درباره این اپیزود
Robin Sutara is Field Chief Data Strategy Officer at Databricks, where she works with organizations facing a common problem: employees sit through AI training, check the box, then nothing changes. The issue isn't awareness. It's that a store manager, plant floor worker, and data scientist need completely different capabilities, but most organizations treat them identically.
Robin breaks down why generic AI literacy programs fail to drive behavior change and how to build persona-specific enablement instead. She explains why your data teams need to sit with domain users (like riding in trucks with electric utility line workers) to understand their actual workflows, how to update performance KPIs to reinforce new behaviors, and why organizations should study their pandemic response as a template for AI transformation speed. The conversation covers JetBlue's production agentic systems, JP Morgan's executive-level AI representation, the Databricks AI Security Framework's 62 risk factors for prioritization, and the specific criteria for choosing which use cases to ship when data isn't perfect across your entire estate.
Topics discussed:
- Persona-based enablement replacing one-size-fits-all AI literacy programs
- Sitting with domain users to translate AI capabilities into changed workflows
- Updating performance KPIs and organizational processes to reinforce AI behaviors
- Defining acceptable failure rates and safe experimentation spaces for pilots
- Applying pandemic-era instant transformation tactics to AI adoption cycles
- Prioritizing use cases where data foundations are ready while modernizing the rest
- JetBlue's agentic systems aggregating weather, maintenance, staffing, and customer data
- JP Morgan Chase's approach to AI representation at executive level
- Databricks AI Security Framework's 62 risk factors for balancing innovation and controls
