Hi Reader,
Summer is here! I hope that everyone is enjoying the blooms!
I greatly enjoyed my time at Realcomm this year where so many of our conversations centered around data. In the context of AI tools, of course, but the data discussions are where it got real. The constant tension persisted with software companies offered solutions for organizing data, while owners shared about the very real and expensive challenges of quality data.
In processing these conversations, I kept coming back to the lessons we can learn from Napoleon, Romeo and Juliet, and Henry VIII. So, I decided to write about how these historical figures remind us that ambition (in the form of an Empire) without logistics rarely ends well, communication failures can be costly (i.e. deadly for the young lovebirds), and blaming the messenger (or the XX chromosome human) rarely solves the underlying problem.
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It was a pleasure to see many of you at Realcomm, ULI, and CREW DC events this month. Thanks for sharing what you liked and what you want more of in future newsletters - I heard you loud and clear.
Your Data Problem is Not a Data Problem
Many of our AI implementation strategy conversations with real estate firms today begin with a discussion about a shiny new AI tool. A leadership team has recently seen a demonstration. The software is polished, the outputs are impressive, and the possibilities appear endless. Naturally, the next question is how that tool can be deployed within their organization.
That is usually when we begin talking about data. The reaction is often revealing. We get everything from blank stares to eye rolls to knowing smiles. Those initial responses are often a good indicator of where an organization is in its AI journey. There is no wrong answer at that stage. There is only reality.
Because regardless of the response, and perhaps especially for the eye rollers, quality data remain fundamental to quality AI outcomes.
The same tension appeared repeatedly at Realcomm this year. Software providers presented increasingly sophisticated technology solutions designed to address data challenges. Real estate organizations openly discussed familiar challenges around data quality, ownership, governance, trust, and accessibility.
Despite years of investment in software platforms, reporting tools, and data warehouses, many organizations continue to struggle with the same issues they faced a decade ago. Reports conflict. Definitions vary. Teams do not trust the numbers. Leaders spend valuable time debating whose spreadsheet is correct instead of what actions to take.
The problem is rarely the technology. The problem is usually people, process, accountability, and communication.
As I listened to these conversations at Realcomm, I found myself thinking about several historical figures whose stories continue to offer lessons for modern organizations. While the technology may have changed. Human behavior has not.
Napoleon: Grand Ambitions, Poor Logistics
Few historical figures embodied ambition quite like Napoleon Bonaparte. He reshaped Europe, built an empire, and pursued increasingly larger objectives with extraordinary confidence. Yet history does not remember him solely for his victories. It also remembers the disastrous Russian campaign that ultimately contributed to his downfall.
Many readers are familiar with Charles Minard's famous flow map of Napoleon’s 1812 invasion of Russia. The graphic begins with an army of 422,000 soldiers marching into Russia. By the time the surviving troops retreat, the line has shrunk dramatically. Fewer than 10,000 soldiers remain. He failed to heed the warning of “Winter is Coming.”
The visualization hangs on the wall in my office because it elegantly captures a lesson that remains relevant to leaders today. Vision is not execution. Ambition is not logistics. Declaring an objective does not create the capability required to achieve it.
Organizations are approaching AI initiatives with a similar level of ambition. They announce bold transformation plans, invest in new technology, lay off human staff, establish innovation committees, launch pilot programs, and describe future operating models built around greater efficiency, improved decision-making, and enhanced productivity.
Then reality arrives.
The third-party property management company uses one definition of occupancy. Fund accounting uses another. Historical records are incomplete. Information is stored across multiple systems. Critical data exist in spreadsheets maintained by individuals who may or may not still work for the organization. Key datasets lack clear ownership, and confidence in the outputs varies depending on who produced them.
The further the organization advances along its AI journey, the more these gaps become visible. The AI tools are not the problem; they are the messenger.
What initially appears to be a technology initiative quickly becomes an exercise in governance, accountability, and organizational discipline. Technology can accelerate performance, but it can also accelerate the discovery of dysfunction.
Organizations that skip the foundational work of data ownership, governance, and standardization often discover that their AI strategy resembles Napoleon's Russian campaign: impressive ambition undermined by inadequate logistics.
Romeo and Juliet: Comminication Failures
If Napoleon's challenge was execution, Romeo and Juliet's challenge was communication.
The tragedy at the center of Shakespeare's story was not caused by a lack of effort or a lack of information. It was caused by information failing to reach the people who needed it most. Assumptions filled the gaps, misunderstandings accumulated, and decisions were made using incomplete information.
Many organizations experience similar challenges with their teams and data.
Leasing teams maintain information that asset management needs. Property operations collect metrics that accounting rarely sees. Sustainability teams gather information that remains disconnected from investment decisions. Research teams generate insights that never reach the professionals responsible for execution.
The problem is not a lack of information. The problem is that information does not move effectively throughout the organization. Multiple versions of reality emerge, reports conflict, and teams spend valuable time reconciling numbers rather than making decisions.
Most organizations already possess technology capable of sharing information. The challenge is organizational alignment. Without common definitions, clear ownership, and established processes, technology simply enables silos to operate more efficiently.
Data silos are often communication silos wearing a technology disguise.
Henry VIII: Scapegoating
When things went wrong in Henry VIII's court, there was rarely a shortage of people available to blame. Advisors were replaced. Alliances shifted. Wives were beheaded. New people were put in place with the promise of delivering better outcomes than those that came before.
Modern organizations often respond to data challenges in much the same way.
A software platform is implemented with the promise of creating a single source of truth. When confidence in the results fails to materialize, attention shifts toward another solution. New software is rolled out. Reports are reorganized. Dashboards are redesigned. Data warehouses are rebuilt.
Each initiative arrives with genuine optimism and the expectation that this effort will finally solve the organization's long-standing challenges. Yet the same frustrations continue to surface.
The reason is straightforward. Technology does not create accountability. It does not establish governance. It does not require teams to agree on definitions. It does not determine who owns a dataset, who maintains it, or how conflicts are resolved.
Those are leadership decisions.
When ownership is unclear, quality suffers. When governance is absent, inconsistency grows. When accountability is weak, trust erodes. Eventually these issues appear in reporting, forecasting, investment decisions, and operational performance.
The software becomes the visible target because it is easier to replace a platform than it is to address organizational behavior.
As firms increase their investment in AI, this distinction becomes even more important. Many leaders hope that increasingly sophisticated technology will compensate for weak operating practices. In reality, advanced technology tends to magnify weaknesses that already exist. Problems that were once hidden inside spreadsheets and manual workflows are brought into the light of day.
Today’s organizations making meaningful progress are not those chasing every new technology release. They are the organizations willing to address the less glamorous work of defining ownership, establishing standards, and creating accountability.
Healthy Data Ecosystems Require Leadership
The real estate organizations creating value with AI are not necessarily those with the most sophisticated technology platforms.
More often, they are the organizations that have invested in building healthy data ecosystems. They understand who owns the data, where it resides, how it is maintained, and which standards govern its use. Most importantly, they verify it and trust it.
The conversation around AI tools frequently centers on models, algorithms, and technology. Those discussions matter, but they are not the starting point.
The starting point is leadership. Leadership establishes accountability, creates governance structures, and aligns teams around common definitions and objectives. Leadership determines whether information moves effectively throughout an organization or remains trapped in silos.
The technology tools are ready.
The question is whether the organization is.