Before the Organization Acts
Now AI can generate analyses and explanations on demand, the harder management task is deciding what the organization understands well enough to act upon
Sales at a consumer brand fall by eight percent.
Within minutes, its AI tools produce several explanations. The brand is losing relevance. Prices have become too high. Distribution has weakened. Product quality has slipped. The wider economy is slowing demand. A final synthesis argues that all five are interacting.
Each account is plausible. Each can be supported with data, charts, and a coherent narrative. None is obviously sufficient.
The organization is not short of explanations. It is short of a reasoned basis for choosing what to believe and what to do.
This is where much of the current discussion about AI and management remains incomplete. We ask whether AI can analyze more data, identify patterns, or improve decisions. Increasingly, it can. McKinsey, for example, describes AI as a potential researcher, interpreter, thought partner, simulator, and communicator in strategy work. That is a useful account of its expanding analytical role. It does not settle the harder question that follows: what should the organization treat as a sufficiently grounded understanding of the situation?
That question cannot be answered by the model alone. Nor should it be answered by a heroic manager who claims to see the whole picture. The understanding required for action is distributed across people, functions, records, tools, and experience. It is often incomplete, contested, and shaped by interests as well as evidence.
Management has long had to act without certainty. AI does not remove that condition. It changes the speed, volume, and apparent authority of the claims competing for attention.
The situation does not arrive already explained
Data are often discussed as though they were neutral facts waiting to be discovered. In practice, data are traces of a situation, shaped by decisions about what to measure, how to classify it, whose behavior to record, and what to ignore.
A fall in sales is observable. Its meaning is not. The same number may point toward weaker demand, poor availability, a pricing problem, a competitor move, or a temporary reporting effect. Before the team debates what the data mean, someone has already determined which data reached the meeting and how they were displayed.
The evidence placed before management is therefore not the situation itself. It is a selected and structured representation of it. This does not make the evidence useless or merely subjective. It means that analysis begins with choices and assumptions that should remain visible.
John Dewey described inquiry as the effort to transform an uncertain situation into one sufficiently settled to support intelligent action. His point was not that managers can wait until uncertainty disappears. Inquiry is a disciplined attempt to improve the quality of the situation by observing, questioning, and testing.
The order of operations matters. As I argued in Prediction First, Data Second, useful inquiry tends to begin with a disciplined prior, a working expectation that the evidence can then correct, rather than with the data alone.
AI enters this process after, and increasingly during, the selection and organization of evidence. It can reveal patterns that people missed. It can also make the limits of the available representation easier to forget.
AI expands interpretive capacity, not certainty
The most important change may not be that organizations have more data. They have lived with data abundance for years. It is that AI makes it dramatically easier to produce analyses, alternative hypotheses, and persuasive explanations from that data.
This can improve inquiry. A management team can ask the system to challenge its favored explanation, identify missing variables, compare the problem with similar cases, or show how different assumptions change the conclusion. AI can widen the set of possibilities considered before the organization settles too quickly on the first acceptable story.
But the same capacity has a less reassuring side. A fluent explanation can appear stronger than the evidence behind it. Research on AI-assisted judgment has found that explanations can increase reliance on both correct and incorrect answers. Other work distinguishes explanations that sound plausible from explanations that faithfully reflect how a model reached its conclusion.
The problem is not that AI always misleads. It is that coherence becomes cheaper to produce. A polished account may be insightful, derivative, weakly grounded, or simply wrong, while sounding much the same in each case.
More interpretations can protect an organization from blind spots. They can also create the illusion that producing more accounts is the same as understanding the situation.
AI may also narrow interpretation rather than expand it. Teams using similar models, prompts, and corporate documents may receive multiple answers that are variations of the same underlying frame. Apparent diversity of output is not necessarily diversity of thought.
From competing accounts to a working understanding
An organization cannot investigate forever. At some point it must allocate resources, change a price, delay a launch, revise a product, or decide not to act. The need for commitment gives management a responsibility that cannot be reduced to producing more analysis.
