The Lazy CEO Podcast

AI Business Use Cases And The Future

by Sep 9, 2024The Lazy CEO Podcast

apple
spotify
stitcher
google podcast
Deezer
iheartradio
tune in
partner-share-lg

In this Episode About AI Business Use Cases And The Future

  • AI Augmentation in Business: Ronak Patel discusses how AI and ML are being used to enhance human productivity in business processes, such as customer service, HR, and financial functions, emphasizing that AI typically serves as an assistant rather than a full replacement for human roles.
  • Smaller, Purpose-Built AI Models: Patel highlights a trend towards smaller, targeted AI models that are more cost-effective and accessible for small—and medium-sized companies. This trend democratizes advanced AI technology previously available only to large enterprises.
  • Challenges and Solutions: The discussion covers challenges like data requirements, high costs, and AI hallucinations, with Patel suggesting strategies such as adjusting AI settings to reduce creativity and improve reliability, ensuring AI outputs are accurate and dependable.

Here is a Glimpse of Business Use Cases And The Future

In this episode of The Lazy CEO Podcast, host Jim Schleckser talks with Ronak Patel, CEO of Sunflower Labs, about integrating AI and machine learning (ML) into business processes to augment human efforts. Patel explains AI’s potential, common use cases, and ethical considerations, especially around AI as an assistant rather than a full replacement for human roles. They discuss AI’s current applications, such as automating customer service, HR functions, and financial processes. They emphasize that AI is most effective when used to enhance human productivity rather than replace it.

Patel highlights the growing trend of smaller, purpose-built AI models that are more cost-effective and targeted than massive, general-purpose models like ChatGPT. These models can be deployed even by smaller companies, democratizing access to advanced technology previously available only to large corporations with substantial budgets.

They also touch on the challenges, such as data requirements for training AI, the high costs of large language models, and the risk of AI hallucinations—where AI generates incorrect or fabricated information. Patel suggests solutions, including adjusting the “temperature” setting in AI models to reduce creativity and ensure more reliable outputs, showing that the future of AI lies in controlled, specific applications rather than generalized, all-encompassing solutions.

Resources mentioned in this episode:

Sponsor for this episode…

This episode is brought to you by The CEO Project. The CEO Project is a business advisory group that brings high-caliber, accomplished CEOs together. Our team of skilled advisors comprises current and former CEOs who have run both public and private sector companies across multiple industries. With our experience and expertise, we guide hundreds of high-performing CEOs through a disciplined approach that resolves constraints and improves critical decisions. The CEO Project has helped high-performing, large enterprise CEOs with annual revenues ranging from $20M to over $2 billion to drive growth and achieve optimal outcomes. If you are an experienced CEO looking to grow your company, visit www.theCEOProject.com.

Archives