From Wearing Many Hats to Orchestrating AI Specialists

In corporate consulting, versatility is sold as a virtue. We are expected to be the strategist, the data analyst, the project manager, and the client whisperer—often in the same afternoon. We call this “wearing many hats.”

This is a broken model.

The constant role-switching isn’t a strength; it’s a source of profound inefficiency. Each shift imposes a cognitive tax. The overhead of context switching eats away at focus, dilutes expertise, and pushes us into the dangerous territory of the “jack of all trades, master of none.” Productivity doesn’t just slow down; deep, meaningful work becomes nearly impossible.

A Different Model

In Brandon Sanderson’s Legion series, the protagonist doesn’t just switch roles. His mind generates distinct, fully-realized personas—each a world-class expert in a specific field. When he needs a cryptographer, he manifests one. When he needs a linguist, that aspect appears. He is not a jack of all trades; he is an orchestrator of masters.

This is closer to what AI-assisted work can look like.

The Shift

My journey into AI has fundamentally rewired my professional model. The muscle memory I’m building is no longer about which hat to wear. It’s about which AI “aspect” to manifest for the task at hand.

Instead of manually wrangling a spreadsheet, I instantiate a data analysis agent to identify patterns and generate insights. Instead of grinding through boilerplate code, I deploy a coding copilot to handle syntax and structure, freeing me to focus on system architecture. Instead of sifting through dozens of articles, I task a research agent with synthesizing market trends and delivering a concise executive summary.

AI tools are not just another “hat” to add to the pile. They are the specialized aspects I can summon on demand. The crippling cognitive load of switching contexts is replaced by the seamless orchestration of specialized AI agents.

The New Work

The trap of “wearing many hats” forces you to be the master of none. The orchestration model demands a different skill: becoming the master of integration.

The core work is no longer about possessing every skill yourself. It is about deeply understanding the problem, identifying the right aspect for the job, and integrating its output into a coherent whole.

This is the shift from operator to architect. The challenge is no longer to do everything, but to build a system of AI aspects that allows you to accomplish anything.

I’m still figuring out where the boundaries are—which tasks benefit from AI aspects and which require the sustained focus that only human attention provides. But the direction feels right: less context-switching, more orchestration.