A tenant rep broker can now canvass an entire submarket for new assignments before the Monday all-hands: pull every company with a lease expiring in the next 18 months, build a profile on each one, rank them by likelihood to move, and draft ten personalized pitches for the tenants worth chasing first.

A landlord rep can walk into an owner meeting with intelligence no one else has: which of the landlord's tenants are outgrowing their space or quietly shopping the market, which tenants across competing buildings are in play, and where the next lease-up demand is coming from, all assembled in an afternoon instead of a week.

This used to take weeks of work, and only with a good analyst or associate on hand. Now it is the work of an AI agent on autopilot. Not a chatbot that writes a nicer email, but a workflow that takes a target submarket and hands back a ranked canvassing list with a personalized angle for every name on it.

The brokers figuring this out first are not more technical than anyone else. They have stopped treating AI as a writing assistant and started treating it as a teammate that canvasses the market on their behalf. Most brokers have no system for building any of this. This workshop gives them one.

The session runs on a single fictional case study: Maya, a CRE agent working both landlord and tenant rep, and her pitch to win a headquarters relocation. It works the full arc, building her brand, researching and canvassing her prospects, assembling a differentiated pitch, and sourcing space her competitors will never find.