Answer extracted from the The Josh Bersin Company podcast — listen to the full episode below.
How can AI systems like Genie generate personalized training content dynamically rather than relying on static course libraries?
AI systems consume multiple input sources—PRDs, product documentation, webinars, and knowledge transfer sessions—then dynamically generate customized training content tailored to individual learner needs and context. The system maintains instructional quality through human-in-the-loop oversight and uses ontology plus product telemetry to ensure content remains relevant to specific use cases, such as teaching a solutions architect how to demo Databricks to a manufacturing customer.
From Static Libraries to Real-Time Customization
Traditional training platforms lock content into predetermined courses. Dynamic AI systems like Genie fundamentally invert that model by treating training as a real-time response to actual job context, not a one-size-fits-all repository.
Rather than asking "What course should this person take?", the system asks "What is this person trying to do right now, and what knowledge do they need to succeed at it?" This shift is explained in detail in this episode of The Josh Bersin Company, where enablement leaders discuss how AI is reshaping learning operations.
The Human-in-the-Loop Safeguard
Raw AI generation without oversight risks hallucination and instructional errors. Databricks maintains human review at critical checkpoints, ensuring that dynamically generated content meets pedagogical standards before it reaches learners.
This hybrid approach—AI for speed and scale, humans for judgment and accuracy—is what separates responsible enablement from reckless automation. As Roshana Golani emphasizes in the full podcast episode, the goal is not to replace instructional expertise but to amplify it.
"We need to understand what is it that the solution architects were doing? Where were they doing it? And can I bring it to them in a way that makes sense?"
Roshana Golani — VP of Enablement at Databricks. Golani brings over a decade of experience transforming how organizations skill their workforces across disruptive technology cycles—from virtualization and cloud migration to AI adoption. She sits within Databricks' field engineering organization, directly accountable to a co-founder, and leads enablement across sales, marketing, and product engineering functions.
That question captures the entire philosophy: context matters. Personalization means understanding not just who is learning, but where and when they need to learn it. A deeper dive into how Databricks operationalizes this principle is available in the full conversation.
Ontology and Telemetry: The Intelligence Layer
Behind every personalized recommendation sits a knowledge map. Ontology—a structured model of how concepts relate—allows the system to understand which skills connect to which outcomes. Telemetry from actual product usage shows where learners struggle and what they're trying to accomplish.
Together, these inputs let Genie infer not just what content to generate, but how to frame it. Teaching someone to demo Databricks to a manufacturing customer requires different emphasis than teaching a data engineer to optimize a pipeline. The system adapts both substance and tone based on audience and use case.
For more on how Databricks integrates its internal tool stack—Skills Navigator, Genie, and others—to create a cohesive learning operation, listen to Roshana Golani's full interview.
See also
What is Skills Navigator and how does it infer employee skills without traditional taxonomies?
Skills Navigator is an internally-built tool at Databricks that uses a skills inference engine running on the Databricks data lakehouse to automatically identify and map employee capabilities based on actual work performed.
How can organizations move from traditional push-based training certification models to AI-driven performance enablement in the flow of work?
Rather than starting with course design and certifications, organizations should identify the actual job being performed and deliver just-in-time training at the moment of need, integrated directly into the workflow.
What are the key limitations of AI video interviewing tools currently?
Even the most sophisticated AI video interviewing tools from companies like Eightfold and HireVue are mostly used only for screening, not final assessment, and have not yet achieved widespread adoption for comprehensive hiring decisions.
Key takeaways
- Dynamic AI systems ingest multiple knowledge sources—documentation, webinars, product telemetry—and generate training on demand rather than relying on static course libraries.
- Human-in-the-loop review maintains instructional soundness and prevents AI hallucination, ensuring quality at scale.
- Ontology and product telemetry allow the system to match content not just to the learner, but to the specific job context and use case they're tackling in the moment.
- Personalization means understanding where and when someone needs to learn, not just who they are—enabling training delivery in the flow of actual work.