That responsibility is not to declare what is finally true. It is to organize a process through which competing accounts can be surfaced, challenged, and compared, and then to authorize a working understanding proportionate to the stakes.
Herbert Simon’s work on bounded rationality remains useful here. Managers operate with limited time, attention, and knowledge. They cannot examine every possibility or calculate every consequence. But this does not mean accepting the first answer that appears “good enough.” It means matching the depth of inquiry to the importance, reversibility, and urgency of the decision.
A small pricing experiment may justify quick action and rapid learning. Closing a factory, entering a politically unstable market, or making a decision that affects people’s livelihoods demands a much stronger evidential basis and a wider hearing of affected perspectives.
The threshold is therefore contextual. What must be understood before acting depends on the potential harm, the cost of delay, the availability of feedback, and the ease with which the decision can be reversed.
Understanding is distributed, contested, and shaped by power
No manager owns the organization’s understanding of a situation. Frontline employees may see customer reactions before the data capture them. Technical specialists may recognize weaknesses hidden inside a summary. Local teams may understand conditions that headquarters treats as noise. Customers, suppliers, and external researchers may hold knowledge unavailable inside the firm.
Edwin Hutchins showed that complex cognition can be distributed across people, tools, and representations rather than residing in one person’s head. This is a useful way to think about organizations. Their capacity to understand depends on how these different contributions are connected, not on the brilliance of a single decision-maker.
The contributions are not equal in visibility or authority. Rank, professional status, incentives, and existing investments shape which explanations receive attention. A numerical analysis may outrank frontline experience because it looks objective. A senior executive’s favored account may survive despite weak evidence because too much has already been invested in it. An AI synthesis may acquire authority because it is comprehensive and fluent, even when its assumptions remain hidden.
This is why the passage from interpretation to action is not purely technical. It is also institutional. Management must ask not only which account appears best supported, but how it acquired the standing to guide action and whose knowledge may have been excluded.
Alignment around an action is not the same as agreement about reality.
A team may decide to proceed while preserving a serious dissenting view. A board may authorize a pilot precisely because the evidence remains inconclusive. A business may coordinate around one course of action without pretending that uncertainty or disagreement has disappeared.
Some disagreements cannot be settled by more evidence
Not every management dispute is a disagreement about facts. Two executives may agree on the likely commercial outcome and disagree about whether the risk is acceptable. A restructuring decision can involve questions of fairness, loyalty, and obligation that no forecast can resolve. A pricing decision may depend on whether the organization values immediate margin or long-term trust.
AI can clarify the trade-offs. It can model scenarios, identify affected groups, and test the consistency of an argument. It cannot convert a conflict of values into a purely factual problem.
This distinction matters because organizations often hide value choices inside analytical language. A recommendation may be presented as “what the data say” when the data have only estimated consequences. The decision about which consequences matter remains a human and institutional judgment.
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Before committing the organization, management should therefore separate at least three questions: What appears to be happening? What might follow from different actions? Which consequences is the organization prepared to accept?
Action is part of inquiry
Yet “before” should not be read as suggesting a clean sequence in which understanding is completed and then action begins. Organizations often learn by acting. They test, observe consequences, and revise their understanding.
Karl Weick emphasized that people partly make sense of situations through what they do and what their actions bring into view. Action does not merely follow interpretation. It changes the situation and generates new evidence.
This is especially important when uncertainty is high. A reversible experiment may produce better knowledge than another month of abstract debate. The responsible question is not always “Do we know enough for the final decision?” It may be “What limited action would help us learn without exposing the organization to unacceptable harm?”
A responsible commitment therefore names the current working view, the assumptions carrying it, the evidence that would weaken it, and the point at which the organization will review the result. It does not confuse decisiveness with permanence. The required discipline is not endless caution. It is the ability to commit while remaining open to correction.
