AI Demystified for Executives
Ever wish you had a coach to help you decipher the AI buzz and hype so you could make better-informed business decisions about what, when, and why to use AI in your business? You'll get that when you tune into the AI Demystified for Executives podcast.
Andrew bridges the gap between complex AI concepts and practical business applications. With experience in both large corporations and high-growth startups, he excels at communicating with business and technical teams alike. As an author, industry thought leader and international speaker, Andrew serves as your trusted advisor and coach on this AI journey.
AI doesn't have to be complicated!
Here is what you can expect from this podcast:
We'll explore a monthly theme with specific topics each week. You'll also receive a free cheat sheet or guide for reference.
In each podcast, our goal is to ensure that you walk away understanding:
- One key AI concept
- Its business applicability
- An actionable takeaway
Our Monday episodes are 7-10 minutes long, perfect for getting up to speed on the week's most important "aha" moments during your commute or while sipping your morning coffee or tea.
On Wednesdays, our episodes run 30-40 minutes, providing a deeper dive into the week's topic. (Maybe a bit much if you're driving!)
Once a month, during the Wednesday podcast, we'll host an interview with either a business or technical professional related to the monthly theme.
AI Demystified for Executives
#12 Implementing AI Agents—From Vision to Execution
In this episode of 'AI Demystified for Executives,' host Andrew Psaltis provides a comprehensive guide on implementing AI agents within organizations. The discussion includes aligning AI strategies with business goals, scoping use cases, and identifying tasks suitable for automation. Practical advice is offered on the build versus buy dilemma, pilot programs for quick wins, and developing internal expertise. The importance of selecting the right large language model (LLM), integrating tools, and continuous learning for AI agents is emphasized. Additionally, the episode addresses ethical considerations, regulatory compliance, and the development of governance policies to ensure responsible AI deployment. Real-world examples and research references are provided to support these strategies.
References:
StreamBench - Towards Benchmarking Continuous Improvement of Language Agents
Code - https://stream-bench.github.io/
Paper: https://arxiv.org/abs/2406.08747
GTA: A Benchmark for General Tool Agents
Code - https://github.com/open-compass/GTA
Paper - https://arxiv.org/abs/2407.08713