Generation AI

From SQL to AI: Transforming Your Technical Team for the Future

Episode Summary

In this insightful episode of Generation AI, hosts Ardis Kadiu and Dr. JC Bonilla dive deep into the practical steps for organizations to become AI-ready. They discuss the importance of transforming data into knowledge bases using vector databases and the role of cloud infrastructure in this process. The conversation also touches on the skills and mindset required for technical teams to adapt to AI, emphasizing the significance of AI literacy over technical know-how. Ardis and JC provide valuable advice on selecting the right AI use cases for a proof of concept and the key considerations for choosing an AI infrastructure such as security, bias, and team readiness.

Episode Notes

In this insightful episode of Generation AI, hosts Ardis Kadiu and Dr. JC Bonilla dive deep into the practical steps for organizations to become AI-ready. They discuss the importance of transforming data into knowledge bases using vector databases and the role of cloud infrastructure in this process. The conversation also touches on the skills and mindset required for technical teams to adapt to AI, emphasizing the significance of AI literacy over technical know-how. Ardis and JC provide valuable advice on selecting the right AI use cases for a proof of concept and the key considerations for choosing an AI infrastructure such as security, bias, and team readiness.

Introduction

• Ardis and JC catch up and discuss VaynerMedia's recent award and upcoming Engage conference

AI Readiness: Transforming Data into Knowledge Bases

• Types of data: structured, semi-structured, and unstructured
• Vector databases and semantic databases for storing and retrieving AI-ready data
• Transforming data into embeddings using AI models

Choosing the Right AI Infrastructure

• Cloud infrastructure options: Google, AWS, Microsoft
• Open-source alternatives: WeV8 for vector databases
• Considerations for selecting an AI infrastructure

Building AI Assistants and Conversational Interfaces

• White-label chatbot frameworks and APIs
• Open-source libraries for building AI assistants: Langchain and LAMA index
• Productizing AI assistants and focusing on defining behavior rather than mechanics

Transforming Your Technical Team for AI

• Assessing core competencies and deciding between building in-house or outsourcing
• The importance of AI literacy over technical know-how
• Fostering a hunger for growth and evolution among technical team members

Launching Your First AI Proof of Concept

• Defining a narrow, focused use case that solves a gap or automates a repeatable process
• Example use cases for higher education: admissions rubrics, IT helpdesk knowledge bases, semantic search for course descriptions
• Key ingredients for a successful AI proof of concept

Conclusion

• Recap of the AI readiness playbook
• The importance of addressing data security and AI bias (to be discussed in a future episode)