AI Persona Model for Organizational Change Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional change/transformation adoption techniques struggle to accurately and efficiently understand and incorporate the diverse characteristics, idiosyncrasies, and dependencies of various groups within an organization, leading to incomplete and unsuccessful change adoption.
Innovation Solution
The development and implementation of AI and/or ML driven personas that simulate group response to organizational changes, utilizing group data to provide instantaneous, holistic, and accurate responses reflecting the consensus opinion of affected groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional feedback collection methods are used to understand group responses to organizational change, then personnel can provide input on proposed changes, but the process suffers from inaccurate data due to small sample size, misunderstanding, and misinterpretation
Solution Approach 1:
The patent creates virtual personas that are digital copies or representations of actual group members, trained on their historical data and communication patterns. These personas can simulate group responses without requiring actual group members to provide feedback, thereby eliminating sample size limitations and reducing misinterpretation while maintaining accuracy.
Solution Approach 2:
The patent introduces an AI intermediary layer that sits between the organizational change proposal and the group feedback process. This intermediary (the persona model) automatically processes and simulates group responses, eliminating the need for direct human feedback collection and its associated problems of misunderstanding and misinterpretation.
2Reliability
If conventional change adoption techniques are used involving individual feedback collection, then some level of consensus can be reached, but the process significantly delays change adoption
Solution Approach 1:
The patent pre-trains persona models on historical group data and communication patterns before actual change proposals are made. This preliminary preparation allows the personas to immediately simulate realistic group responses when changes are proposed, eliminating the time-consuming process of collecting and analyzing actual feedback while maintaining consensus quality.
Solution Approach 2:
By using virtual persona copies instead of actual group members for feedback simulation, the system eliminates the time delay inherent in human feedback collection while preserving the quality of consensus through accurate simulation of group dynamics and preferences.
3Adaptability or versatility
If conventional feedback processes are used for transformation adoption, then individuals can participate in change decisions, but the process suffers from misinterpretation and inaccurate data leading to ineffective change adoption
Solution Approach 1:
The patent creates detailed virtual copies of group members (personas) that are trained on their historical data, communication styles, and characteristic responses. These personas accurately capture and simulate group characteristics, dependencies, and preferences without the information loss that occurs during human feedback collection and interpretation.
Solution Approach 2:
The system implements automated feedback loops where persona models continuously learn from simulated change scenarios and refine their understanding of group characteristics. This automated feedback mechanism ensures accurate capture and retention of group information without the misinterpretation problems of human feedback processes.
Data Source
AI summary
Techniques for simulating transformation adoption are disclosed herein. An exemplary computer-implemented method may include receiving change data associated with an organizational change anticipated to affect members of a first group and inputting a portion of the change data into a persona model configured to generate responses representative of the first group. The persona model may be trained using a plurality of training change data and a plurality of group data as inputs to output a plurality of training responses. The exemplary method may further include generating, by executing the persona model, a response to the portion of the change data; and outputting the response for display to a user.