Sophisticated AI applications are at the heart of modern business go-to-market strategies. When deployed into customer-facing applications, AI supports better sales performance, more effective marketing campaigns, improved customer experience and retention, and much more.
Generative AI is increasingly the differentiator in the customer journey. Driving conversational AI through cloud-based large language models, generative AI is the engine behind a new generation of self-service applications that leverage natural language processing. As a core feature of chatbots, copilots, and embedded digital assistants, generative AI can drive 1:1 personalization, contextual recommendations, and real-time guidance both for the customer directly and for sales, service, marketing, and other stakeholders.
High-quality unified customer data is a key ingredient for unlocking the business value of generative AI. In this webinar, TDWI senior research director James Kobielus will discuss how enterprises can enrich their generative AI applications by leveraging data and analytics as a service in the cloud. He will be joined by Sneh Kakileti, ZoomInfo VP of product management, and Tom Cannon, Google’s head of data ecosystem for cloud partner engineering, for an in-depth discussion of generative AI opportunities and challenges. They will discuss the following topics:
How are today’s sales, marketing, and customer relationship management professionals using generative AI to boost their productivity?
How important is high-quality unified enterprise data for building high-performance generative AI?
What are the best practices for establishing a reliable data foundation for generative AI?
What are the principal challenges for enterprises in building and deploying high-performance generative AI applications?
How can enterprises leverage a cloud-based data exchange to acquire the third-party data assets needed to enrich their generative AI applications?
What are the best practices and emerging approaches for making generative AI applications accessible at scale, easier to use, and more trustworthy?