AI Copilot Alarm Detection Across Non-Hierarchical Business Units
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Solution Overview
Problem
Traditional hierarchical organizational structures hinder the effective implementation of artificial intelligence due to siloed data and sluggish decision-making, leading to high employee turnover, low productivity, and cultural clashes, while existing machine learning systems are anchored to these rigid hierarchies, resulting in sub-optimal performance.
Innovation Solution
The implementation of a non-hierarchical organizational structure using the Deep Value CSI Assisted Management and Operations Copilot (AMO-Copilot) platform, which utilizes AI to measure customer satisfaction and innovation metrics, enabling near-real-time performance monitoring and empowering workers through a culture of trust and ownership, and incorporates features like Master MBU Status Charts, Critical Impediments Charts, and Innovation Charts to facilitate early problem identification and predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If traditional hierarchical organizational structures are used, then established processes and clear chains of command are maintained, but data becomes siloed and decision-making becomes sluggish
Solution Approach 1:
The patent segments the organization into autonomous Micro Business Units (MBUs) that operate independently rather than in rigid hierarchical silos. Each MBU is a self-contained entity with its own P&L responsibility, enabling data to flow freely between units while maintaining organizational stability through standardized interfaces and shared governance protocols.
Solution Approach 2:
The patent introduces an AI intermediary layer that sits between MBUs and organizational leadership, translating and synthesizing data from autonomous units. This AI mediator enables rapid decision-making by processing information from multiple MBUs simultaneously, eliminating the sluggishness of traditional hierarchical approval chains while preserving structural stability.
2Stability of the object's composition
If traditional hierarchical organizational structures are used, then clear chains of command are maintained, but employee turnover increases due to disengagement
Solution Approach 1:
The patent empowers MBUs to be self-service entities with autonomous decision-making authority over their operations, resources, and strategic direction. This self-determination fosters employee engagement and ownership, reducing turnover while maintaining organizational stability through standardized governance frameworks that ensure alignment with overall company goals.
Solution Approach 2:
The patent implements continuous feedback loops where MBUs receive real-time performance data and customer satisfaction metrics, enabling them to self-correct and improve. This feedback mechanism increases employee engagement and retention by making workers feel empowered and heard, while maintaining structural stability through standardized performance measurement and reporting protocols.
3Ease of manufacture
If existing machine learning systems are anchored to rigid hierarchies, then implementation is straightforward, but performance becomes sub-optimal
Solution Approach 1:
The patent creates a dynamic organizational structure where MBUs can adapt and reconfigure based on market conditions and performance data, rather than being locked into rigid hierarchical positions. This dynamism enables AI systems to access diverse, real-time data from autonomous units, improving performance while maintaining implementation ease through standardized MBU templates and governance protocols.
Solution Approach 2:
The patent changes the fundamental parameters of organizational structure from fixed hierarchical levels to flexible autonomous MBUs with variable boundaries and responsibilities. This parameter change enables AI systems to process diverse data streams from independent units, improving performance while maintaining implementation ease through standardized interfaces and governance frameworks that simplify integration.
Data Source
AI summary
System and method to flatten a hierarchical organizational structure by applying Magic Grid, specifically by generating a trained machine learning model using artificial intelligence using training data comprising a history of an organization's deep value customer satisfaction ratings and innovation (CSI) and associated operating status data of said organization, outputting indications of whether an alarm should be triggered, wherein said training model weights one or more nodes of an artificial neural network; providing said model with current operating status data, outputting a value indicating whether an alarm should be triggered, triggering said alarm based upon said value, receiving user input via a software interface, and further training said model based upon said user input.