The nine EQOS dimensions of the AI market, read as AI bubble signals

What are the nine EQOS dimensions of the AI market, and how are they grouped?

The study groups the nine dimensions in 3 domains. Domain A, internal alignment, holds coherence, integrity, and resonance. Domain B, relation and living, holds care, trust, and communication. Domain C, action and matter, holds clarity, stability, and energy. Each dimension gets a value between 0 and 1, computed from public data on the enterprise AI market in April 2026. The three domains give the nine values a structure that a board can read in a few minutes.

The grouping gives a reader three questions to ask of any AI plan. Does the organization agree with itself? Do people and systems relate well? Does the work get done with clear aims and enough resources? A weak value in one domain points to the question that deserves attention first.

How do coherence, integrity, and resonance shape the AI bubble?

Coherence scores 0.425, integrity 0.458, and resonance 0.325. Coherence measures whether the stated AI strategy matches what happens on the ground. The study links it to the 31% of failed projects that had no alignment with strategy. Teams that announce an AI-first plan while their pilots never reach production show the gap.

Resonance, at 0.325, is the weakest of the three, and integrity, at 0.458, is the strongest. The study ties neither of them to a named failure cause, so read them as context for the coherence value. Coherence is where the practical lever sits, and the page on strategic coherence at 0.425 explains how projects drift away from their stated plan.

Why is communication the weakest of the nine AI market dimensions?

Communication scores 0.305, which is 0.005 above the critical threshold of 0.3. The study ties it to insufficient or biased data, the most frequent cause of failure at 42%. Information that moves poorly between data, models, and decision makers produces outputs that nobody can trace back to their inputs. The explainability of deployed models, at 0.25, sits inside this dimension.

Because the margin to the threshold is so thin, one incident could move this dimension into the critical zone and pull trust down with it. The page on data and communication at 0.305 covers the data audit that the study recommends before any deployment.

What do care and trust reveal about the AI bubble in the nine dimensions?

Care scores 0.338. The study observes that companies use AI mainly to cut human costs, and that fewer than 15% have a retraining plan for affected employees. Ethical issues and bias explain 18% of failures. The page on care and ethics at 0.338 details the retraining budget of 2% to 5% of AI investment.

Trust scores 0.450 and ties to team resistance, which explains 24% of failures. People trust systems they understand, so trust rises with explainability. The page on AI trust at 0.450 shows how team resistance appears and which steps reduce it before a project launches. The study lists a correction for each, with a before and after value.

How do clarity, stability, and energy balance the AI market score?

Energy leads the nine dimensions at 0.588, which reflects the large capital and effort that the industry puts in, about $200B a year. Stability scores 0.412. Clarity scores 0.487, the second highest value overall, yet the study links it to the 22% of projects whose return on investment was never demonstrated. Stability, at 0.412, is the lowest of the three.

A strong energy score shows effort and does not show results. The market pours resources in, while clear goals and measured returns lag behind. The page on clarity at 0.487 shows how to turn effort into a demonstrated return, which is the step that separates a pilot from a product.

Which failure causes link to which of the nine AI market dimensions?

The MIT 2025 finding that 95% of generative AI projects fail in production comes with five causes, and the study maps each one to a dimension. Insufficient or biased data weighs 42%, lack of alignment with strategy 31%, team resistance 24%, unproven return 22%, and ethical issues 18%. The figures overlap because a project can fail for several reasons.

The mapping turns a failure rate into a diagnosis. A team that sees data problems looks at communication, and a team that meets resistance looks at trust. Because each cause points to a dimension, each dimension points to a specific correction, and the study lists those corrections in its recommendations.

How can a team use the nine AI market dimensions in practice?

Score your own organization on the same nine dimensions, then compare it with the market values above. The Λ simulator estimates your φ(S) in about 10 minutes. A dimension far below the market value deserves the first correction, because in a weighted product the lowest value limits the whole result. Write the values down with their date, so the next quarter has a baseline.

For a full structural review, the method page explains how the audit runs, and the contact page opens a 30-minute call with the team. Each route returns nine values you can track every quarter, so your board sees progress dimension by dimension instead of one opaque number. Give each domain an owner, so that every weak value has a person who works on it.

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About the author

Stéphane Jambu

President, EQOS Forecast Asia

Stéphane Jambu leads EQOS Forecast Asia and works as a consultant in e-commerce, search visibility, and AI. LinkedIn profile