A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI

TEXT START: Enterprise artificial intelligence is increasingly embedded in decisions that must remain lawful, explainable, adaptable, and accountable despite personnel turnover, model replacement, regulatory change, and shifting organizational incentives.

The Dissection

The paper constructs a governance and continuity layer for enterprise AI. It converts institutional memory, oversight, adaptability, feedback, and legal fidelity into measurable variables, then tests routing and risk logic through synthetic simulation.

The simulation demonstrates internal coherence, not institutional survival. It measures whether a decision system can preserve procedural integrity—not whether humans retain economic power, authority, or productive necessity.

The Core Fallacy

The framework conflates decision integrity with institutional viability. Under Discontinuity Thesis mechanics, lawful and explainable decisions do not preserve the mass employment–wage–consumption circuit, human bargaining power, or control over AI capital.

“Human oversight” is treated as a durable control variable. Under competitive pressure, it can become ceremonial review, liability assignment, or an automated checkpoint wearing a human badge. The paper governs the decisions produced by AI but does not model who owns the models, infrastructure, energy, logistics, or authority to override them.

Hidden Assumptions

• Governance remains enforceable despite shifting incentives and concentrated AI ownership.
• Human oversight remains substantive rather than nominal.
• Explainability meaningfully correlates with correctness and accountability.
• Regulatory knowledge graphs can resolve jurisdictional conflict, ambiguity, and political contestation.
• Review intervals can keep pace with model replacement and regulatory change.
• Organizations retain the resources and authority to abstain, escalate, or reject automated recommendations.
• Synthetic parameters and Monte Carlo behavior transfer meaningfully to production environments.
• “Beneficial” outcomes can be defined independently of institutional power and class interests.
• Preserving organizational memory is equivalent to preserving sound judgment, rather than preserving inherited procedures after their social function has changed.

Social Function

Primary classification: transition management, with a partial-truth core and a layer of elite self-exoneration.

The framework may genuinely improve auditability, provenance, regulatory routing, and controlled abstention during the lag phase. Its limitation is more consequential: it allows institutions to present themselves as responsible stewards of AI while leaving ownership, displacement, authority concentration, and the disappearance of human economic necessity outside the model.

It is a cockpit checklist for institutional conscience, not a theory of who owns the aircraft or who gets expelled from it.

The Verdict

A technically useful governance scaffold, but strategically incomplete under the Discontinuity Thesis. It can make automated institutions more traceable and legally defensible. It cannot defeat P1, P2, or P3.

The likely legacy is a stronger audit layer wrapped around a shrinking human role: the paperwork survives after the human economic function has been automated.