AI in commerce is no longer theoretical. It’s here, it’s fast, and it’s being adopted everywhere.
But as JP Beeghly at Sonos and Josh Maynard at MrBeast make clear, the real question isn’t whether AI works. It’s whether it can be trusted.
JP has been stress-testing Chord’s Commerce Copilot more than almost anyone in the customer base — not looking for surface-level answers, but pushing it to handle Sonos-specific nuance. The problem isn’t access to data. It’s context. Enterprise AI must traverse multiple tables, definitions, attribution rules, and business constraints to answer what appears to be a simple question.
Josh reinforces the same issue from a different angle. Without centralized, structured data, teams feeding different spreadsheets into ChatGPT will get contradictory answers. AI amplifies fragmentation if the foundation isn’t sound.
One of the biggest surprises? Dimensional modeling isn’t obsolete in the age of LLMs — it’s more important than ever. If AI is writing SQL, then data models must be designed for AI query patterns, not just dashboards.
Both leaders admit something refreshingly honest: their organizations aren’t fully training AI yet. And that becomes the core theme of the episode. AI should be onboarded like a junior employee — given access, trained on terminology, supervised, and gradually trusted with more responsibility.
The conversation also tackles quality control, non-deterministic answers in business intelligence, compressed iteration cycles, and why brand remains fundamentally human territory.
This isn’t an AI hype episode. It’s an operator’s guide to closing the gap between promise and implementation.
In this episode, you’ll learn:
- Why enterprise AI fails without business context
- How dimensional modeling enables reliable AI-generated SQL
- Why onboarding AI like a junior hire improves long-term results
- How AI compresses go-to-market timelines
- The quality control gap in AI-generated outputs
- Why brand strategy remains fundamentally human
- What non-deterministic answers mean for BI trust
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