AI Digital Twins for Enterprise Knowledge Retention
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Solution Overview
Problem
Human-to-human interactions within organizations are hindered by unavailability, limited memory, and potential unprofessional or inaccurate responses, leading to inefficiencies and loss of expertise when the right expert is not accessible.
Innovation Solution
Implementing AI-based digital twins that replicate employee knowledge and behavior using large language models and machine learning, allowing for continuous access to expertise and ensuring compliance with company policies, even after employees leave the organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human employees are used to provide expertise and answers, then the quality of information can be high, but availability is limited and expertise is lost when employees leave
Solution Approach 1:
The patent creates digital twins that are copies of employees' knowledge, skills, and work styles. These digital twins replicate human expertise through AI models trained on employee data, allowing organizations to access employee knowledge continuously without relying on the actual employees being present or retained.
Solution Approach 2:
The system performs preliminary action by capturing and training digital twins before employees leave the organization. The digital twins are trained on historical data, communications, and knowledge transfers, ensuring that expertise is preserved and accessible even after employees depart.
2Productivity
If employees are asked frequently, then more information can be obtained, but employee availability is reduced and burnout increases
Solution Approach 1:
The digital twins provide self-service by automatically answering queries, generating responses, and providing information without requiring employee intervention. The system handles routine queries independently, freeing employees from repetitive tasks and allowing them to focus on higher-value work.
Solution Approach 2:
The digital twins serve as intermediaries between information requests and human employees. Instead of employees directly responding to all queries, the digital twins act as mediators that handle routine information requests, filtering out tasks that require human involvement.
3Loss of information
If employees share all their knowledge, then more information is available, but privacy and security risks increase
Solution Approach 1:
The digital twins function as secure intermediaries that access and share information through controlled AI models. The system maintains privacy and security by processing information through encrypted channels and controlled data access, preventing direct exposure of sensitive employee data while still enabling knowledge sharing.
Solution Approach 2:
The patent replaces direct human-to-human information sharing with AI-based digital twin systems. This substitution maintains information accessibility while introducing layered security measures, including AI-driven access controls and data encryption, that prevent privacy breaches.
4Measurement precision
If employees respond to queries, then accurate information is provided, but responses may be inconsistent with company objectives
Solution Approach 1:
The digital twins incorporate feedback mechanisms that continuously align responses with current company objectives, policies, and culture. The AI models are trained on organizational data and receive feedback loops that ensure information accuracy and strategic alignment, adjusting responses based on evolving company goals.
Solution Approach 2:
The system dynamically adjusts the parameters and training data of digital twins to reflect changing company objectives and priorities. This ensures that information provided by digital twins remains accurate and aligned with current organizational strategies, adapting to evolving business needs.
Data Source
AI summary
An enterprise knowledge retention and access system is disclosed. In various embodiments, data comprising a plurality of content items associated specifically with a user is stored. Generative artificial intelligence techniques are used to generate, based at least in part on the plurality of content items associated specifically with the user, a generated content reflecting information derived from the plurality of content items with respect to a specific subject.