While most conversations about generative AI focus on chatbots, Thomas Wiecki (PyMC Labs, PyMC) has been building systems that help companies make actual business decisions. In this episode, he shares how Bayesian modeling and synthetic consumers can be combined with LLMs to simulate customer reactions, guide marketing spend, and support strategy.
Drawing from his work with Colgate and others, Thomas explains how to scale survey methods with AI, where agents fit into analytics workflows, and what it takes to make these systems reliable.
We talk through:
Using LLMs as “synthetic consumers” to simulate surveys and test product ideas
How Bayesian modeling and causal graphs enable transparent, trustworthy decision-making
Building closed-loop systems where AI generates and critiques ideas
Guardrails for multi-agent workflows in marketing mix modeling
Where generative AI breaks (and how to detect failure modes)
The balance between useful models and “correct” models
If you’ve ever wondered how to move from flashy prototypes to AI systems that actually inform business strategy, this episode shows what it takes.
LINKS:
The AI MMM Agent, An AI-Powered Shortcut to Bayesian Marketing Mix Insights (
https://www.pymc-labs.com/blog-posts/the-ai-mmm-agent)
AI-Powered Decision Making Under Uncertainty Workshop w/ Allen Downey & Chris Fonnesbeck (PyMC Labs) (
https://youtube.com/live/2Auc57lxgeU)
The Podcast livestream on YouTube (
https://youtube.com/live/so4AzEbgSjw?feature=share)
Upcoming Events on Luma (
https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
🎓 Learn more:
Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (
https://maven.com/s/course/d56067f338) —
https://maven.com/s/course/d56067f338 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit
hugobowne.substack.com
