By now, most of you are probably familiar with Google’s latest AI innovation: the podcasting tool integrated into Notebook LM, which has quickly become a viral sensation in the AI community. This tool enables users to upload a variety of content, such as links, videos, and PDFs, and in return, it generates a 10-minute podcast-style discussion. The AI synthesizes the material into a dynamic conversation between a male and female voice, offering an engaging and interactive way to break down complex topics.

While this kind of AI innovation is impressive and creates viral hits, virality alone doesn’t build sustainable skills or businesses. These tools are great, but human effort is essential to creating an impactful podcast highlighting key points.

This got me thinking about how AI can support recruiters, workforce planners, and skills architecture teams. After much reflection, I’ve identified a critical skill that I’m calling “Mapping Skills Data Assets.”

To demonstrate this, we’ve attached a detailed spreadsheet (I hope you enjoy the level of detail). The key opportunity here is to create a comprehensive map across multiple attributes for each job family, which can serve as a “master guide” for all internal and external systems. In the example we’ve provided for the Legal job family, if you scroll from left to right, you’ll see definitions, O*NET comparators, key workloads, core skills, and soft skills. This kind of mapping makes it easier to pinpoint where AI will impact specific skills.

We’ve generated these mappings more efficiently with Draup’s Curie and other models. The mappings can serve as a common framework that integrates seamlessly across your HRIS, LMS, external market systems, global standards like O*NET, and country-specific standards, such as those from Nasscom. This unified framework ensures consistency and alignment across different platforms and standards, enabling more efficient workforce planning and skills development.