Answer extracted from the The Josh Bersin Company podcast — listen to the full episode below.
Why do non-technical business users often drive AI adoption better than technologists?
Non-technical business users tend to adopt and innovate with AI technology more effectively than technologists because they focus on solving specific business problems rather than engineering complexity. They approach tools pragmatically, which accelerates real-world impact, while subject matter experts remain critical for providing domain knowledge and ensuring accuracy.
This pattern reveals a fundamental difference in how each group approaches technology. When technologists encounter AI tools like Databricks, they often spend time optimizing infrastructure, debating architectural decisions, or exploring edge cases. Business users, by contrast, ask a simpler question: "How does this solve my immediate problem?"
Program managers without technical backgrounds have successfully built their own applications and solutions using Databricks, demonstrating that the right platform design can lower barriers to entry. As Roshana Golani discusses in the episode, the technology should be architected specifically to make it easy for both subject matter experts and non-technical users to contribute, rather than requiring deep engineering knowledge before someone can create value.
The key insight is that business friction, not technical sophistication, should drive adoption. When organizations remove the friction points between a business user's need and the ability to address it with AI, adoption accelerates naturally. This doesn't diminish the role of subject matter experts—their knowledge of domain rules, edge cases, and quality standards remains irreplaceable—but it does reshape how enablement and product design should prioritize accessibility.
"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. With a career spanning VMware, Amazon, and Google, Golani has pioneered skills transformation and enablement during major technology shifts, including virtualization, cloud adoption, and now AI. At Databricks, she leads enablement across sales, marketing, and product engineering within the field organization, reporting to a company co-founder.
This principle challenges the traditional assumption that technical depth is the prerequisite for AI innovation. Instead, it suggests that organizations seeking to scale AI adoption should prioritize designing systems that meet users where they are, not where engineers wish they were. The episode explores how Databricks structures its own internal learning and enablement to operationalize this insight at scale across its customer base and go-to-market functions.
See also
How can AI systems like Genie generate personalized training content dynamically rather than relying on static course libraries?
AI can consume multiple input sources including PRDs, product documentation, webinars, and knowledge transfer sessions, then dynamically create customized training content tailored to individual learner needs.
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 to automatically identify and map employee capabilities without relying on manual skill taxonomy maintenance.
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 and support directly within the context where work actually happens.
Key takeaways
- Business users innovate faster with AI because they prioritize problem-solving over technical elegance.
- Subject matter experts remain essential for domain knowledge, quality standards, and preventing errors.
- Platform design should lower friction for non-technical users to contribute, not require deep engineering expertise.
- Organizations scaling AI adoption should measure success by business impact solved, not technical complexity deployed.