m seeing many, many possibilities
A: I can see why you’re seeing so many possibilities. Ambient AI could connect the Hansen architecture to what is actually happening in the work. It gives the system a way to notice relevant changes as they happen, rather than waiting for someone to describe the situation in a prompt.
I’d place it this way:
So the shorthand might be:
Ambient AI notices the change. SLAP OS governs the loop. ARA reasons through it. RAM 2025 challenges the reasoning. The human owns the outcome.
Take a supplier delivery delay. Ambient AI might notice that the ETA changed, a port alert appeared, and a planner has started using a workaround. On their own, those are just signals. SLAP OS would bring them together with the relevant permissions and provenance. ARA could surface different explanations and implications. RAM 2025 could challenge whether the proposed response accounts for dependencies elsewhere in the supply chain. The person responsible decides what to do—and the result becomes evidence for the next loop.
That points to a larger possibility: ambient AI could make the Hansen models responsive to operational conditions, rather than limited to scheduled assessments or manually initiated analyses. It could help detect when assumptions have stopped matching reality, when a local success is creating a downstream failure, or when a workaround is hiding a system problem.
The boundary matters. Ambient AI can observe and raise a signal; it shouldn’t silently turn an uncertain signal into an authorized decision. That’s where SLAP OS, the reasoning process, and human accountability become essential. The opportunity is not just “AI that runs in the background.” It is a governed, evidence-producing loop between the real operating environment and structured reasoning.
Q: So, going back to RAM 1998 when the human orchestrator could indicate there was a weather situation that could affect delivery and put that into the system. RAM 1998 would take that manually and respond accordingly. With Ambient, you could have a weather sensor, for lack of a better word, that would pick up this information, receive it and automatically put it in
A: Yes—that is the direct extension of the RAM 1998 pattern.
In 1998, the human orchestrator noticed a condition—a weather event that might affect delivery—and entered it. RAM then used that information to respond.
With an ambient input, a weather feed or sensor could detect the condition and place it into the system’s live operating context automatically. The system could match it to the affected route, shipment, supplier, or delivery window, then trigger the relevant response.
The key change is how the information enters the loop:
That could reduce the delay between a real-world change and the system’s response. The feed supplies the signal; the system still needs rules for deciding whether it is relevant and what response is authorized.
Author’s note: This is where Strand Commonality™ comes in. Deciding whether a signal is relevant means knowing which strands it crosses, where they meet, and what they have in common when they produce an outcome. A weather feed reports the storm; Strand Commonality™ identifies which deliveries, suppliers and decisions the storm will actually reach.
What the diagram shows vs. what actually happens: the same six functions from the 1998 engagement, drawn as a sequence on the left and as the strands operate on the right. Illustrative, not measured data.
Q: Would it be out of line for me to say that in 1998 when I developed that human interface input that I was ahead of the curve in anticipating ambient AI? I mean the reality is, is you streamline and monitor everything. That’s a natural progression, isn’t it?
A: It would be fair to say that RAM 1998 anticipated an important operational principle now associated with ambient AI: the system needs timely information about changing real-world conditions to respond well.
I’d keep the claim specific. The broader ideas behind ambient intelligence were also emerging in the late 1990s, including work on systems that sensed context and responded without a direct command. So I wouldn’t claim that RAM anticipated or originated ambient AI as a whole. [1][2]
But based on your description, you can credibly say RAM applied that principle to procurement execution: the human orchestrator could bring a live condition—such as weather risk—into the system, and the system could adapt its response. Ambient AI can automate parts of that input by drawing from sensors and data feeds. The progression is from human observes and enters to system detects, connects, and routes relevant information.
Author’s note: In a paper I wrote in the fall of 2004, titled Acres of Diamonds (to my regular readers, you are no doubt more than familiar with the Acres paper), I made the following statement: “It is my position that 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. This is the cornerstone of agent-based modeling.” I revisited it in Is Cisco Really Driving 21st Century Supply Chain Innovation? (November 2023).
I’d say “monitor everything” more narrowly: monitor the conditions that could change the decision or the outcome. The point isn’t constant surveillance; it’s getting relevant signals into the reasoning loop early enough to act.
