Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs

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Vanishing Gradients

Hugo Bowne-Anderson

62 episodes

4 days ago

A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson. It's time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We'll dive deep into all the moving parts of the data world, so if you're new to the space, you'll have an opportunity to learn from the experts. And if you've been around for a while, you'll find out what's happening in many other parts of the data world.

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Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs

Vanishing Gradients

59 minutes 4 seconds

5 days ago

Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs

Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd Studios, he bridges the gap between data science, AI engineering, and executive strategy to deliver a practical framework for execution.

In this episode, Randy and Hugo lay out how to find and solve what might be considered "boring but valuable" problems, like an EdTech company automating 20% of its support tickets with a simple retrieval bot instead of a complex AI tutor. They discuss how to move incrementally along the "agentic spectrum" and why treating AI evaluation with the same rigor as software engineering is non-negotiable for building a disciplined, high-impact AI strategy.

They talk through:

How a non-technical leader can prototype a complex insurance claim classifier using just photos and a ChatGPT subscription.
The agentic spectrum: Why you should start by automating meeting summaries before attempting to build fully autonomous agents.
The practical first step for any executive: Building a personal knowledge base with meeting transcripts and strategy docs to get tailored AI advice.
Why treating AI evaluation with the same rigor as unit testing is essential for shipping reliable products.
The organizational shift required to unlock long-term AI gains, even if it means a short-term productivity dip.

LINKS

🎓 Learn more:

In Hugo's course: Building AI Applications for Data Scientists and Software Engineers — https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=AI20

Next cohort starts November 3: come build with us!