Enterprise AI Workflows
Governing AI action inside organizations.
Enterprise AI workflows may automate approvals, procurement, HR processes, legal reviews, customer communications, supply chain operations, compliance tasks, technical operations, and management decisions. These workflows can make institutional action look routine even when consequences are serious.
The risk is not only that AI may make a mistake. The deeper risk is that automation can hide who authorized the action and who remains responsible.
Example Scenario
An enterprise AI workflow reviews supplier performance and recommends automatically terminating a supplier contract due to missed delivery metrics. The recommendation may be based on real data, but the action can create legal, financial, operational, and reputational consequences.
Without LERA
Without LERA, performance data may become recommendation, and recommendation may become contract action. The organization may treat the result as a system outcome even though it is also an institutional decision.
This creates a responsibility gap: the action appears automated, while the consequences remain human, legal, and organizational.
With LERA
With LERA, contract termination is treated as a high-consequence proposed action. The Judgment-Governance Layer determines whether human authority, legal review, responsibility anchoring, rule validity, or escalation is required.
The AI may recommend action, but the enterprise does not actually act until the execution path has been governed.
How LERA Applies
- Judgment: determine what the proposed action means, what consequences may follow, and whether uncertainty is acceptable.
- Authority: identify who has legitimate authority to allow the action to continue.
- Responsibility: anchor responsibility before execution, not only after an incident.
- Reliability Rules: apply the relevant WRS-C and domain-specific WRS-D conditions for the context.
- RCC: govern who may change execution-related rules and how those changes are authorized and recorded.
- ECS: keep the action controllable immediately before the Execution Boundary.
- Outcome: route the action to Allow, Block, or Escalate before real-world execution.
What LERA Changes
LERA prevents enterprise automation from hiding institutional responsibility. It makes the difference between analysis, recommendation, proposed action, and execution visible before consequences occur.