talent development

Organizations are increasingly focused on the Future of Work due to the unprecedented convergence of the AI revolution and workplace transformation. New capabilities rapidly emerge, forcing organizations to reimagine their approach to talent development. One of those capabilities is the quality and sophistication of AI agents. These digital companions are extensions of individual capabilities, creating unique professional signatures that combine human expertise with customized AI support (Josh Bersin calls them Superworkers). L&D leaders must prepare for a workforce where success depends not just on individual skills, but on the synergetic relationship between human talent and their AI extensions. This aligns with changing expectations, particularly among younger generations, to prioritize growth opportunities and demand a more dynamic and personalized approach to professional development. L&D leaders are challenged to realize both organizational agility and individual career resilience.

A common framework for IC development is the 70/20/10 rule: 

• 70% of learning happens on the job by means of facing and overcoming tough challenges

• 20% of learning takes place via relationships and peer learning, and

• 10% of learning is covered via formal training / development initiatives

As such, IC development in itself requires an integrated approach to outpace the rate of change. And while the framework is sound, making measurable progress is hard (as pointed out in this Forbes article). A good adaptation of the 70/20/10 framework is the 3:1 proposal: "for every one formal learning event, you should design and facilitate three on-the-job application exercises." To make that scalable, a strong, and ideally AI-powered, learning platform (e.g.,Sana Labs) in indispensable. 

In addition to consulting organizations regarding the creation and activation of career tracks / rubrics, I offer access to a Learning Management System [LMS] to host live workshops, offer customized, self-paced, courses and in-the-flow-of-work micro-learning experiences. Examples of such experiences are: