Your property data isn't AI-ready (Even if you think it is)

Here's a scenario playing out in boardrooms across the country: A retail organization has invested heavily in digitizing its property portfolio. Thousands of documents have been scanned. Hundreds of thousands of photos organized. Spreadsheets tracking everything from maintenance schedules to compliance checklists. The executive team feels confident and proclaims, "We should be using artificial intelligence to analyze our property data.”

But when they attempt to deploy artificial intelligence (AI) for portfolio-wide insights, the results are disappointing. The AI can't identify meaningful patterns. It struggles to compare properties. The promised intelligence never materializes.

What went wrong?

The answer is simpler than most realize: digitized data and AI-ready data are fundamentally different things.

The critical misconception: Digital ≠ AI-ready

Most organizations confuse digitization with AI readiness. It's an easy mistake to make. After all, if your property data exists in digital form like PDFs, drone scans, images, survey findings, shouldn't AI be able to work with it?

Not necessarily.

Think about it this way: Having a library of books doesn't mean you have a searchable database. Having photos of every property doesn't mean you have comparable data. Having spreadsheets from different departments doesn't mean you have connected intelligence.

AI needs more than digital files. It needs standardized, consistent, and structured data that removes subjectivity and enables analysis at scale across an entire portfolio. Without this foundation, even the most sophisticated AI tools will struggle to deliver meaningful insights.

What "AI-ready" property data actually means

AI-ready property data is structured in a way that allows artificial intelligence to analyze, learn from, and generate insights consistently across your entire portfolio. It's data that has been intentionally prepared for machine learning, not just converted from paper to pixels.

The distinction matters because AI systems work by identifying patterns. They need consistent data structures to make valid comparisons. They require standardized taxonomies to understand relationships. They depend on connected information to generate portfolio-level intelligence.

Consider this real-world example that illustrates the problem:

In Year One, a property survey asks: "Is the signage free of any obstructive views?"

• Yes = Positive score

• No = Negative score

In Year Two, the same survey asks: "Is the signage blocked by trees or other obstacles?"

• Yes = Negative score

• No = Positive score

The physical condition hasn't changed. The structural change in the question completely invalidates year-over-year comparisons. An AI system trying to identify the changes to the state of signage visibility across your portfolio will generate meaningless results because the underlying data structure keeps shifting. Teams will have to manually manipulate the data to allow for any comparisons.

This isn't a technical problem; it's a strategic one. And it's exactly why the term "AI-ready" has become so important in property data management.

From large language models to large location models

You're likely familiar with Large Language Models (LLMs) - AI systems like ChatGPT that learn information patterns from being trained on vast amounts of data. The same conceptual approach can transform property management through what we call Large Location Models.

Instead of analyzing text patterns, Large Location Models analyze property patterns across your portfolio. Which locations have the highest maintenance costs? What physical attributes correlate with strong performance? How do layout configurations impact customer behavior? Which properties should be prioritized for refresh investments?

But these models can only be as intelligent as the data they're built on. If your property data is fragmented, inconsistent, or subjective, the AI will amplify those problems rather than solve them.

The Phygii Score: three essential dimensions

Not all property data is created equal. At IDS, we use the Phygii Score framework to measure data quality across three critical dimensions to determine AI readiness:

1. Fidelity: Accuracy and precision

Fidelity measures how accurate and precise your data capture is. High-fidelity data means measurements are reliable, images are clear enough for analysis, and dimensional information enables confident decision-making.

Poor fidelity creates expensive problems downstream. When measurements are unreliable, construction bids include costly contingencies. When images lack the resolution needed for analysis, teams schedule unnecessary site visits. When dimensional information can't support confident decisions, projects get delayed.

The key is right-sizing your capture efforts to organizational needs. Not every property requires millimeter-level precision scanning. Not every application needs photorealistic imagery. The goal is to match fidelity requirements to intended use cases, ensuring your data quality is sufficient for the decisions it needs to support.

2. Completeness: Comprehensive coverage

Completeness ensures all relevant property aspects are captured systematically. It's not enough to document just the front facade or the sales floor. AI-ready data captures building systems, site conditions, brand elements, accessibility features, and operational considerations across every property.

