NAREIM's annual Data & Information Management Meeting in Chicago opened with attendees choosing from a dozen topics for discussion during the morning Windy City Chat.
Overwhelmingly, the room gravitated toward artificial intelligence, dedicating the entire hour to the topic before revisiting it in subsequent sessions throughout the day.
The overarching takeaway? Data and IT professionals within real estate investment management firms are grappling with how to leverage generative AI to deliver measurable business value while controlling costs, managing risk and encouraging employees to embrace new ways of working.
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“There’s AI for the masses, which is a general productivity tool, and then there is AI to help turbocharge the real estate investment management business. It can help you raise capital. It can help you deploy capital,” attendees heard.
Adoption: Turning Enthusiasm Into Action
Throughout the meeting, discussions repeatedly touched on the question of how to translate AI experimentation into productivity gains and competitive advantage.
Attendees described applications ranging from investor reporting to acquisitions analysis.
One firm uses an AI agent to process 400 to 500 broker emails daily, extracting investment opportunities, building a comps database and tracing deal correspondence. Another firm was described as using AI to streamline customized investor reporting.
“I do a lot of investor templates; AI helped me create a system, a template to create templates. Each investor wants something different and this helps us give them what they want more efficiently.”
Despite growing enthusiasm, adoption remains uneven.
“People get excited, but then we find sometimes the usage of the subscriptions we are buying for them is zero,” a participant said.
To sustain engagement, firms are responding with AI 101 sessions, departmental demonstrations and peer-learning cohorts, emphasizing regular opportunities to share experiences and applications.
“We have split people up into Claude cohorts and meet every two weeks for ‘show and tell,’” an attendee shared.
Exposing the True Cost of Bad Data
For data management professionals, AI has created an unexpected opportunity to secure executive support for longstanding data governance priorities.
“AI has triggered our C-suite to take data governance more seriously.”
“The carrot of AI productivity and efficiency helps data teams get the buy-in from the rest of the business that they have long wanted to get their firms’ data cleaned up.”
Poor data quality is not a new problem, but AI makes its consequences more visible, and potentially more expensive.
“AI did not create data quality problems; AI simply started charging us for them,” an attendee shared. “Humans used to just absorb the mental cost of bad data quality.”
Shadow IT: The Risks of Unchecked AI Use
Participants also discussed shadow IT, including unauthorized AI applications and spreadsheets stored on individual employees’ computers.
“What are the hidden spreadsheets that are only on someone’s C drive? Have them show you those,” a member noted.
The proliferation of AI tools has heightened compliance concerns, particularly when employees upload proprietary information or use AI-generated tools and skills without adequate safeguards.
“AI is a reputational risk. At the end of the day, we want to be trusted by our investors.”
AI Slop: Who’s Accountable?
Beyond the risks of unauthorized AI use, attendees raised concerns about the quality and accountability of AI-generated work.
As AI-generated content proliferates, firms face another challenge: ensuring employees review, understand and take responsibility for the work they produce.
One attendee defined AI slop as “garbage that no one reviewed.”
“Just like with a junior employee, we have to make the managers responsible,” the participant said.
The problem extends beyond junior employees.
Attendees described senior executives circulating “20-page” AI-generated memoranda, leaving middle managers and analysts to spend hours fact-checking.
Another source of AI slop: “New hires who don’t understand the business yet or know how to do their jobs.”
Participants also questioned whether AI could undermine the development of junior professionals who traditionally learn through repetitive analysis and detailed review.
“Managers also need to be cognizant about how AI usage is stunting the growth, learning and career development of their analysts.”
Participants also highlighted how AI usage differs across generations and career stages.
At one firm, interns exhausted their monthly token allocation in a single day, frustrating the tech team but also introducing vice presidents to new applications.
Meanwhile, overworked analysts often lack time to experiment and resent being asked to do so, attendees shared.
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