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AI may help leaders spot signals about whether an organization can execute a major decision, but it cannot certify readiness. In a September 25, 2026 interview with The AI Journal, Regan Inkster argues that leaders should look beyond performance metrics to organizational coherence, capacity for change, uncertainty, and governance—and that executive accountability remains with the person who signs.
What organizational readiness means in Inkster’s view
Inkster describes a recurring gap between strategic intent and execution. For him, coherence concerns whether the people setting direction and the people doing the work share an understanding of what is happening, why it matters, and what the organization is trying to achieve. If those groups are working from different interpretations, a strategy may be sound on paper yet difficult to carry out.
Readiness, in this framing, is not simply a strong quarter or a favorable forecast. It also concerns whether the organization can coordinate around a decision, absorb the change, and sustain execution under current conditions. The idea is relevant to acquisitions, restructuring, AI transformation, and technology migration, where the decision itself may be only the start of a demanding implementation.
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Why conventional performance metrics may not be enough
Revenue, margin, utilization, pipeline, and productivity describe outputs. Inkster’s argument is that they do not, by themselves, describe the condition of the system producing those outputs. A company can therefore have acceptable headline results while leaders still need to ask whether teams share an understanding of priorities and have the capacity to deliver another major change.
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That distinction is useful, but it does not make conventional measures irrelevant. The interview presents coherence as another lens for leaders to consider, not a substitute for financial, operational, or strategic analysis.
What AI and Organizational Φ can—and cannot—tell leaders
A signal, not a verdict
Inkster says Organizational Φ estimates organizational coherence. He cautions that no score can reduce an organization to a single number. In practice, a coherence signal should prompt questions about how the organization is functioning; it should not be treated as proof that a particular decision will succeed or fail.
Decision intelligence and architecture
Inkster describes Phive Dynamics’ work as extending coherence measurement into decision intelligence and decision architecture. As presented in the interview, this involves building structured histories of decisions, examining how similar decisions have landed, estimating uncertainty, and identifying constraints before commitment. The intended use is to help leaders consider whether an action is executable under current conditions and whether guardrails may be useful.
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AI agents need organizational context, too
Inkster argues that authorization alone may not be enough when an AI agent can take action. A system may have permission to act without accounting for the organization’s current state or its ability to manage the consequences. He describes decision architecture that could help determine whether an action should proceed, be staged, delayed, or reconsidered. Dynamic permissions and machine-readable guardrails are presented as developing, rather than established capabilities.
What Inkster’s reported figures do—and do not—establish
In the interview, Inkster says he analyzed public organizational and financial information from more than 100 publicly traded companies across more than 40 quarters. He reports that sharp declines in coherence appeared in financial and operating performance roughly two quarters later, and that recovery ran about two to one against decline.
Those figures are Inkster’s account in the interview, not independently validated general findings. The interview does not name an external study, provide an independently assessed dataset, or report an independent replication. They should therefore be read as a reported observation, not as a universal forecast or a demonstrated causal rule. The “two-quarter” timing is not a guarantee that a coherence decline will predict a downturn on that schedule.
Questions to ask before committing to a major change
Inkster’s ideas can be translated into a set of discussion prompts. They are questions for leadership judgment, not a validated scoring model.
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- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
- Shared understanding: Do leaders and the people doing the work agree on what is happening, what it means, and the objectives?
- Capacity to absorb change: Can the organization coordinate around this initiative and carry it through without undue strain?
- Uncertainty and constraints: What remains uncertain, and which constraints or guardrails should affect commitment or execution?
- Governance and accountability: Does oversight account for organizational conditions as well as system permissions, and who is accountable for the decision?
These questions do not produce a readiness certificate. They help make assumptions and execution risks visible before leaders commit resources or authorize action.
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Inkster’s stated limit is uncertainty: organizations are networks of people, and no model removes uncertainty from a high-stakes decision. He presents AI as a possible early-detection aid that may give leaders more time to make a considered choice, not as a replacement for judgment. As he puts it: “Accountability stays with the executive who signs.”
The source for Inkster’s views and reported figures is Tom Allen’s interview, “Can AI Tell Whether a Company Is Ready for Its Next Big Decision? Regan Inkster on Organizational Readiness,” published by The AI Journal on September 25, 2026: read the interview.
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