Incomplete data creates blind spots that undermine portfolio-wide intelligence. You might have perfect documentation of exterior signage but no systematic record of parking lot conditions. You might have comprehensive floor plans but no data on mechanical systems. These gaps prevent AI from generating holistic insights.

Think about a Facilities director trying to optimize preventive maintenance budgets. Without complete data across all properties, the AI can't identify which systems are most likely to fail or which locations should be prioritized for investment. The intelligence is only as comprehensive as the underlying data.

3. Currency: Timely and fresh

Currency measures whether your data reflects current conditions. Property data has a shelf life. Buildings change. Equipment ages. Yesterday's accurate data becomes tomorrow's liability when it's no longer current.

An annual refresh is ideal for most property portfolios. Three-year refresh cycles remain valuable. But data that's five or more years old? It's probably creating more risk than insight. AI systems trained on outdated information will generate recommendations based on conditions that no longer exist.

Consider a brand compliance initiative. If your data is three years old, you're making decisions based on signage that's been replaced, layouts that have been reconfigured, and equipment that's been upgraded. The AI might flag problems that have already been fixed while missing new issues that have emerged.

Again, the key is to right-size currency with the decisions that need to be made using the data. Important strategic decisions or those that will have major capital expenditures involved benefit from more current data.

Removing subjectivity: The make-or-break factor for qualitative data

Consistent, AI-ready data requires we eliminate human subjectivity from the data collection processes.

When one evaluator rates signage visibility as "good" while another rates identical conditions as "acceptable," your data becomes unreliable. When survey questions use vague assessments instead of measurable criteria, AI systems can't learn meaningful patterns. When data collection methods vary by property or by inspector, portfolio-wide comparisons become impossible.

The solution is creating comprehensive data collection playbooks that standardize every aspect of capture:

• Visual reference guides showing example photos for each rating level

• Consistent rating scales used identically across all properties and all years

• Locked question phrasing that prohibits changing survey language once established

• Objective criteria replacing vague assessments ("clearance exceeds 6 feet" instead of "good clearance")

These playbooks transform data collection from art to science, creating the consistency that AI systems require. It's not about making the process more rigid, it's about making the intelligence more reliable.

The business impact of AI-ready data

The distinction between digital and AI-ready data isn't academic. It has direct bottom-line implications.

Organizations with truly AI-ready property data are seeing remarkable results:

• Capacity increases from 10 to 300 sites per year

• 6 months faster execution on portfolio-wide initiatives

• 15% revenue lift in properties that achieve brand compliance according to research from McKinsey

These outcomes are impossible when AI systems are working with fragmented, inconsistent, or subjective data. The intelligence that drives these results requires data that has been intentionally structured for machine learning from the ground up.

Think about what AI-ready data enables at the director and manager level:

For Facilities teams, it means predicting which properties need attention before systems fail rather than reacting to problems as they emerge.

For Construction leaders, it means comparing project performance across the portfolio to identify which contractors, approaches, and configurations deliver the best outcomes.

For Brand managers, it means quantifying the revenue impact of compliance and prioritizing investments where they'll generate the highest returns.

For Operations directors, it means understanding which store attributes correlate with performance so you can replicate success across the portfolio.

For Finance teams, it means having confidence that capital deployment decisions are backed by reliable intelligence rather than gut instinct.

These capabilities require AI-ready data. Digital data alone won't get you there.

Taking the First Step

If you're managing property data today—whether it's 50 locations or 5,000—the question isn't whether you should pursue AI readiness. The question is how quickly you can get there.

Organizations that act now are establishing advantages that compound over time. They're building data foundations that enable every future AI initiative. They're creating competitive moats that become increasingly difficult to replicate. They’re enabling their brick-and-mortar locations to thrive alongside the online experience they provide their customers.

The organizations that wait will find themselves playing catch-up in an increasingly data-driven competitive landscape.

Your property data is probably more digital than you realize and less AI-ready than you think. Understanding that distinction is the first step toward building the intelligence that drives measurable business outcomes.

Ready to assess where your property data stands today? Download our comprehensive white paper, "Getting Your Property Data AI-Ready: A Strategic Guide for Multi-Property Retail Leaders," to access our AI Readiness Assessment Tool and learn the complete roadmap from fragmented to intelligent property data.