Exploring the complexity of generative AI, including large language models and guardrails. Customized models for conversational AI using company data. Secrets of personalized customer interactions with generative AI models.
Generative AI creates new content, transforming AI capabilities beyond data processing.
Companies must carefully implement generative AI in customer interactions to manage risks effectively.
Deep dives
Understanding Generative AI and Its Significance
Generative AI introduces a new dimension to artificial intelligence by creating new content rather than just processing data and making predictions. This capability to generate new content is particularly impactful in various professional and consumer settings, ranging from text to image creation for enhanced personalization. Large language models like chat GPT exemplify this, where the model strings together words based on probabilities to produce outputs, emphasizing the probabilistic nature of generative AI.
Leveraging Internal Instances of Chat GPT for Data Security
Organizations are exploring the use of internal instances of chat GPT to maintain control over sensitive data and ensure data privacy. By training these instances on corporate data, companies can utilize the functionalities of chat GPT while safeguarding proprietary information. Additionally, companies can customize models like GPT 3.5 or GPT4 for specific use cases, emphasizing the importance of data security and ownership.
Challenges and Considerations in Customer-Facing Applications of Generative AI
Implementing generative AI in customer-facing applications poses significant challenges due to potential risks such as providing inaccurate or harmful information. Companies, especially in regulated industries like banking, approach such deployments cautiously to mitigate adverse outcomes. Ensuring data accuracy, personalized responses, and proactive management of unintended consequences are crucial considerations when extending generative AI to customer interactions, underscoring the need for a nuanced approach to balancing innovation with risk mitigation.
What do CX pros need to know about the promises and perils of generative AI? Forrester VP and Principal Analyst Martha Bennett explains the statistical complexity in layman’s terms and offers recent CX examples to illustrate the new paradigm.
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