Everyone Has AI.
But Are They Seeing Results?

From Pilot to Production: An Actionable Framework to Deploy AI in CRE

Based on conversations with our 300+ clients, we outline why most AI pilots don’t take off and the four-stage framework for technologists to deliver trustworthy results at scale.

THE PROBLEM

The AI Pilot Plateau

Only 5%

of CRE organizations report achieving most of their AI program goals—despite 88% of firms having launched AI pilots

Source: JLL October 2025 Global Real Estate Technology Survey

Weak Data Foundations Are Unscalable

The bottleneck isn’t budget, ambition, or access to models. It’s the data foundation underneath them.

Fragmented Data

Deal, asset and property data is scattered across emails, spreadsheets, and tools.

No Workflow Integration

AI is used tactically: one-off queries and outputs that die in privately owned spreadsheets.

No Path to Scale

Pilots see mild success and slight efficiency gains, but leave no scalable footprint or room to refine.

THE FRAMEWORK

The AI Maturity Model for CRE

Where are you today? What does your firm need to move to the next stage?

STAGE 1

Exploration

Experimenting with general-purpose AI or point solutions for specific tasks. Results evaluated case-by-case. One-off experiments don’t inform firmwide workflows.

STAGE 2

Centralization

Deal data moves into a centralized platform with structured fields, consistent taxonomies, and searchable history. This data foundation is where institutional memory begins to compound.

STAGE 3

Integration

AI begins to accelerate work across the deal lifecycle by reducing time spent on manual tasks, surfacing relevant context automatically, and helping teams operate with greater insight.

STAGE 4

Deployed at Scale

AI informs every decision throughout the deal lifecycle. Outputs flow into the firm’s proprietary database to bolster their competitive advantage.

When teams can trace every output back to verified data, they’re treated like professional analysis.

BUILDING THE FOUNDATION

Data Infrastructure Comes Before AI Can Deliver Results

100%

of firms surveyed cite fragmented data as the #1 AI roadblock.

Source: Dealpath AI Readiness Survey

Top Criteria For Building An AI-Ready Database

An AI-ready database acts as infrastructure across all your AI analysis.

Standardized Data Schema

IRR, cap rates, hold periods, and financing terms captured in consistent fields for easy comparison

Centralized Historical Data

Searchable record of past deals, passed opportunities, and market comps

Clean Naming Conventions

Every value follows the same format so deals can be filtered, grouped, and compared

Structured Relationships

Deals linked to properties, comps, contacts, models, and documents so AI sees the full context

A structured data model can save your firm a significant amount of token usage, making AI deployment easier and cost-effective.

HOW TO TAKE ACTION

Seven Steps to Break Through

Audit your data gaps
Standardize field structures and naming conventions
Centralize your active pipeline and historical data in one platform
Structure relationships between deals, contacts, and properties
Standardize field structures and naming conventions
Connect third-party market data sources
Train your team on how to unlock stronger decision making with AI

THE ADVANTAGE

Thoughtfully Deploy AI.
 Build Your Competitive Edge.

Building the data, processes, and fluency now gives your firm a compounding advantage.

More, higher quality deals pursued

Faster investment velocity

Greater analytical rigor

Optimal capital allocation

93%

of executives in institutional real estate believe early AI adopters will gain a competitive edge — and the window to be “early” is closing.

Source: Dealpath AI Readiness Survey

Break Through the Pilot Plateau. Drive Real Results With AI.

See how leading investment firms use Dealpath to build the data foundation that makes AI work — and turn pilot results into enterprise-wide competitive advantage.

Everyone Has AI.
But Are They Seeing Results?