Common business use cases for generative AI

Summary

In a panel discussion, industry experts from Google, Vodafone, and Blue Core shared insights on the application of generative AI in business. They highlighted the importance of prioritizing customer value and technical feasibility in AI projects. Notable use cases included Alpha Fold's impact on drug discovery, product cataloging, and customer service operations. The panel emphasized the transformative potential of AI, the need for continuous learning, and the value of hands-on experience in understanding its practical applications and limitations.

Takeaways

• 🌟 Prioritization of AI use cases focuses on customer value, friction points, technical feasibility, and aligning with internal research innovations.
• 🔬 AlphaFold on Google Cloud is a prime example of AI research translated into a practical solution, accelerating drug discovery by understanding protein structures.
• 🛍️ Product cataloging is a significant use case where AI helps in categorizing products for search and creating website copy, improving customer experience and retail performance.
• 💬 Customer service operations benefit from AI through conversational agents and internal support for SRE teams, enhancing post-mortem search and summarization.
• 📊 Generative AI is utilized to improve features on platforms, increase internal efficiencies, and better serve customers by standardizing product data and mapping it to taxonomies like Google's.
• 🌐 Telecommunication companies leverage AI for network deployment, predictive maintenance, and customer call analysis, translating and summarizing calls to understand customer issues and improve service.
• 🔄 Technical solutions in AI involve embedding models, vector databases, and vector search engines to process and retrieve unstructured data efficiently.
• 🚀 The application of Transformer models extends beyond text and images to non-traditional use cases, such as analyzing and predicting user events in software applications.
• 🌐 Global deployment of AI solutions requires consideration of scalability, replicability, data security, and compliance with local regulations like GDPR.
• 📈 Business value from AI is measured in terms of increased employee productivity, improved user experiences, and the generation of new insights or ideas that were previously impossible.
• 💡 The importance of continuous learning, hands-on experience, and staying up-to-date with the latest AI research and technologies is emphasized for maximizing the potential of generative AI.

Q & A

What is the main focus of the session on common business use cases for general AI?

The session focuses on discussing and understanding the top business use cases for general AI, how to prioritize them, and how to get started with their implementation in various industries.

Who are the panelists in the session and what are their roles?

The panelists include Nema Dakiniko, a product manager at Google for AI portfolio, Ignacio Garcia, the global director of data analytics and AI and CIO of Vodafone Italy, Arvind Christian, who runs engineering, data science, and solution architecture teams at Blue Core, and Donna, who leads the Technical Solutions management team for generative AI at Google Cloud.

How does Google prioritize its AI solutions for customers?

Google prioritizes AI solutions by focusing on what will add value to the customers, identifying friction points they face, and considering the technical implementation of the solutions. They also look internally to their research teams to bring innovations to customers.

Can you explain the significance of AlphaFold on Google Cloud?

AlphaFold is a significant research project by DeepMind that focuses on solving the protein folding problem. It's important because understanding a protein's structure can lead to the development of drugs that modulate its function. Google Cloud has operationalized this research into a solution that adds value to healthcare organizations by making it reproducible, scalable, and cost-effective.

What are some of the technical patterns observed in different AI use cases?

Technical patterns include using embedding models to process unstructured data and index it with a vector database, using coding models like Codex to generate SQL or Cypher for database access, and applying Transformer models to non-image, non-text use cases.

How does Vodafone Italy utilize AI in its operations?

Vodafone Italy uses AI for strong model analysis to understand customer behavior and offer models, network deployment for capex efficiency, predictive maintenance, and summarization of customer call data to understand and improve customer service and reduce detractors.

What are the key technical considerations when deploying AI solutions globally?

Key considerations include scalability and replicability, ensuring data security, adhering to local regulations like GDPR, avoiding bias in models, and creating policies to maintain these standards.

Why did Blue Core choose Google Cloud for its AI projects?

Blue Core chose Google Cloud due to its innovation roadmap, co-innovation approach, data governance and security features, and the performance they observed with Google's AI technologies compared to other models.

What are the business benefits of using generative AI?

Business benefits include increased employee productivity, more intuitive and better user experiences, new insights or ideas that were impossible before, and cost-effectiveness in problem-solving and scaling solutions.

What advice does the panel have for businesses looking to implement generative AI?

The advice includes experimenting and iterating with AI, investing in foundational architecture, creating a safe environment for experimentation, not restricting innovation, and educating the entire company about the potential of AI.

How does the panel suggest businesses should approach the rapid evolution of AI technologies?

Businesses should stay humble, focus on short-term use cases, invest in tooling and platforms to support AI, and ensure that their AI initiatives are not centralized to avoid stifling innovation.