Generative AI Negotiation Training Framework
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Solution Overview
Problem
Existing sales training systems are ineffective as they fail to consider the uniqueness of each customer and do not provide adequate feedback for improvement.
Innovation Solution
A system utilizing generative artificial intelligence (AI) that trains users for conversational encounters with customers by receiving encounter-defining parameters, formatting them into a large language model input, and iteratively conversing with the user to provide an evaluation of their performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static sales training is used, then training delivery is simple, but training effectiveness is poor due to lack of customer uniqueness consideration and feedback
Solution Approach 1:
The system implements a feedback mechanism where the AI agent evaluates the user's conversational responses and provides performance feedback. The system analyzes user inputs against ideal response patterns and delivers constructive feedback to improve sales techniques, directly addressing the lack of feedback in traditional training methods.
Solution Approach 2:
The training system transitions from static pre-recorded content to dynamic AI-driven interactions. The AI agent adapts its responses based on user inputs, creating a dynamic conversational environment that simulates real customer interactions and adjusts to user performance in real-time.
2Adaptability or versatility
If AI-based dynamic training is implemented, then training effectiveness improves through personalized feedback, but system complexity increases
Solution Approach 1:
The system uses an AI language model as an intermediary to bridge the gap between simple text input and complex training evaluation. The AI agent processes user responses, compares them against training objectives, and generates personalized feedback without requiring complex manual evaluation systems.
Solution Approach 2:
The system creates virtual copies of customer interactions through AI simulation. Instead of requiring multiple real customer scenarios, the AI generates synthetic customer responses and evaluation criteria, allowing personalized training without proportionally increasing system complexity.
3Measurement precision
If iterative conversational training is provided, then user skill improvement is enhanced, but training time and iterations required increase
Solution Approach 1:
The training system maintains continuous evaluative action throughout the conversation. Rather than providing feedback only at the end, the AI agent continuously analyzes user responses and provides ongoing guidance, making the training process more efficient by eliminating gaps between actions and evaluations.
Data Source
AI summary
A system for training a user for a conversational encounter with a customer receives encounter-defining parameters via a user interface. The encounter-defining parameters are descriptive of attributes of the conversational encounter. The system formats the encounter-defining parameter for transmission to a large language model (LLM). The LLM receives the instructions and outputs a conversational opening viewable by a user via a user interface. The user then responds to the conversational opening and iteratively converses with the LLM. After a defined maximum number of iterations have been reached, the LLM provides an evaluation to the user via the user interface. The evaluation is indicative of the evaluated outcome of the conversational encounter, including at least one of a score, a summary of the encounter, a likelihood that a deal is reached, and suggested improvements.