The built environment is entering a machine-first era: According to a recent report by Johnson Controls, 67% of facilities managers are already using AI to improve the operation, utilization, and maintenance of their buildings, while 61% say they plan to implement or expand the use of AI in 2026. With this widespread adoption of AI comes the potential for significant cost savings, efficiency gains, and operational improvements. But unlocking that potential requires the frictionless flow of accurate, timely data—and when it comes to data interoperability, much of the industry still operates like it’s 2006.
Today’s sleek analytics, automation, and reporting tools are often enabled by mountains of manual work. Teams shuttle data between platforms using spreadsheets. Analysts reconcile mismatched field names. Integration specialists manually map one system’s data structure to another’s.
That outdated approach isn’t just time-consuming. It’s expensive: Research across enterprise IT consistently shows that 20-30% of technology spending is consumed by integration and data preparation activities. OSCRE estimates that real estate organizations globally are wasting $50 billion per year on avoidable integration work. That’s just the tip of the iceberg. The true cost extends to lost productivity, missed opportunities, and delayed or misinformed decision-making.
Below are five ways manual data mapping is holding facilities leaders back—and what a machine-first approach makes possible.
1. Facilities Teams Are Distracted by Data Wrangling
Facilities leaders are increasingly expected to demonstrate strategic value by supporting sustainability performance, risk management, portfolio optimization, and tenant experience. But too often, their workday is dominated by something else: preparing the data.
The result is a kind of operational “Excel purgatory,” where highly skilled professionals are consumed by the mechanics of making information usable rather than acting on it. This, in turn, makes it more difficult for FM teams to evolve beyond a reactive operating model. It also reinforces the long-standing perception of facilities as cost centers rather than drivers of business growth.
A machine-first approach changes that dynamic by reducing the need for manual reconciliation and making consistent data interpretation a built-in capability. When systems can exchange and understand data automatically, facilities teams spend less time preparing information and more time using it to access insights that improve performance, reduce risk, and support strategic decision-making.
2. Integration Becomes a Recurring Cost
Integration is often thought of as a one-time IT project. In reality, for most real estate organizations, it’s a permanent operational expense.
Every new acquisition, system upgrade, vendor onboarding, or reporting requirement triggers another cycle of manual reconciliation. Charts of accounts need to be aligned. Data fields need to be normalized. Inconsistencies need to be resolved.
This work depends heavily on expensive human expertise in the form of data analysts, accountants, IT specialists, and operations staff.
Because each integration is typically bespoke, much of that work cannot be reused. When the next portfolio change occurs, the process begins again. As portfolios grow, partners change, and regulatory demands evolve, the burden can compound quickly. Scaling operations requires scaling the people doing the mapping, turning growth into a headcount challenge rather than an efficiency gain.
A machine-first approach changes that equation by making interoperability repeatable and reusable. Instead of rebuilding integrations from scratch, organizations can scale portfolios, onboard vendors, and expand reporting capabilities without continuously adding manual effort.
3. Decision-Making Runs on Stale Data
In a modern operating environment, time-to-insight matters. Manual mapping introduces latency: The more humans are required to reconcile and validate datasets, the longer it takes for information to become usable. Over time, organizations normalize this delay and decisions drift further from real conditions.
That’s not just an operational issue; it’s a strategic limitation. The gap shows up in portfolio performance management, vendor accountability, sustainability reporting, and capital planning. When data isn’t timely and consistent, organizations either wait too long to act or make decisions without the level of confidence they should have.
I recently saw a firm spend three weeks manually reconciling utility data from a newly acquired building just to get it into a portfolio energy dashboard. By the time the manual mapping was finished, the data was already three weeks stale—and the opportunity to use the insight to optimize energy spend had passed.
A machine-first model lowers this latency by reducing reliance on manual transformation and making consistent interpretation a built-in capability, not an after-the-fact exercise.
4. Risk Increases While Compliance Confidence Weakens
Manual mapping also quietly introduces risk. Small upstream changes, such as a renamed field or altered data structure, can cascade into downstream reporting errors undetected. Inconsistent definitions across systems can produce discrepancies that only surface during audits or regulatory reviews.
As compliance requirements expand across energy, emissions, and building performance standards, the potential impact of that risk rises.
Organizations need data pipelines that are not only automated, but also governable and auditable. That means systems must understand the meaning of the data they exchange, not just its format. The goal isn’t “automation at any cost.” It’s automation that is transparent, traceable, and defensible, so compliance reporting and operational metrics don’t depend on fragile processes or informal institutional knowledge.
5. True Automation and AI Readiness Become Unattainable
Real estate organizations hoping to unlock the full value of advanced technology must confront a hard truth: You can’t operate in a machine-first era if your data is still handmade.
Humans are excellent at interpreting messy data. We can recognize that “EnergyUse,” “Energy_Consumption,” and “Total_kWh” might represent the same concept across different systems. Machines can’t, unless the meaning is standardized and machine-readable.
When real-world concepts are represented differently across platforms, automation breaks down. AI tools struggle to interpret the data reliably and workflows stall because human intervention is still required to reconcile differences.
More importantly, trust breaks down. If data mapping is inconsistent or manually defined, organizations cannot fully trust the outputs of AI models or automated workflows. Standardized, machine-readable data is what enables transparency, auditability, and confidence in AI-driven insights, particularly in regulated environments where compliance and reporting accuracy are critical.
This is why AI readiness is not primarily a technology problem. It’s a data infrastructure problem.
Without shared definitions, standardized models and machine-readable context, organizations end up layering sophisticated tools on top of fragile foundations.
How Do We Move on from Manual Mapping?
Whether the goal is to consolidate portfolio reporting, integrate new vendors, support sustainability disclosures, or enable advanced analytics, manual mapping has become the hidden tax on progress.
Manual mapping won’t disappear overnight, but it shouldn’t remain the backbone of how the industry shares and uses data. A modern real estate ecosystem needs interoperability that is repeatable, reliable, and increasingly automated.
This is where initiatives like the OSCRE Smart Data Highway come into play. By establishing shared data standards and machine-readable context across the industry, this initiative represents a collective industry effort to eliminate the need for one-off mapping and enable seamless, scalable data exchange.
Organizations that invest in this shift won’t just operate more efficiently. They’ll be better positioned to scale, manage risk, meet rising reporting demands, and unlock the value of automation and AI.
Richard Reyes is the VP of digital strategy at ConnexFM and the CEO and executive director at OSCRE International. Connect with Richard on LinkedIn here.