Best Practices to Close the Gap Between Generative AI’s Promise and Reality
Learn how to maximize the effectiveness, accuracy, and value of your generative AI investments while creating new pathways for innovation.
The promise of generative AI is vast. In fact, McKinsey estimates that “applying generative AI to customer care functions could increase productivity by between 30 and 45 percent.”
However, to translate this potential into tangible business value, it is important to apply best practices to avoid common pitfalls. A “generative AI can do it all” narrative fails to address the complexities of real-world CX challenges.
To harness the full potential of generative AI, a comprehensive approach is essential—one
that includes selecting the best-fit language learning models (LLMs), using guided prompt building, and layering different types of AI purpose-built for CX within a unified platform for optimal results.
In this Best Practices Guide, you’ll learn how to:
- Leverage a unified platform to manage and optimize all modern interactions
- Apply generative AI where it will produce the best outcomes
- Select an AI language model that optimizes your desired use case
- Implement industry-specific models to deliver more accurate and relevant customer insights
Bridge the gap between gen AI’s potential and measurable success. Download the guide to learn more.
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