We spent decades worrying about organizations that did not know enough. Artificial intelligence may give us the opposite problem—organizations capable of knowing almost everything and understanding remarkably little.
By Joe Cozart
For most of modern business history, information was scarce enough to possess value simply because it was difficult to obtain. Companies paid for research because research required people. They commissioned studies because studies required time. They maintained analysts because someone had to find the information, compare it, organize it and eventually place it in front of someone empowered to make a decision. Competitive intelligence was valuable partly because intelligence itself was expensive.
Artificial intelligence is quietly dismantling that economy.
I noticed it recently in something remarkably ordinary: an advertisement describing the leading enterprise uses of AI. The first item was market intelligence. Below it were predictive maintenance, customer-support routing, claims processing, customer onboarding, anti-money laundering, medical-literature synthesis, marketing generation, process automation and regulatory reporting.
What interested me was not that artificial intelligence could perform these functions. By now, that is hardly surprising.
What interested me was what the list revealed about what organizations increasingly want machines to do.
They want machines to notice.
They want machines to watch enormous environments, identify changes, classify signals, recognize patterns, compare possibilities and bring something important to human attention before a human being would otherwise have discovered it.
That changes the problem.
For years, the central question surrounding artificial intelligence has been what the machine can produce. Can it write? Can it code? Can it create an image? Can it summarize a report? Can it analyze a spreadsheet? Those were necessary questions during the introduction of the technology because production was the easiest capability to see.
But I suspect the greater economic transformation will come from something less theatrical.
Artificial intelligence is creating an enormous expansion in observational capacity.
An organization that once had five analysts can increasingly observe as though it had fifty. A company that previously reviewed a handful of competitors can continuously examine an industry. Regulatory changes that once required someone to discover them can be monitored automatically. Scientific literature can be synthesized continuously. Customer behavior can be examined almost as it occurs. Operational anomalies can be identified before someone notices that something feels wrong.
Information scarcity begins disappearing.
And that sounds unquestionably beneficial until I ask the next question.
What happens when intelligence becomes abundant?
I believe we are about to discover that intelligence abundance creates an entirely different category of organizational problem.
Knowing more does not guarantee understanding more.
Under certain conditions, knowing more may actually make understanding harder.
Every organization has a finite capacity for attention. It has a finite capacity for judgment. It has a finite capacity for implementation. It has a finite capacity for changing direction. Artificial intelligence does not automatically remove those limitations. Instead, it can dramatically increase the amount of information arriving at the doors of systems that were never designed to absorb it.
That is what I think of as the intelligence surplus.
And surplus changes value.
When something is scarce, acquiring it carries a premium. When it becomes abundant, value migrates toward whatever remains scarce.
I believe that is beginning to happen with intelligence.
If every organization can research the market, analyze competitors, summarize regulations, model scenarios, monitor customers, synthesize scientific literature and generate strategic alternatives at extraordinary speed, possessing intelligence becomes less differentiating.
Everyone has it.
The advantage moves somewhere else.
It moves to interpretation.
From interpretation, it moves to judgment.
From judgment, it moves to decision.
And from decision, ultimately, to whether an organization can actually do something consequential with what it knows.
That progression interests me far more than the current competition over which organization can deploy the largest number of AI tools.
I can give an organization more information without making it wiser. I can give its executives better dashboards without improving their decisions. I can place an AI agent inside every department without changing the organizational assumptions that caused those departments to misunderstand one another in the first place. I can automate workflows without ever asking whether those workflows should exist.
That last possibility deserves considerably more attention than it receives.
Automation contains an implicit seduction. When a process becomes faster, we instinctively perceive improvement. Numbers move. Reports appear. Tasks disappear from queues. Response times decline. Productivity metrics improve.
Activity accelerates.
But activity and progress are not synonymous.
An organization that confuses activity with progress can move quickly for years while repeatedly discovering that one more unresolved variable stands between demonstration and deployment.
Artificial intelligence does not eliminate that danger.
It can magnify it.
Give a coherent organization artificial intelligence and it may become extraordinarily capable.
Give an incoherent organization artificial intelligence and it may become extraordinarily efficient at reproducing its incoherence.
I think that distinction will ultimately prove more important than many of the technological distinctions receiving attention today.
It also explains why I have become increasingly interested in the distance between demonstration and deployment.
Demonstrating that a technology works is not the same thing as demonstrating that a system works.
A machine can perform beautifully while the surrounding organization remains incapable of deploying it reliably. The technology can be ready while procurement is not. Engineering can be ready while regulation is not. Economics can appear attractive while the operating model remains unresolved. The demonstration can succeed while the ecosystem required to sustain it does not yet exist.
Every individual component can work while the system itself remains undeployable.
Artificial intelligence will encounter precisely the same problem.
An AI agent successfully completing a task is a demonstration.
An enterprise reliably incorporating thousands or millions of AI-assisted observations and decisions into its operations is a system.
Those are very different accomplishments.
And somewhere between them lie governance, accountability, validation, institutional knowledge, security, economics, human judgment, regulatory exposure, organizational incentives and the uncomfortable question of who is responsible when an extraordinarily capable machine is confidently wrong.
