Introduction: AI is only as good as the data behind it
There’s no shortage of conversation around AI in commercial real estate.
From predictive insights to automated decision-making, AI is often positioned as the next evolution of building operations. But for many teams, the results don’t match the promise.
The reason is simple: AI doesn’t fail because of technology. It fails because of the data.
Before AI can deliver value, operations data needs to be structured, consistent, and connected. Without that foundation, even the most advanced tools produce limited or misleading results. Teams may invest in AI expecting smarter outcomes, but without reliable inputs, they’re left with outputs they can’t fully trust or act on.
That gap is already showing up in the market. Building Engines’ “State of Property Management Technology 2026” research found that while more than 45% of respondents say they have a good understanding of how AI can support property management, only 28% have implemented AI in their building operations.
Why most operations data isn’t AI-ready
In many CRE organizations, data is everywhere. However, it’s usually not usable.
Information lives across:
- Work order systems
- Email threads
- Spreadsheets
- Vendor platforms
- Inspection logs
Even when data exists, it’s often:
- Inconsistent (different formats, naming conventions, or processes)
- Incomplete (missing steps, updates, or outcomes)
- Disconnected (no linkage between related activities like inspections > work orders > vendor actions)
AI relies on patterns. When the data is fragmented or unreliable, those patterns either don’t exist, or worse, point in the wrong direction. This creates a situation where teams may have access to more data than ever, but less confidence in what it actually means or how to use it.
As Aliza Carpio, Senior Director, Technical Product Manager at JLL, puts it, “it’s about the data … data integrity and data quality.” In practice, that means AI readiness depends less on the model itself and more on whether the underlying operational data is complete, current, and connected.
The core elements of AI-ready operations data
For AI to be effective in building operations, data needs to meet a few key criteria:
- Standardization
Processes must be consistent across properties. If every building tracks work orders or inspections differently, AI can’t compare or learn effectively. - Completeness
Workflows need to be fully captured, from start to finish. That includes requests, actions taken, resolution, and follow-up. - Connectivity
Data points need to be linked. For example, connecting an inspection finding to the resulting work order and vendor performance. - Timeliness
Data must reflect what’s happening now, not what happened last week. Delayed or manual updates reduce AI’s ability to provide meaningful insight.
When these elements are in place, data becomes something AI can actually interpret and act on. Without them, AI is working with partial or distorted inputs, which limits its usefulness and can lead to incorrect conclusions.
Why centralization comes first
One of the biggest misconceptions is that AI can be layered onto existing systems without change.
In reality, AI requires a centralized operational environment.
When data lives in one system:
- Workflows are tracked consistently
- Data is captured in real time
- Relationships between actions are preserved
- Reporting becomes reliable
Without centralization, AI becomes an overlay on top of fragmented inputs, which limits its effectiveness. As Carpio explains through her work with Prism AI, the right question is not whether the AI is ready, but whether the customer’s operational data is ready. If core building information is missing or incomplete, teams have to address those gaps first before AI can deliver reliable value.
This aligns with what product leaders are seeing in practice. As Carpio explains, “AI is the how, not the why.” The real focus must remain on solving operational problems and improving outcomes for property teams.
Without that clarity, organizations risk applying AI to disconnected workflows, which limits its ability to generate meaningful insight.
What AI looks like when data is ready
When operations data is structured correctly, AI starts to deliver practical value:
- Identifying recurring issues across properties=
- Highlighting underperforming vendors
- Flagging compliance risks before they escalate
- Recommending prioritization based on impact
These aren’t abstract benefits. They directly impact how teams operate day to day. Instead of reacting to problems after they occur, teams can anticipate issues and address them earlier, improving both efficiency and outcomes across the portfolio.
Getting there: a practical approach
Most teams do not need to implement AI all at once. They need to prepare for it by improving the state of their workflows and data first.
That means focusing on:
- Centralizing core workflows
- Standardizing how work is tracked
- Improving data consistency and completeness
- Reducing reliance on manual or offline processes
This is also where change management matters. As Carpio makes clear, successful AI adoption requires more than the technology itself. Teams need guidance, training, and repeated exposure to practical use cases so they can understand where AI fits into their day-to-day work.
This approach allows teams to build a strong foundation without overcomplicating the process. By focusing on operational improvements first, organizations position themselves to adopt AI in a way that actually delivers value, rather than adding complexity without clear return.
A helpful way to think about this progression is through a phased approach: start by identifying high-friction workflows, then integrate AI into those processes, and finally scale insights across the portfolio. This “start small, prove value, expand” mindset ensures that AI adoption remains grounded in real operational impact rather than theoretical use cases.
This approach also reduces risk, making it easier for teams to build confidence and drive adoption over time.
Closing: better data drives better outcomes
AI has the potential to transform building operations, but only when the foundation is in place.
For CRE teams, the real opportunity isn’t just adopting AI. It’s building the kind of operational environment where AI can actually deliver meaningful, reliable value. When data is structured, connected, and consistent, AI becomes a tool that enhances decision-making rather than complicating it. Or, as Carpio puts it, AI is the “how,” not the “why” — the value comes from solving the right operational problems first.
AI works best when it solves real operational problems. See how CRE teams are using smarter building operations technology to reduce friction, improve visibility and drive better portfolio performance.
