The Question is Whether the Data Behind it Can Keep Up
by Blake Adams, SeekNow
Signal 1 // Adoption has reached near-universal levels
According to JLL’s 2025 Global Real Estate Technology Survey of more than 1,500 senior CRE decision-makers across 16 markets, 88% of investors, owners, and landlords are now actively piloting AI, most pursuing five or more use cases simultaneously. That’s not experimentation anymore; that’s a race.
Signal 2 // Adoption is outpacing results
Here’s what the headline numbers don’t show: widespread adoption hasn’t produced widespread impact. Only 5% of companies report achieving all their AI program goals. More than 60% remain strategically, organizationally, and technically unprepared to scale beyond pilots.
Signal 3 // Data quality has become the defining constraint
The PEX Report 2025/26, based on a survey of more than 200 business transformation professionals, found that over half cite data quality and availability as the single biggest barrier to AI adoption, ranking ahead of talent gaps, regulatory concerns, and change resistance. A separate 2025 Qlik survey of 500 AI practitioners found that 81% say their company still has significant data quality problems, yet 85% believe leadership isn’t adequately addressing them.
In real estate, this problem takes a specific shape. Property condition, damage, readiness, and risk are captured across thousands of homes, often in inconsistent formats, with varying levels of detail and unstructured field inputs. When that variability enters the system, it carries through into every downstream decision AI is asked to support.
As AI becomes more embedded in portfolio operations, from capex planning to claim triage to asset valuation, standardized inspections, repeatable data structures, and reliable field visibility aren’t operational details. They’re the foundation for determining whether AI-driven decisions are trustworthy.
The firms pulling ahead aren’t just investing in better models. They’re investing in better inputs.
