Under pressure, organizations tend to shut down external feedback loops for self-protection. This creates a "self-referencing" system that can't adapt. Effective leadership maintains permeable boundaries, allowing feedback to flow in and out for recalibration, which enables smarter, systems-aware decisions.

Exceptional people in flawed systems will produce subpar results. Before focusing on individual performance, leaders must ensure the underlying systems are reliable and resilient. As shown by the Southwest Airlines software meltdown, blaming employees for systemic failures masks the root cause and prevents meaningful improvement.

By replacing the foundational, detail-oriented work of junior analysts, AI prevents them from gaining the hands-on experience needed to build sophisticated mental models. This will lead to a future shortage of senior leaders with the deep judgment that only comes from being "in the weeds."

Adaptable organizations are built on curiosity. This is nurtured not by formal courses, but by leaders encouraging small, daily acts of connecting disparate ideas (e.g., "What did you see this weekend and how can we apply it?"). This builds the collective "mental muscle" for navigating disruption.

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.

While experience builds valuable pattern recognition, relying on old mental models in a rapidly changing environment can be a significant flaw. Wise leaders must balance their experience with the humility and curiosity to listen to younger team members who may have a more current and accurate understanding of the world.

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.

The key technical skill for an AI PM is not deep knowledge of model architecture but a higher-level understanding of how to orchestrate AI components. Knowing what AI can do and how systems connect is more valuable than knowing the specifics of fine-tuning or RAG implementation.

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.