How AI Exposes the Assumptions You Don't Know You're Making

Severin Sorensen
3 days ago
5 min read

By the time you bring a decision to a conversation, you have usually already made up your mind.

You may not realize it. The situation may feel genuinely open, the options unresolved. But in most cases, the moment you begin describing a problem to another person, you have already climbed a cognitive staircase that began with a selective observation and ended with a firmly held conclusion, moving through layers of interpretation, assumption, and belief so quickly that the process left no visible trace.

Chris Argyris named this staircase the Ladder of Inference. Peter Senge brought it to a broader organizational audience through The Fifth Discipline. The model is well known in leadership development circles. The challenge has always been personal: how do you see the rungs you skipped when the climb happened automatically and the view from the top feels like obvious truth?

AI provides a practical answer to that question for the first time.

A Brief Account of the Ladder

Argyris described the Ladder of Inference as the mental pathway through which human beings move from raw data to action. The progression begins at the observable facts of a situation and moves upward through a series of interpretive steps: we select the data that seems relevant, we interpret what that data means based on prior experience, we form assumptions from those interpretations, we draw conclusions from our assumptions, we build or reinforce our beliefs from those conclusions, and finally we take action based on those beliefs.

The problem is not the process. The problem is the speed and the invisibility of it. By the time you frame a situation as requiring a decision, you have already selected your data, interpreted it through your existing mental models, and formed a conclusion that now presents itself as self-evident. The early rungs of the ladder are gone from view.

Argyris further observed that individuals tend to operate from a reflexive loop in which their beliefs about the world determine which data they select, reinforcing those same beliefs over time. Leaders who are never asked to examine the bottom rungs of their own ladder become progressively more confident and progressively less accurate. The more experience you accumulate, the more invisible this pattern becomes.

What AI Can Do That a Trusted Advisor Cannot Easily Do Alone

A skilled advisor, peer, or coach can ask powerful questions that slow you down and encourage reflection on your reasoning. What no human interlocutor can do efficiently, in real time, with the full breadth of a situation in view, is independently reconstruct your inferential chain from the available facts and name, in specific terms, where your account diverges from the observable data.

This is precisely where AI becomes a powerful thinking instrument. When you describe a situation to an AI system and ask it to separate observable data from interpretation and assumption, the output creates a map of the ladder that you could not have produced as quickly or as dispassionately on your own. The AI has no stake in your conclusion, no relationship to protect, and no career risk from telling you that your reasoning rests on something that has not been verified.

Using the WHISPER Framework leveraged in PromptSensei, for structured AI collaboration, an executive might open this dialogue as follows:

"You are a strategic thinking partner trained in organizational behavior and decision science, with a specific focus on the Ladder of Inference developed by Chris Argyris. I am a senior executive working through a high-stakes situation, and I want us to examine my reasoning together before I act. Where my account is incomplete or where you need clarification to do this accurately, ask me before drawing any conclusions. Your tone should be analytically honest and direct, the kind of assessment a trusted advisor with no personal stake in the outcome would offer. Our purpose is to separate what I can actually observe from what I am interpreting or assuming, so that I can identify where my reasoning may be carrying more weight than my evidence warrants. I will describe the situation in full. Once I have, please organize my account into three categories: what is directly observable or verifiable, what represents my interpretation of those observations, and what appears to be an assumption I am treating as established fact. Where an interpretation or assumption seems to be doing significant work in my reasoning, flag it and ask me what evidence I am drawing on. Present your analysis in a format I can sit with before my next major decision. Here is the situation: [insert details here]."

The output will not tell you what to decide. It will show you the structure of how you arrived at your current position, including the places where your confidence exceeds your evidence. That is the information most executives never see, because most of the people around them are too invested in the outcome to name it.

Three Patterns That Surface Repeatedly

In working with executives who use AI-assisted ladder analysis, three assumption patterns appear with notable consistency.

Attribution without evidence. Executives frequently ascribe intentions, motivations, or attitudes to others based on behavior alone. A board member who asked a pointed question in a meeting is characterized as adversarial. A direct report who missed a deadline is described as disengaged. The behavior is observable. The attribution is an inference, and it is often carrying enormous weight in the decisions that follow.
Selective data framing. Under pressure, it is natural to present the evidence that supports the conclusion you have already reached and to omit the evidence that complicates it, often without realizing you are doing so. AI, given the full account, will identify when your reasoning would change materially if certain facts were included.
Generalizations from single events. A single failed initiative becomes evidence that the organization cannot execute. A single difficult quarter becomes evidence that the strategy is fundamentally flawed. AI will identify when a conclusion of significant scope is being drawn from a data set too narrow to support it.

The Shift This Creates in How You Lead

When you examine your own reasoning before acting, rather than after a decision has gone wrong, the dynamic of every subsequent conversation changes. You arrive at the table having already interrogated your assumptions. You know which parts of your position are grounded in evidence and which parts are grounded in belief. That self-awareness sharpens your authority.

The questions you ask of your team will be more precise. The moments when you choose to hold your position or revise it will be better calibrated. The probability of reaching the decisions that prove durable over time increases substantially, because you are no longer confusing confidence with clarity.

Argyris spent decades arguing that the Ladder of Inference was one of the most important tools available for helping intelligent people learn from experience. The barrier has always been practical: surfacing the ladder requires time, honesty, and someone with no stake in your conclusions. AI provides exactly that, and it does so before the decision is made.

References

Argyris, C. (1990). Overcoming Organizational Defenses: Facilitating Organizational Learning. Pearson Education / Prentice Hall. ISBN: 978-0205123384

Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. Doubleday. ISBN: 978-0385517256