Generative AI Techniques for Customer Experience (CX) Champions 

This course delves into the exciting world of Generative AI, a branch of Artificial Intelligence (AI) focused on creating new content, from text and images to music and code. You'll explore the potential of Generative AI to transform customer experiences (CX) within your organization, empowering you to become true CX Champions. 

• Explain the core concepts of Generative AI and its applications in the CX domain.

• Identify different Generative AI techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

• Explore how Generative AI can be used to create personalized and engaging content for various customer touchpoints.

• Evaluate the potential of Generative AI for tasks like product recommendation, marketing content creation, and chatbot development.

• Discuss the ethical considerations and potential biases surrounding Generative AI implementation in CX.

1. Unveiling the Power of Generative AI:

• Introduction to Generative AI: Understanding the core concept and its ability to create new data formats.

• Demystifying Generative Techniques: Exploring Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in simple terms.

• The Art of Generative AI: Examining applications for creative content generation, like product images or personalized marketing materials.

• Case Study: Utilizing Generative AI to personalize product recommendations based on customer preferences and past behavior.

• Hands-on Session: Experimenting with a user-friendly platform to explore basic text generation using pre-trained Generative AI models.

2. Reimagining CX with Generative AI Applications:

• Personalization at Scale: Unveiling the potential of Generative AI to personalize customer experiences across various channels (e.g., email marketing, chatbots).

• Engaging Content Creation: Exploring how Generative AI can create personalized marketing copy, product descriptions, or social media posts.

• Chatbot Enhancement with Generative AI: Utilizing Generative AI to improve chatbot responses, making them more creative, engaging, and informative.

• Guest Speaker Session: Inviting a CX professional who has implemented Generative AI in their work to share their experience and best practices.

• Group Discussion: Brainstorming potential applications of Generative AI for specific CX challenges within your department.

3. From Creation to Implementation: Responsible Generative AI in CX:

• Data Considerations for Generative AI: Understanding the importance of high-quality data for effective Generative AI models in CX applications.

• Mitigating Biases in Generative AI: Addressing potential biases in training data and algorithms to ensure fair and inclusive customer experiences.

• Explainability and Transparency: Exploring techniques to explain how Generative AI models arrive at their outputs, fostering trust in CX applications.

• Interactive Workshop: Experimenting with techniques to identify and mitigate potential biases in a sample dataset for a chosen Generative AI application.

• Project Planning: Developing a project plan outlining the responsible implementation of Generative AI for a specific CX initiative within your team.

4. The Future of Generative AI and the Evolving CX Landscape:

• Emerging Trends in Generative AI: Exploring advancements in Generative AI technology and its potential future applications in CX.

• The Role of the Human Element: Understanding the importance of human oversight and collaboration with Generative AI in CX initiatives.

• Building a Responsible AI Framework for CX: Developing strategies to ensure ethical and responsible implementation of Generative AI within your organization.

• Course Wrap-up & Project Presentations: Teams present their project plans, outlining the chosen Generative AI application for CX, responsible implementation strategies, and potential impact.

• Resource Sharing: Discussing best practices and ongoing learning opportunities for staying up-to-date with Generative AI advancements in the CX field.

• Strong understanding of deep learning concepts and architectures (e.g., CNN, RNN, Transformers)

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with probability theory and statistical concepts