I’ve been thinking a lot lately about my grandmother, who immigrated to the US from the Czech Republic as a child. Although she was incredibly gentle, she had hands that were weathered from decades of work, but incredibly skilled. They were capable of turning a bushel of strawberries into perfectly sealed jars of jam that would last through winter, or transforming a pile of fabric scraps into dresses that kept me clothed throughout my childhood. She taught me these “maker” skills not because they were economically necessary in our age of mass production, but because she understood something profound about the value of learning through doing.
Today, as I discuss AI transformation with nervous business leaders, I find myself returning to those lessons. Not because I’m nostalgic for pre-industrial life, but because I’m concerned we’re about to repeat a familiar pattern of lost knowledge – this time in the professional world.
The Coming Disruption
The statistics are sobering. Some sources suggest that up to 50% of entry-level white-collar jobs could be eliminated by AI within the next five years. Unlike previous technological disruptions that primarily affected manufacturing, this wave targets the cognitive tasks that have traditionally served as training grounds for professional development.
Consider the career path of a typical research analyst. They might start by cleaning data, building basic models, and preparing routine reports. These tasks might seem mundane but they actually teach pattern recognition, attention to detail, and an intuitive understanding of how numbers tell stories. If AI handles these foundational activities, how will the next generation develop the judgment and expertise needed for senior roles?
The Pleasure of Finding Things Out
Nobel physicist Richard Feynman wrote about “the pleasure of finding things out,” which is the deep satisfaction that comes from wrestling with problems and arriving at insights through your own intellectual effort. This isn’t just about personal fulfillment; it’s about developing the kind of creative problem-solving abilities that organizations desperately need.
When my grandmother taught me to make jam, she didn’t just show me the steps. She explained how to test for doneness by listening to the way the mixture bubbled, how to judge the ripeness of the fruit by touch, and how to adjust for variations in temperature and altitude. These weren’t skills you could learn from a manual, they required experience, experimentation, and yes, occasional failure.
Similarly, junior professionals traditionally learned their craft through what we might call “productive struggle.” They developed intuition by making mistakes, spotting patterns through repetition, and gradually building the contextual understanding that separates true expertise from mere technical competence.
The Knowledge Transfer Crisis
What worries me most isn’t just the potential job displacement – it’s the disruption of knowledge transfer systems that have operated for generations. In my grandmother’s time, practical skills passed naturally from parent to child, master to apprentice. Today’s organizations face a similar challenge: how do we ensure that the hard-won wisdom of experienced professionals reaches the next generation?
This tacit knowledge, the understanding of organizational dynamics, reading between the lines in client conversations, and knowing when data doesn’t pass the smell test, might be the most valuable asset companies have. Yet it’s also the most vulnerable to disruption.
Reimagining Professional Development
The solution isn’t to reject AI, but to thoughtfully redesign how we develop talent in an AI-augmented world. Several approaches show promise, including the following:
- Deliberate Mentorship Programs: Rather than assuming junior employees will naturally absorb knowledge through routine work, companies need formal mechanisms for knowledge transfer. This might look like structured apprenticeships where experienced professionals guide newcomers through increasingly complex challenges.
- AI-Augmented Learning: Instead of having AI provide answers, we could design systems that guide junior professionals through problem-solving processes, asking Socratic questions that help them develop their own analytical frameworks. The goal is to preserve the “pleasure of finding things out” while leveraging AI’s capabilities.
- Hybrid Collaboration Models: New roles might emerge where humans and AI work together, with junior employees responsible for writing complex prompts, interpreting AI outputs, identifying patterns and stories, and making judgment calls that require contextual understanding.
- Experimentation Laboratories: Companies could create “learning labs” where junior employees can experiment, fail safely, and develop the kind of creative problem-solving abilities that can’t be automated.
These approaches share a common thread: they prioritize developing human judgment and wisdom alongside technological capability. This dual investment creates something competitors focused purely on AI efficiency will struggle to replicate.
The Competitive Advantage of Wisdom
Organizations that recognize this challenge early might gain significant competitive advantages. While competitors focus solely on AI efficiency, companies that invest in thoughtful talent development could develop deeper capabilities in areas that remain uniquely human: strategic thinking, creative problem-solving, and the ability to navigate complex, ambiguous situations.
Just as my grandmother’s canning skills proved invaluable in sustaining our family through periods where we simply couldn’t afford to purchase produce at the market, the foundational competencies we’re at risk of losing might become critical differentiators in an increasingly automated world.
Preserving the Craft
The question isn’t whether AI will transform the workplace, but rather if we’ll be intentional about preserving and transmitting the human elements that make organizations truly effective. This requires us to think carefully about which experiences are essential for professional development and how to create pathways for those experiences in an AI-augmented environment.
My grandmother’s hands knew things that no manual could teach. In our rush to embrace AI’s capabilities, we must ensure that the next generation of professionals has the opportunity to develop their own kind of knowledge: the judgment, intuition, and creative problem-solving abilities that emerge only through experience.
The pleasure of finding things out isn’t just a luxury for curious minds, it’s the foundation of the expertise our organizations will need to thrive in an uncertain future. Our challenge is to preserve it while embracing the transformative potential of AI.
My grandmother’s food preservation skills that she passed on to my father and, eventually, to me, sustained our family through many seasons. What knowledge are we passing on to the generation that will inherit our AI-transformed world?