Commercial real estate leaders are racing to implement artificial intelligence, but here's the thing—many are building on shaky foundations. According to JLL’s recent Global Tech Survey, while 57% of CRE organizations are upgrading their digital infrastructure to prepare for AI integrations, only 40% have actually taken the time to thoroughly audit their existing data systems. This disconnect reveals a gap that could derail even the most promising AI initiatives.
The harsh reality? Most AI projects fail not because the technology itself is inadequate, but because the underlying data is unstructured, inconsistent, or incomplete. Without proper data foundations, organizations find themselves with sophisticated tools that simply can't deliver meaningful insights. It's kind of like trying to build a skyscraper on quicksand.
The Hidden Cost of Scattered Data
This almost certainly sounds familiar: lease records sit in one system, capital projects in another, and energy operations data lives somewhere else entirely. When executives ask strategic questions like "Which assets should we prioritize to improve energy efficiency?", getting accurate answers becomes nearly impossible. Why? Because data is disconnected across multiple systems.
This fragmentation is more than an inconvenience—it undermines decision-making and limits the potential return on technology investments. Leaders making decisions with incomplete data are essentially flying blind, relying on gut instincts rather than evidence-based insights. Not exactly a recipe for success in today's data-driven world.
What Makes a Data Platform Essential
A well-designed data platform serves as the engine of smart decision making in CRE. It converts raw, unstructured data into information and insights that are easy to understand. The architecture operates on three critical layers: data governance that establishes trust and accuracy standards, a data factory that ingests and processes information from various sources, and data products that deliver insights through dashboards, analytics, and AI tools.
The governance layer ensures data quality and sets rules for how information is validated and used. This becomes especially crucial when deploying generative AI, where data accuracy is key to reliable outputs.. “Garbage in, garbage out” as the saying goes.
Real-World Applications Drive Results
Organizations implementing robust data platforms see real improvements across the business. Take this example: a Fortune 500 company set standard rules to benchmark costs by site. They were able to use data from data from workplace management systems, sensors, and space bookings to identify underused assets and better allocate spaces. The results? Significant cost savings and better space utilization.
The AI Advantage Hidden in Plain Sight
Here's something interesting—CRE leaders consistently underestimate AI's value in solving data system problems. Recent survey data shows a persistent 6-12% gap between expected and actual AI benefits across three key areas: consolidating technology stacks, opening new data sources, and uncovering fresh insights. This suggests that properly implemented AI can deliver more value than anticipated. But (and this is important) only when built on solid data foundations.
Making the Business Case
Data platforms have a clear ROI, making the business case for them straightforward. Done right, data platforms will reduce manual work, speed up reporting timelines, avoid regulatory penalties, and improve decision making speed. The key lies in connecting technology capabilities directly to business priorities and measuring the impact over time.
One other key? Keeping your team involved. Identifying project champions and ensuring user comfort with new tools is just as important as the technical details of implementing the new data platform.
Your Next Strategic Move
Don't start your technology journey with the fancy AI tool without the right approach behind it. Start with the structure that makes it all work—your data platform. When data is properly governed, standardized, and packaged, the rest of your technology can perform as promised. In modern real estate, data is no longer a byproduct of operations—it drives smarter portfolio strategy, better experiences, and stronger financial outcomes.
