The AI industry spends $200 billion per year on R&D and deployment. But measurable results are disappointing. Here is the data nobody puts forward in keynotes.
In 2025 MIT studied generative-AI projects deployed in companies. The finding is brutal: 95% fail to reach production. The reasons are not technical — they are structural.
| Cause d'failure | Frequency | Dimension EQΦS |
|---|---|---|
| Insufficient or biased data | 42% | Communication = 0.305 |
| No alignment with the strategy | 31% | Coherence = 0.425 |
| Team resistance | 24% | Confiance = 0.450 |
| ROI not demonstrated | 22% | Clarity = 0.487 |
| Ethical issues / bias | 18% | Soin = 0.338 |
Predicted failure rate ≈ 94%
LLMs (GPT, Claude, Gemini) produce factually false answers between 0.7% and 30% of the time depending on the model and domain. In a medical context, the rate rises to 67% (JMIR 2025). Et 47% of enterprise users took at least one major decision based on hallucinated content.
P(hallucination): LLM > 0 (always) — φ = 0 (deterministic)
Sources : Vectara Hallucination Leaderboard 2025, JMIR 2025, Enterprise AI Survey 2024, AI Safety Research 2025.
According to Bain & Company, $2.3 trillion of digital-transformation projects fail every year in the world. Generative AI is now its Main component. Companies invest massively in tools they don't know how to integrate structurally.
This is not a technology problem. It's a problem of coherence organizationnelle — exactly what EQΦS measures.
None dimension en critical zone (< 0.3), Mays all except are in fragile zone. The system is functional but structurally fragile — a shock (regulation, incident, recession) can push several dimensions below threshold.
Explainability of AI models deployed in companies is at 0.25 (out of 1). Concretely: when a model recommends firing 200 people or investing $50M in a new market, no one can explain why. Transparency of automated decisions is at 0.30. Companies deploy systems they don't understand — and make million-dollar decisions based on black boxes.
Communication = 0.305 ≈ critical threshold 0.3 — below 0.3: STOP zone
Companies use AI mainly to cut human costs — not to create value. McKinsey estimates 30% of hours worked could be automated by 2030. But fewer than 15% of companies have a retraining plan for impacted employees. The Codex measures this intent with Care(S):
Amour(S) = 0.30
Each dimension is raised to its weighting — it's a geometric product, not a simple average. The lowest score drags the whole system down.
EQΦS aggregates the 9 axiss via a weighted geometric product — not an average. The expanded form and coefficients of the Codex Universalis remain proprietary.
Codex constants are protected. Only φ = 1.618… (golden ratio) is public.
Λ = 0.4558
φ(S) = 0.337
ethical safeguard active.067 → Passage authorized
The market φ(S) is at 0.337. But your organization may be above or below. Here are 4 actions to raise your specific φ(S) above 0.5 — the viability threshold for a successful AI deployment.
Action : Audit your internal data signal/noise ratio. If X(S) < 0.5, AI will amplify noise instead of signal. Concretely: check training-data quality, potential biases, edge-case coverage.
Impact : from 0.305 → 0.50. You go from "we don't understand what the model does" to "we know exactly when and why it's wrong".
Δφ ≈ +0.017 par point
Action : Do not deploy a model that cannot explain its decisions. Use SHAP, LIME, or interpretable models (decision trees, regression) for critical decisions. Reserve LLMs for assistance, not decision.
Impact : Trust from 0.450 → 0.60 (teams trust when they understand). Clarity from 0.487 → 0.55.
Action : Measure alignment between your stated AI strategy and what actually happens on the ground. If the gap is too large, there is a dangerous mismatch.
Impact : Coherence from 0.425 → 0.55. You stop saying "AI-first" in meetings while deploying POCs that never reach production.
Action : For each automated role, define a skills plan. Budget: 2-5% of total AI investment. AI must increase humans, not replace them.
Impact : Care from 0.338 → 0.50. Care(S) rises from 0.30 to 0.40. Ethical safeguard increases significantly.
φ(S) = 0.401
You just read the proof.
Computed on your data, your case, your framing. Not an opinion — a reproducible mathematical verdict.