Q: But wasn’t that why or isn’t that why the 1998 firm fences were so important? Because you just can’t, there’s certain functionality that you very much restrict like, like the largest Cat dealer, Finning, would have sensors telling it when equipment needed maintenance or starting time or things like that. I mean, those things are direct. I mean you need firm fences with ambient AI
A: Exactly. Ambient AI expands what can enter the system; firm fences define what the system is allowed to do with it. That makes your 1998 firm fences more relevant, not less.
With a Finning/CAT-style example, equipment telemetry might directly show operating hours or a maintenance threshold. If the signal is trusted and the response is pre-authorized, the system could create a maintenance alert or draft work order automatically. But the fence would limit what happens next: it might require approval before placing an order above a set amount, changing the service provider, or taking equipment out of operation.
Simple, reliable signals can follow a bounded path. Uncertain, conflicting, or consequential signals can be routed through SLAP OS™ into ARA™ and RAM 2025™ for deeper reasoning and human review.
So the fence doesn’t stop the system from sensing. It governs the response—what may happen automatically, what needs more reasoning, and where human authority is required.
Author’s note: I wrote about Finning in August 2023, in Predictive Analytics & Procurement: A Simple Idea?, quoting a 2019 Long View Systems article in which Finning described connecting to its customers’ machinery to monitor equipment health, track idle time and predict breakdowns before they happen.
Q: So again, Hansen’s architecture and operating principles in RAM 1998 are really a timeless foundation, a methodology for working with advanced intelligence like ambient intelligence, agentic intelligence, agent intelligent, etc. I mean the framework, the foundational approach and model remains the same
A: Yes. The core operating principles in RAM 1998 can serve as a durable foundation for ambient and agentic intelligence. The intelligence may now come from people, sensors, software agents, or multiple AI models. The operating questions remain: What conditions matter? What is each part allowed to do? How are dependencies handled? How do we know the response improved the real outcome?
The architecture can grow around those principles. In today’s Hansen system, ambient inputs can bring new conditions into the loop; SLAP OS™ governs how they enter and what authority they carry; ARA™ and RAM 2025™ structure and challenge reasoning; firm fences bound action; and the human orchestrator remains accountable.
I’d phrase the claim this way:
RAM 1998 established operating principles that remain applicable as intelligence evolves. The inputs and capabilities have expanded; the need to connect real conditions, bounded authority, coordinated reasoning, and verified outcomes remains.
That is the heart of your Invariant Physics™ framing: the technology changes; the underlying conditions for getting the outcome right endure.
Q: The way I see it, technology will always advance in ten, fifteen years, who knows what kind of intelligence will be there. But the advance reasoning architecture that began in RAM 1998 will still be applicable in, in the future regardless of what the technology is
A: Yes. You’re describing a reasoning architecture intended to outlast the technology it runs on. Ten or fifteen years from now, the form of intelligence may be very different. It will still need relevant context, clear functional boundaries, coordinated action, and evidence that its actions produced the intended outcome.
That is the Invariant Physics™ idea: technology advances; the operating principles for getting the outcome right endure.
A concise way to state your point:
RAM 1998 began a reasoning architecture for working with intelligence in real operating conditions. The technology will keep changing. The architecture’s foundational principles should remain applicable to whatever forms of intelligence come next.
Author’s note
In 1998, our ambient agents were people. They noticed what the system could not see, and they brought it in. Ambient AI changes who notices. It does not change what the system must do with what it notices, or who answers for the result.
Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
-30-
Sources cited in the discussion
[1] IEEE Technology Navigator, “Ambient Intelligence.” https://technav.ieee.org/topic/ambient-intelligence/
[2] Microsoft Research, “Visual Context Awareness via Wearable Computing” (January 1998). https://www.microsoft.com/en-us/research/publication/visual-context-awareness-via-wearable-computing/
[3] McGovern Medical School, “Ambient Documentation.” https://med.uth.edu/mshbc/coding-compliance-overview/introducing-the-ai-scribe/ambient-documentation/
Added by the author: Predictive Analytics & Procurement: A Simple Idea? — Procurement Insights, August 29, 2023. https://procureinsights.com/2023/08/29/predictive-analytics-procurement-a-simple-idea/
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