Answer extracted from The Josh Bersin Company podcast — listen to the full episode below.
How can organizations move from traditional push-based training certification models to AI-driven performance enablement in the flow of work?
Stop designing courses first. Instead, identify the actual job being performed and deliver just-in-time training exactly when the person needs it, using telemetry from the tools they already work in. By measuring real performance and productivity—not test scores—enablement becomes personalized and context-aware, shifting from "in case you need this someday" to "here's what you need right now."
Moving from certification mindset to performance measurement
Traditional L&D asks: "What course should we build?" The new model asks: "What is the person actually trying to accomplish right now?" This is not a small shift—it fundamentally changes where and when learning happens. As Roshana Golani explains in The Josh Bersin Company, the real power comes from embedding support directly into the workflow, not pulling people out of their work to sit in training.
The measurement changes too. Instead of tracking course completion rates or certification badges, organizations measure what actually matters: did productivity go up? Did the sales rep close the deal? Did the solution architect solve the customer's problem? Telemetry from CRM systems, product infrastructure, and collaboration tools reveals exactly where gaps exist and who needs help.
Building context-aware, just-in-time enablement at scale
At Databricks, which serves over 20,000 customers globally and grows 80 percent year over year, the scale of traditional training is impossible. You cannot build enough courses. Instead, Golani's team uses data from where work actually happens—tools like Salesforce, Slack, and the product itself—to understand what solution architects and sales engineers are doing and where they struggle.
This approach means personalization becomes automatic, not manual. Rather than offering the same curriculum to everyone, the system identifies that Person A needs help with a specific integration in this customer conversation, while Person B needs coaching on discovery questions. The training is delivered in context, at the moment of need, often via conversational AI or micro-learning tools integrated into existing workflows. As Golani notes, "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?"
The shift also addresses a deeper truth about transformation: over 60 percent of people initially fear that AI will replace their jobs. When enablement feels personalized and supportive rather than mandatory compliance, it becomes a signal that the organization is investing in people, not just implementing technology.
"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 has led skills transformation and enablement at major technology companies including VMware, Amazon, and Google, guiding organizational change during periods of virtualization, cloud migration, and now data and AI adoption. At Databricks, she sits within the field engineering organization and reports directly to a co-founder, giving her unique authority to reshape how the entire go-to-market team—spanning sales, marketing, and product engineering—learns and performs.
For a deeper look at how this plays out in practice at a hypergrowth company, listen to Roshana discuss Databricks' learning infrastructure and the specific tools like Skills Navigator that power this model.
Key takeaways
- Start with the job and the workflow, not with course design—understand where people are struggling before you build enablement.
- Use telemetry and data from real work tools (CRM, product, messaging) to identify gaps and deliver micro-learning in context, not classroom training.
- Measure performance outcomes—productivity, deal closure, customer satisfaction—rather than certification completion rates.
- Personalized, just-in-time enablement signals organizational investment in people and reduces fear around technology adoption.
See also
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, limiting their real impact on hiring quality.
What role does industrial-organizational psychology play in the future of AI recruiting?
Smarter IO (industrial-organizational psychology) assessments built into AI represent the future direction of recruiting technology, moving beyond surface-level pattern matching.
How should companies differentiate their AI recruiting approach by job type?
For entry-level or low-skilled roles, companies can automate heavily and move through a quick process focused on basic skills, while senior or specialized roles require deeper assessment and human judgment.