A live interactive workshop for owners, operators and asset managers exploring how close we really are to running multifamily properties with little—or no—full-time staff.
 

Property management has always been a people-heavy business.
 

​But automation, AI, centralization, and global labor arbitrage are quietly changing that reality—one workflow at a time.
 

​In this workshop, we’ll walk through a thought experiment turned operating blueprint: Asimov Management, a full-service multifamily property manager designed to operate with zero full-time employees. From leasing and onboarding to maintenance, renewals, and reporting, we’ll examine how far today’s technology stack can realistically replace—or radically augment—traditional on-site roles.
 

​This is not a pitch for cutting headcount at all costs. Instead, it’s a clear-eyed exploration of what can be automated today, where humans still matter, and how operators should think strategically about service levels, resident experience, and asset positioning in an increasingly automated world.
 

​You’ll leave with a practical framework for evaluating automation opportunities in your own portfolio—and a clearer view of what the future of property management may actually look like.
 

​Participants will receive post-workshop access to our community on Circle, where we’ll share recordings, recommended tools, curated vendor lists, and implementation guides.

You’ll Learn How To

​Understand what “zero-employee” really means

​Learn which property management functions can be fully automated today, which require humans-in-the-loop, and which still demand on-site presence.
 

​Design a centralized “brain” for operations

​Explore how PMS platforms, integration layers, and general-purpose AI can work together to coordinate complex property workflows.
 

​Automate the front of the house

​See how marketing, leasing, touring, pricing, onboarding, and payments can operate end-to-end with little to no human involvement.
 

​Rethink maintenance and resident service

​Understand how AI, marketplaces, and predictive systems can replace traditional on-site teams—without sacrificing resident experience.
 

​Evaluate the tradeoffs

​Learn when automation creates value, when it risks degrading service or asset quality, and how these decisions intersect with Class A/B/C positioning.
 

​Prepare for what’s coming next

​Assess how improving AI reliability, computer vision, and predictive maintenance could invert resident preferences toward machines over humans.

Questions about the workshop?
View FAQs on format, access, recordings and more: FAQs

The Zero-Employee Thought Experiment

• ​Why property management is uniquely hard to automate

• ​Lessons from Asimov Partners (zero-employee development)

• ​Where the constraints still are—and why they’re shrinking
   

​The Central Operating Stack (“The Brain”)

• ​PMS as system of record and evolutions of the PMS

• ​Integration layers and workflow automation

• ​Generalist AI as decision-maker for unstructured data

• ​Why integrations—not AI—are the biggest bottleneck
   

​Marketing, Leasing, and Onboarding at Scale

• ​Automated listings, pricing, and lead management

• ​AI leasing agents and self-guided tours

• ​Screening, lease execution, and onboarding workflows

• ​Payments, rewards, and ancillary revenue automation
   

​Customer Service & Maintenance Without Staff

• ​AI-first resident support and escalation logic

• ​Offshored human-in-the-loop models

• ​Maintenance dispatch via marketplaces

• ​Cost control, duplicate ticket prevention, and QA

• ​Predictive vs. reactive maintenance
   

​Renewals, Reporting, and Asset Intelligence

• ​Renewal automation

• ​Package management without on-site staff

• ​AI-generated owner reporting and narrative insights
  Who is the “asset manager” in an automated world?
   

​The People Problem

• ​Why headcount reduction isn’t always the answer

• ​Service levels, perception of quality, and cap rates

• ​When centralization breaks—and why

• ​How resident expectations may flip in favor of machines