Writing has always been how I solve problems: taking something complex and distilling it into its simplest form. “Writing is thinking. You can’t write clearly if you don’t think clearly.”
For me, AI is a complex topic that I have to write about to understand.
Everyone is rushing to implement it. But most private real estate companies, including Atlas, are lost. We know that AI is a huge paradigm shift that will change the way we work, how we spend our time and which skills are most valued. We know this requires us to rethink our structure and culture.
What we don’t know exactly is how it will all turn out. But we are taking proactive steps to prepare.
The first thing we did was consolidate and organize our data. OMs, PSAs, PPMs, OAs, side letters, loan documents, property level reporting, investor reporting, market data, SOPs, etc. etc. – all the document chaos that defines residential real estate. The backbone of unlocking these powerful AI tools is organized data and standardized processes.
As Alex Robinson of Juniper Square put it, “You can’t automate chaos. When ownership, definitions, access, and change management are broken, AI initiatives fall back into shadow spreadsheets and distrust. Being AI-ready is less about the tech stack and more about whether the organization can absorb a new way of working.”
This process reinforces the value of adaptability:
Now that our data is organized, we’re mapping all the work we do as a company, understanding which tasks can and will be done by AI and which are better done by humans. This includes creating process maps and SOPs for each individual task, from closing a new deal, to negotiating a PSA, to preparing a quarterly investor letter, to preparing an asset for a refinance or sale.
It will quickly become clear which tasks will be performed by AI.
Take acceptance, for example. The initial BOE can be automatically populated using AI tools and data from the T12, rent list, Comps, tax research and market data. Tools like Shortcut do this today. Models that took hours to complete can now be completed in minutes.
But these tools will not replace our analyst. Team members must remain flexible about their roles and take advantage of opportunities to adapt and grow. Think about who you are in a world where AI can do the technical work. Wherever AI goes, there will always be a role for humans.
AI does not replace humans. It makes their domain expertise and what I like to call “shoe leather experience” more valuable.
This is what I mean.
An AI agent can process every line in a document. It can build spreadsheets, extract data, and summarize information faster than any analyst. Those are now table stakes.
But it cannot answer the questions that really matter and ultimately drive returns. Take acquisitions: AI cannot build a reputation where unique opportunities come your way. AI doesn’t have the deep domain expertise and experience to read the seller’s motivation, understand why you should have confidence in the upside, know why you’re in a position to execute on it, and recognize why this opportunity fell into your lap in the first place.
AI cannot visit an equity partner and clearly tell the story behind the deal. And the story is where the value lives.
AI can collect comp data, summarize rental prices, and track absorption trends. But it can’t tell you why residents will choose this property over the Comps.
You get the point.
Expertise is gained from years of walking real estate, talking to teams and residents on site and understanding what actually drives value.
One of the advantages of a relatively small organization is that it’s easier to be agile and rework infrastructure from the ground up without layers of bureaucracy slowing things down.
On a personal level, we all have a choice. You may fear that AI will do what you’re paid to do, and perform the skills you’ve honed throughout your career better than ever before. Or you can use the extra time and support to explore areas of interest.
An acquisition analyst doesn’t have to be limited to data entry and closing deals. They can spend time in markets to uncover untapped areas. They can cultivate broker relationships through in-person events. They can explore the benefits of alternative investment strategies. They can build a personal brand by writing on Twitter and LinkedIn.
Real estate has always been document-rich, but data-poor. AI changes the data part. But the judgment, the pattern recognition built up through years of making deals, becomes the differentiator.
The tools are getting smarter. The question is how we can use them more intelligently.
#framework #thinking #private #real #estate #company #student #real #estate #game