It's nearly impossible to hire senior product or engineering leaders who are also fluent in AI. The advice for experienced managers is to step back into an Individual Contributor (IC) role. This allows them to build hands-on AI skills, demonstrating the humility and beginner's mindset necessary to lead in this new era.
Related Insights
To prepare for a future of human-AI collaboration, technology adoption is not enough. Leaders must actively build AI fluency within their teams by personally engaging with the tools. This hands-on approach models curiosity and confidence, creating a culture where it's safe to experiment, learn, and even fail with new technology.
AI is a 'hands-on revolution,' not a technological shift like the cloud that can be delegated to an IT department. To lead effectively, executives (including non-technical ones) must personally use AI tools. This direct experience is essential for understanding AI's potential and guiding teams through transformation.
Product leaders must personally engage with AI development. Direct experience reveals unique, non-human failure modes. Unlike a human developer who learns from mistakes, an AI can cheerfully and repeatedly make the same error—a critical insight for managing AI projects and team workflow.
Simply instructing engineers to "build AI" is ineffective. Leaders must develop hands-on proficiency with no-code tools to understand AI's capabilities and limitations. This direct experience provides the necessary context to guide technical teams, make bolder decisions, and avoid being misled.
To effectively learn AI, one must make a conscious mindset shift. This involves consistently attempting to solve problems with AI first, even small ones. This discipline integrates the tool into daily workflows and builds practical expertise faster than sporadic, large-scale projects.
The pace of change in AI means even senior leaders must adopt a learner's mindset. Humility is teachability, and teachability is survivability. Successful leaders are willing to learn from junior colleagues, take basic courses, and admit they don't know everything, which is crucial when there is no established blueprint.
GSB professors warn that professionals who merely use AI as a black box—passing queries and returning outputs—risk minimizing their own role. To remain valuable, leaders must understand the underlying models and assumptions to properly evaluate AI-generated solutions and maintain control of the decision-making process.
AI's rise means traditional product roles are merging. Instead of identifying as a PM or designer, focus on your core skills (e.g., visual aesthetics, systems thinking) and use AI to fill gaps. This 'builder' mindset, focused on creating end-to-end, is key for future relevance.
To lead in the age of AI, it's not enough to use new tools; you must intentionally disrupt your own effective habits. Force yourself to build, write, and communicate in new ways to truly understand the paradigm shift, even when your old methods still work well.
The AI startup scene is dominated by very young founders with no baggage and repeat entrepreneurs. Noticeably absent are mid-level managers from large tech companies, a previously common founder profile. This group appears hesitant, possibly because their established skills feel less relevant in the new AI paradigm.