The great corporate AI race may therefore produce an unexpected result.
Organizations may spend billions acquiring more intelligence only to discover that intelligence was never their principal constraint.
The constraint was their ability to make sense of it.
That possibility has changed the way I think about my own work.
I am not particularly interested in becoming another AI consultant. There will be no shortage of people capable of recommending models, agents, platforms and automation. There will be technology companies far better positioned than I am to build those technologies, and organizations should use them.
My interest begins somewhere else.
I want to know what happens after the intelligence arrives.
What reaches the decision-maker?
What should reach the decision-maker?
What gets lost between discovery and decision?
Which signals receive attention because they are important, and which receive attention simply because they are measurable?
Where has information accumulated without changing understanding?
Where are different parts of an organization independently holding pieces of the same answer?
Where has technological capability been mistaken for organizational readiness?
Where has a successful demonstration quietly become evidence for a deployment that the surrounding system cannot yet support?
And where has activity been mistaken for progress?
Those questions define the space in which I believe I can be most useful.
If I were brought into an organization confronting this problem, I would not begin by asking how much artificial intelligence it has deployed. I would begin with something much simpler.
Give me one consequential decision.
Not a hypothetical decision. Not a demonstration. Not a strategy exercise.
A real decision the organization needs to make.
Then I would follow the intelligence backward.
I would want to understand what information reaches that decision, where it originated, what assumptions accompanied it, who interpreted it, what never reached the room, what arrived but should not have mattered, and what information somewhere else in the organization might materially change the decision if the people making it knew that information existed.
I would want the technologists in that room. I would want the operators. I would want the executives, engineers, analysts and subject-matter experts. They possess knowledge I do not possess, and pretending otherwise would make the exercise useless.
My role would be different.
I would be looking across what they know.
That distinction is important.
I do not need to know more about engineering than the engineer. I do not need to understand medicine better than the physician, autonomous systems better than the autonomy engineer, finance better than the CFO or artificial intelligence better than the people building the models.
I need to see what happens between them.
That space between expertise is where surprisingly consequential things can disappear.
One person understands the technology. Another understands the economics. Another understands regulation. Another understands operations. Another understands the customer. Another understands the political environment. Each can be entirely correct within his or her own domain while the organization remains wrong about the system.
That is not a failure of intelligence.
It is a failure of coherence.
And artificial intelligence makes this distinction more urgent because it is about to give every one of those specialists considerably more intelligence.
More research.
More predictions.
More scenarios.
More alerts.
More correlations.
More recommendations.
More answers.
The organization can become smarter everywhere and clearer nowhere.
That is the problem I want to work on.
I think of it as examining the distance between intelligence and decision.
That distance can be mapped. Its friction can be identified. Its assumptions can be surfaced. Its blind spots can be exposed. Its unnecessary complexity can be removed. Its dependencies can be understood. And the distinction between what is technologically possible and what is organizationally deployable can be made visible before enormous amounts of capital and time are committed to discovering it the hard way.
The result should not be another AI strategy sitting on a shelf.
It should be a clearer operating environment for decisions.
Leadership should understand which intelligence deserves escalation. Teams should know where human judgment remains essential. AI systems should have defined purposes rather than unlimited mandates. Assumptions should become visible enough to challenge. Demonstrations should face deployment questions early rather than after years of investment. And organizational activity should be continuously tested against the considerably harder standard of progress.
There is another reason I think this matters.
Artificial intelligence is rapidly lowering the price of answers.
That may turn out to be one of its most profound economic consequences.
Research that once required days can increasingly happen in minutes. Alternative scenarios can be produced almost instantaneously. Thousands of pages can be synthesized. Competing arguments can be constructed. Assumptions can be tested. Patterns can be surfaced.
Answers become abundant.
And when answers become abundant, the economics invert.
The question becomes more valuable.
So does judgment.
So does context.
So does knowing which answer deserves attention.
So does recognizing when the question itself is wrong.
And so does the ability to distinguish signal from noise, correlation from causation, capability from deployability, urgency from importance and motion from progress.
That is where I see my work going.
Not toward competing with artificial intelligence, but toward working at the point where artificial intelligence makes an older human problem considerably more consequential.
Clarity.
We spent decades worrying about organizations that did not know enough.
Artificial intelligence may give us the opposite problem: organizations capable of knowing almost everything and understanding remarkably little.
That is the intelligence surplus.
And the competitive advantage in an intelligence surplus will not belong to the organization possessing the most information.
It will belong to the organization capable of seeing clearly enough to know what all that information means—and disciplined enough to know what to do next.
That is the work I want to help build.
——— GMJoe™ ———
Clarity. Strategy. Sovereignty.
Books by Joe Cozart are available at: amazon.com/author/joecozart
GMJoe.org
Joe Cozart is a writer and consultant based in North Dakota. His work explores the intersection of political performance, cultural clarity, and the architecture of power. He is the creator of the GMJoe™ consulting voice and author of several longform essay series exploring sovereignty, institutional systems, industrial civilization, and the recursive tensions shaping the modern age.