Your host, Cindi Howson, and CTO of NVIDIA AI agentic software, Bartley Richardson, discuss the transformative potential of generative and agentic AI in business, focusing on customer service, HR, and workplace innovation. They explore real-world use cases, the challenges of managing diverse data sources, and the tools and technologies shaping the future of AI which lead to…. Data Challenges : Cindi and Bartley discuss the complexities of managing structured, semi-structured, and unstructured data in the context of generative AI. They explore the challenges and opportunities presented by different data formats. Tools and Technologies : Bartley provides guidance for AI and tech leaders on evaluating and building AI agents, emphasizing the importance of listening to employee needs and selecting the right tools for specific use cases. Real-World Use Cases : The conversation digs into practical applications of agentic AI, with a focus on customer service and software development. Bartley highlights examples of how companies are using AI agents to improve efficiency and productivity. The Future of AI : The episode concludes with a look ahead at the future of AI, with Bartley sharing his optimism for the transformative potential of agentic AI and offering advice for data and AI leaders Discover the creative facets that inspire Bartley and how data has been a driving force in his life since earning his PhD. Key Moments: Understand agentic AI : Bartley explains how agentic AI is one of the most exciting and transformative developments in the AI space, evolving from generative AI and LLMs (large language models) to create systems capable of taking actions on behalf of users. (2:20) Use Case Summary - AI-Powered Agentic Workflows at NVIDIA : NVIDIA has embraced agentic AI workflows to enhance both employee efficiency and customer experience. A prime example is their implementation of Agent Morpheus, a system designed to streamline software delivery and security processes. (13:16) AI is the new HR : Bartley highlights how generative AI has been effectively applied in HR, particularly in employee handbooks and onboarding documents. HR documents, often buried in PDFs, contain a wealth of structured data, making them a rich source for AI applications. (15:26) Data ingestion within the future of data processing : Bartley hones in on the primary concern of how data is ingested and how structured queries are executed in ways that align with business needs. The technology is progressing rapidly, but refinement is still needed for impactful data usage. (37:43) Key Quotes: "Generative AI and agentic AI are really exciting because we're finally at the point where the experience of using AI meets our expectations. It's no longer just a label or something that might be statistics; it's something meaningful in our day-to-day life." -Bartley Richardson "If I had to pick the time to be alive and in this industry, it would be right now. The amount of progress just leaps every day, with new breakthroughs, announcements, or capabilities that didn't exist the day before." -Bartley Richardson "AI does not absolve you of critical thinking and this data literacy thing. If anything, it amplifies the need for this." -Bartley Richardson Mentions: How to Create a Data and AI Literate Company with Bridgestone and The Data Lodge Ersilia Open Source AI CEO Gemma Turon Examines Ersilia’s Impact on Biomedical Research The Happiness Hypothesis: By Jonathan Haidt Setting the Table: By Danny Meyer Guest Bio: Bartley Richardson is CTO of NVIDIA AI agentic software and Director of Engineering for cybersecurity AI development and product engagement, including accelerated computing and generative AI. Previously, Bartley was a technical lead on multiple DARPA research projects. He was also the principal investigator of an Internet of Things research project which focused on applying machine and deep learning techniques to large amounts of IoT data to provide intelligence value relating to form function, and pattern-of-life. His primary research areas involve NLP and sequence-based methods applied to cyber network datasets as well as cross-domain applications of machine and deep learning solutions to tackle the growing number of cybersecurity threats. He holds a PhD in Computer Science and Engineering with a focus on AI. Hear more from Cindi Howson here . Sponsored by ThoughtSpot .
