Psychological-Based Organizational Growth System Using AI Kinetic Transformation
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Solution Overview
Problem
Transformation Initiatives in businesses face a high failure rate due to challenges such as losing employee engagement, communication issues, unpredictability, fatigue, and inefficient change management processes, particularly in large-scale change programs.
Innovation Solution
A psychological-based approach utilizing artificial intelligence and a Kinetic Transformation Algorithm to assess, build, execute, and scale change programs, focusing on alignment, automation, and data-driven decision-making to predict and address problems in real-time, with customizable workplans and governance structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional transformation initiatives are implemented in large-scale change programs, then organizational change can be initiated, but the failure rate exceeds 70% due to employee disengagement, communication issues, and fatigue
Solution Approach 1:
The patent segments large-scale transformation initiatives into smaller, manageable modules or phases. This breakdown allows for more controlled implementation, easier monitoring of progress, and reduced employee fatigue by presenting change in digestible increments rather than overwhelming all-at-once initiatives.
Solution Approach 2:
The patent implements continuous feedback mechanisms throughout the transformation process, including regular surveys, performance metrics tracking, and communication channels that allow employees to voice concerns and provide input. This feedback loop helps identify issues early, adjust strategies accordingly, and maintain employee engagement throughout the change process.
2Reliability
If comprehensive change management processes are implemented to address all potential issues, then transformation success may improve, but the time and resources required increase significantly
Solution Approach 1:
The patent emphasizes preliminary actions taken before the main transformation initiative, including early stakeholder engagement, pre-planning of communication strategies, and preparation of change management frameworks. By addressing key elements in advance, the actual transformation execution can proceed more efficiently with fewer delays and interruptions.
Solution Approach 2:
The patent utilizes parameter changes to optimize the transformation process, adjusting variables such as implementation speed, resource allocation, and scope based on real-time conditions and feedback. This allows the organization to maintain momentum while adapting to emerging challenges without requiring complete process redesigns.
3Adaptability or versatility
If diverse subject matter expertise is brought in to handle complex transformation needs, then program quality improves, but coordination and communication challenges increase
Solution Approach 1:
The patent develops a universal transformation framework that can accommodate diverse subject matter expertise while maintaining consistent communication protocols and processes. This framework serves as a common language and structure that enables different expert teams to collaborate effectively without losing information or creating silos.
Solution Approach 2:
The patent introduces intermediary roles or mechanisms that facilitate communication and coordination between diverse subject matter expert teams. These intermediaries act as translators and connectors, ensuring that information flows effectively across different specialized groups and that their contributions are integrated coherently into the overall transformation initiative.
Data Source
AI summary
A system and method for a psychological-based approach for organizational growth of a business is described herein. The method is executed by an engine, an application, a software program, a service, or a software platform on a computing device. The engine includes an artificial intelligence (AI) component. The method includes: capturing data associated with a project continuously and in real-time, combining the captured data with other data to create a data set, utilizing the data set to better predict and drive a resolution of one or more problems associated with the project, outputting a plurality of frameworks to address the one or more problems, receiving a selection from a user of a framework from the plurality of frameworks to address the one or more problems, executing a base build using the selected framework to address the one or more problems.