Yet they are all pointing toward the same underlying condition.
None of them treats AI capability as the determining variable.
They are about what happens after capability exists.
Governance.
Verification.
Decision rights.
Human judgment.
Organizational absorption.
Authority.
Accountability.
Operational survivability.
In other words, they are all asking variations of the same question:
Can the organization absorb what the technology produces?
That question has followed me across every technology era.
ERP.
E-procurement.
Cloud.
SaaS.
RPA.
Platforms.
AI.
Agentic AI.
The technology changes.
The question remains remarkably consistent.
That consistency is the observation worth examining. Each technology era arrived with its own analytical vocabulary, its own industry framing, its own promised outcomes. The vocabulary evolved across eras. The fundamental question of whether organizations could absorb what the technology produced remained essentially unchanged across all of them.
In 1998, while working with Canada’s Department of National Defence, we faced a persistent next-day delivery problem.
The organization had capable people.
Capable suppliers.
Capable systems.
Capable processes.
Yet the expected outcomes were not materializing.
The breakthrough did not come from a better system.
It came from a simple question:
What time of day do orders come in?
That question revealed hidden conditions that no one had previously considered.
The architecture wasn’t failing because of technology.
It was failing because the model of the problem was incomplete.
Which raises an interesting question for today’s AI discussions.
As organizations build agents, orchestration layers, MCP servers, AI IDEs, and increasingly autonomous workflows:
How does the system know to ask the question nobody thinks matters?
How does it know to challenge the assumptions embedded in the model itself?
Because sometimes the difference between success and failure is not better execution.
It is identifying the hidden conditions that collectively determine whether an architecture survives contact with reality.
That was true in 1998.
It remains true today.
Tell me:
How would Agentic AI have known to ask, “What time of day do orders come in?”
Why Would AI Produce Any Better Results Than Previous Technologies?
Posted on May 29, 2026
0
Over the past few days, a series of LinkedIn posts jumped off my screen:
At first glance, these posts appear unrelated.
Different authors.
Different industries.
Different technologies.
Different use cases.
Different perspectives.
Yet they are all pointing toward the same underlying condition.
None of them treats AI capability as the determining variable.
They are about what happens after capability exists.
Governance.
Verification.
Decision rights.
Human judgment.
Organizational absorption.
Authority.
Accountability.
Operational survivability.
In other words, they are all asking variations of the same question:
Can the organization absorb what the technology produces?
That question has followed me across every technology era.
ERP.
E-procurement.
Cloud.
SaaS.
RPA.
Platforms.
AI.
Agentic AI.
The technology changes.
The question remains remarkably consistent.
That consistency is the observation worth examining. Each technology era arrived with its own analytical vocabulary, its own industry framing, its own promised outcomes. The vocabulary evolved across eras. The fundamental question of whether organizations could absorb what the technology produced remained essentially unchanged across all of them.
In 1998, while working with Canada’s Department of National Defence, we faced a persistent next-day delivery problem.
The organization had capable people.
Capable suppliers.
Capable systems.
Capable processes.
Yet the expected outcomes were not materializing.
The breakthrough did not come from a better system.
It came from a simple question:
What time of day do orders come in?
That question revealed hidden conditions that no one had previously considered.
The architecture wasn’t failing because of technology.
It was failing because the model of the problem was incomplete.
Which raises an interesting question for today’s AI discussions.
As organizations build agents, orchestration layers, MCP servers, AI IDEs, and increasingly autonomous workflows:
How does the system know to ask the question nobody thinks matters?
How does it know to challenge the assumptions embedded in the model itself?
Because sometimes the difference between success and failure is not better execution.
It is identifying the hidden conditions that collectively determine whether an architecture survives contact with reality.
That was true in 1998.
It remains true today.
Tell me:
How would Agentic AI have known to ask, “What time of day do orders come in?”
-30-
HPT: https://hansenprocurement.com/
Share this:
Like this:
Related