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In the rapidly changing tech landscape, staying current is a core competency. Product managers should formally schedule time each week to experiment with new AI and product tools. This isn't just about learning; it's about developing new instincts and discovering areas for personal specialization.

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To overcome the fear of new AI technology, block out dedicated, unstructured "playtime" in your calendar. This low-pressure approach encourages experimentation, helping you build the essential skill of quickly learning and applying new tools without being afraid to fail.

As AI automates generalist PM tasks like documentation and context sharing, the role is evolving. The new path to value is specialization. PMs should identify their passion—be it data, design, or prototyping—and master the corresponding AI tools to develop deep, defensible expertise.

For product managers not yet working on AI, the best way to gain experience is to build simple AI tools for personal use cases, like a parenting advisor or a board game timer. Using no-code prototyping tools, they can learn the entire development lifecycle—from ideation to prompting and user feedback—without needing an official AI project at work.

In today's fast-paced tech landscape, especially in AI, there is no room for leaders who only manage people. Every manager, up to the CPO, must be a "builder" capable of diving into the details—whether adjusting copy or pushing pixels—to effectively guide their teams.

The essential skill for AI PMs is deep intuition, which can only be built through hands-on experimentation. This means actively using every new LLM, image, and video model upon release to objectively understand its capabilities, limitations, and trajectory, rather than relying on second-hand analysis.

AI's rapid capability growth makes top-down product specs obsolete. Product Managers now work bottoms-up with engineers, prototyping and even checking in code using AI tools. This blurs traditional roles, shifting the PM's focus to defining high-level customer needs and evaluating outcomes rather than prescribing features.

To upskill a product team in AI, avoid creating a separate, intimidating new skill category. Instead, frame AI as a tool to augment existing competencies like execution (writing user stories), customer insight (synthesizing research), and strategy (brainstorming).

AI's value for PMs is augmentation, not replacement. By automating tactical tasks that consume most of a PM's day (e.g., "six out of eight hours"), AI frees up critical capacity for higher-level strategic, creative, and innovative work—the core functions of a product leader.

The rise of AI tools isn't replacing the PM role, but transforming it. PMs who embrace an "AI-enhanced" workflow for research, docs, and prototyping will gain a massive productivity advantage, ultimately displacing those who stick to traditional methods.

To avoid becoming an "ivory tower" manager, engineering leaders should use side projects as a playground for new technologies. This practice ensures they understand the limitations of new tools like AI and can provide credible, concrete, hands-on guidance to their teams.