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Adopting medieval approaches for leading-edge technology in AI apprenticeships
In previous eras, where more jobs required hands-on work, apprenticeships would play a major role in the transfer of specialized skills — from carpenters to blacksmiths. Even today, this model is leveraged in industries such as manufacturing, where apprenticeship programs provide on-the-job learning in technical areas to create highly skilled staff and increase productivity. According to one study, 91% of apprentices continue at a job where they receive training, reducing turnover and overall training costs.1 In the apprenticeship format, the teacher or mentor works together with the “apprentice” on a skill or task. Rather than trying to grasp an abstract concept, the apprentice can learn by watching and doing the task directly. The teacher directly models their thought process or approach, empowering the mentee to iterate on a task and build their proficiency.
Applying the apprenticeship model to AI learning can help accelerate experimentation and build confidence in the workforce. A mentor or teacher might walk through their approach to using web-based GenAI to generate an outline for a business report. They may demonstrate how they develop the prompt for the GenAI model, explaining their word choices and the data they included. The apprentice can take this example and apply it to their own reports, but also broaden the application for other content or writing needs, expanding possible use cases. This model also moves away from the traditional mode of career development, where knowledge transfers occur from the top down, curated training curriculum or a buildup of skills as an individual progresses. AI expertise may reside across all levels of an organization, often with greater concentration of AI literate individuals in the middle or early part of their career. Incorporating apprenticeships as not just a style of learning but as a leadership mindset within day-to-day work promotes employees to create spaces for modeling skills whenever they collaborate on or assign tasks.
Early adopters within your organization will be candidates to become mentors to late adopters, allowing more experienced professionals to educate and train their colleagues. This community-based practice encourages individuals who innovate to lead and encourages a culture of codevelopment and collaboration. Apprentices learn how to apply AI leading practices continuously and within their daily work and in the context of hands-on applications, rather than on the basis theory or policy first. Identifying which groups of adopters your workforce falls into within each of the two groups — apprentices and teachers — will help build the structure of AI apprenticeships that supports the uptake of AI in a secure and responsible manner. The groups will be fluid, as more people become confident and can demonstrate how AI can be applied in tangible use cases and to produce business value. More active users of the technology may sound like a risk, but we’ve found just the opposite to be true if the right governance is in place to enable a culture of innovation at scale.