More than any other post, this one is for me.<\/p>\n
Writing has always been how I solve problems\u2014taking something complex and distilling it into its simplest form. \u201cWriting is thinking. You cannot write clearly if you aren\u2019t thinking clearly.\u201d<\/p>\n
For me, AI is a complex topic I need to write about to understand.<\/p>\n
Everyone\u2019s racing to implement it. But most private real estate firms, Atlas included, are lost. We know AI is a massive paradigm shift that will change the way we work, how we spend our time, and what skills are most valued. We know it will require us to rethink our structure and culture.<\/p>\n
What we don\u2019t know is exactly how it all shakes out. But we\u2019re taking proactive steps to prepare.<\/p>\n
The first thing we\u2019ve done is consolidate and organize our data. OMs, PSAs, PPMs, OAs, side letters, loan docs, property-level reporting, investor reporting, market data, SOPs, etc. etc.\u2014 all the document chaos that defines private real estate. The backbone of unlocking these powerful AI tools is organized data and standardized processes<\/strong>.<\/p>\n As Alex Robinson from Juniper Square put it: \u201cYou can\u2019t automate chaos. If ownership, definitions, access, and change management are broken, AI initiatives fall back into shadow spreadsheets and mistrust. Being AI-ready is less about the tech stack and more about whether the organization can absorb a new way of working<\/strong>.\u201d<\/p>\n This process reinforces the value of adaptability:<\/p>\n With our data is organized, we\u2019re mapping out all the work we do as a firm, understanding which tasks can and will be done by AI and which are better done by people. This includes creating process maps and SOPs for each individual task, from underwriting a new deal, to negotiating a PSA, to preparing a quarterly investor letter, to prepping an asset for a refi or sale.<\/p>\n It quickly becomes clear which tasks will be done by AI.<\/p>\n Take underwriting, for example. The initial BOE can be auto populated using AI tools and data from the T12, rent roll, comps, tax research, and market data. Tools like Shortcut are doing this today. Models that took hours can now be completed in minutes.<\/a><\/p>\n But these tools won\u2019t replace our analyst. Team members should stay flexible about their roles and embrace opportunities to adapt and grow. Think about who you are in a world where AI can do the technical work. No matter where AI goes, there\u2019s always going to be a role for humans.<\/p>\n AI doesn\u2019t replace people. It makes their domain expertise and what I like to call \u201cshoe leather experience\u201d more valuable.<\/p>\n Here\u2019s what I mean.<\/p>\n An AI agent can process every line in a document. It can build spreadsheets, extract data, and summarize information faster than any analyst. That\u2019s table stakes now.<\/p>\n But it can\u2019t answer the questions that actually matter and ultimately drive returns. Take acquisitions: AI can\u2019t build a reputation where unique opportunities come your way. AI doesn\u2019t have the deep domain expertise and experience to read the seller\u2019s motivation, understand why you should be confident in the upside, know why you\u2019re positioned to execute, and recognize why this opportunity fell into your lap in the first place.<\/p>\n AI can\u2019t visit an equity partner and clearly tell the story behind the deal. And the story is where the value lives<\/a>.<\/p>\n AI can pull comp data, summarize rent rolls, and track absorption trends. But it can\u2019t tell you why residents will choose this property over the comps.<\/p>\n You get the point.<\/p>\n![\"\"](\"https:\/\/astudentoftherealestategame.com\/wp-content\/uploads\/2025\/12\/Adaptability-819x1024.jpg\")
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