AI's current strength lies in enhancing efficiency by handling tasks like summarization and data categorization. It is not suited for big-picture thinking or complex processes. The goal should be to make existing teams more effective—augmenting their abilities rather than pursuing wholesale replacement, which is a common misconception among business leaders.

Effective AI implementation isn't about automating entire human jobs. It's about re-architecting workflows to assign AI the research and analysis tasks it excels at, while preserving relationship-building, empathy, and high-judgment tasks for humans. This division of labor maximizes the strengths of both.

AI tools can handle administrative and analytical tasks for product managers, like summarizing notes or drafting stories. However, they lack the essential human elements of empathy, nuanced judgment, and creativity required to truly understand user problems and make difficult trade-off decisions.

AI can easily write code for system integrations, but the primary bottleneck isn't coding—it's context. The real work involves tracking down employees to understand what ambiguous, legacy data fields actually mean, a fundamentally human task of institutional knowledge discovery.

Adding AI to boost individual speed can paradoxically create more team friction. Organizations with the most advanced automation often report the highest number of coordination issues because they fail to redesign the underlying collaborative workflows, optimizing individual tasks instead of the entire process.

Even when AI automates complex workflows, a human is still required to provide the initial prompt and direction. The nature of work shifts from manual execution to high-leverage direction, but the human role remains critical.

AI can assemble data-rich presentations, but it cannot replicate the human emotional intelligence required for stakeholder management. Understanding an executive's personal values and tailoring a message—like connecting a design system to company values—remains a critical and uniquely human skill for gaining buy-in.

AI agents can flawlessly execute predefined tasks (SOPs). However, they still require significant human management to ensure high-quality output, apply taste, and surface meaningful signals from the data they generate. This creates a new layer of human work, rather than a complete replacement.

Despite AI's capabilities, it lacks the full context necessary for nuanced business decisions. The most valuable work happens when people with diverse perspectives convene to solve problems, leveraging a collective understanding that AI cannot access. Technology should augment this, not replace it.