The danger of institutionalized misunderstanding
The most serious risk is not that one AI analysis will be wrong. Organizations have long made mistakes. The deeper danger is that a weakly grounded account becomes embedded in targets, budgets, workflows, and incentives before it has been adequately challenged.
Once that happens, the organization begins producing evidence that appears to confirm its own interpretation. A sales team told that the problem is weak execution is measured more aggressively. A product team told that quality is not the issue receives no resources to investigate complaints. A pricing strategy built around assumed price sensitivity changes customer behavior in ways later treated as proof of the original assumption.
AI can accelerate this process by turning a tentative account into a polished plan, presentation, and operating model within hours. The speed is useful only if the organization has equally strong ways to preserve uncertainty, expose assumptions, and revisit the account after action begins.
In earlier pieces, I examined how cheap execution can make judgment more valuable and how managers can lose the cognitive practice needed to supervise automated work. The concern here is different. It is how a provisional explanation acquires institutional force and begins shaping what the organization can subsequently see.
What management must organize
Management does not need to monopolize interpretation. It needs to create a process by which the organization can move from distributed and contested claims to responsible commitment.
That process should bring relevant perspectives into view, connect claims to evidence, expose assumptions, protect serious dissent, and distinguish empirical disagreement from disagreement about values. It should also establish when further inquiry is justified and when delay has become its own costly decision.
One practical way to make distributed understanding more visible is to map how an issue is interpreted and acted upon across the organization: which activities shape it, what information enters at different points, which people and systems influence its interpretation, and where commitments are made or actions initiated. The value of such a map lies not only in the finished representation, but in the disagreements and omissions that emerge while it is being constructed. It can reveal where different parts of the organization are working from different accounts of the same situation.
A map, however, is not the situation itself. It simplifies, selects, and gives some relationships greater visibility than others. Its purpose should therefore be to make assumptions examinable, not to give a contested understanding the appearance of settled fact.
For consequential commitments, managers should be able to answer several plain questions:
• What is our current working account of the situation?
• Which evidence supports it, and how independent are the sources?
• What important evidence is missing or excluded?
• Which competing explanation has not been adequately ruled out?
• What values and interests influence the choice?
• What would cause us to revise our view?
• Can we learn through a limited and reversible action before making a larger commitment?
These questions do not guarantee a correct decision. No process can. They make it harder for fluency, hierarchy, or institutional convenience to pass unnoticed as understanding.
Before the organization acts
AI will continue to expand the analytical reach of organizations. It will generate hypotheses, compare scenarios, and challenge established views. In many cases, it will improve the quality and speed of inquiry. But improvement is not automatic. A recent meta-analysis found that combinations of humans and AI often perform worse than the best of either working alone, and that the gains vary with the task. Whether an organization captures them depends on how the collaboration is organized.
The advantage will not come from having the most analyses or the most eloquent explanations. It will come from building organizations capable of deciding what those analyses warrant, what remains unknown, and what level of commitment the available understanding can responsibly support.
Management does not own the truth. It organizes the passage from contested interpretation to organizational commitment, and keeps that commitment open to correction.
Before the organization acts, it must decide what it understands well enough to put its name, resources, and future behind.
Related writing
This argument develops a different part of a wider discussion I explored in Cheap Execution, Expensive Judgment, The Quiet Deskilling of Leadership, When Confidence Outruns Competence, When Algorithms Sit at the Table and Roots, Trunks, and the Seduction of the Leaf. Those pieces focus on judgment, capability, and the development of expertise. The present article focuses on the organizational passage from competing claims to action.
Dr. Nicos Rossides is a CEO, educator, and author focused on management, innovation, and the future of work. He is Co-Founder and Chairman of the Advisory Board of listening247 and Professor of Management at Minjiang University’s International Digital Economy College. His books include Engaging the Workforce (Routledge, 2022), Employee Engagement in Startups (Routledge, 2025), and AI Powered Insight (Routledge, 2026), co-authored with Michalis A. Michael.
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