If you work in corporate real estate (CRE), you’ve probably noticed something dramatic happening over the last three years: suddenly, everyone is running an AI pilot.
Not “exploring AI.”
Not “thinking about AI.”
Actually running pilots.
According to JLL’s 2025 Global Real Estate Technology Survey, AI pilots have jumped from 5% of CRE teams to a staggering 92% in just a few years. That’s nearly the entire industry sprinting into the future. (JLL Report)
But the headline has a twist.
Despite all this activity — despite all the demos, all the experiments, all the “we’re testing a new AI tool” conversations — only 5% of companies say they’ve actually achieved their AI program goals.
Let that sink in: 92% adoption. 5% success.
Something is clearly broken. And if you’re feeling this gap inside your organization… you’re not alone.
Let’s break down what’s really happening — and how CRE teams can finally move from AI hype to AI impact, with a little help from companies like Dextra Labs.
AI in CRE: From Curiosity to Mandate
The JLL survey (spanning 1,000+ real estate leaders across 16 major markets) makes one thing crystal clear: AI is no longer an innovative toy. It’s now a competitive necessity.
Some firms are genuinely excited about AI. Others? They’re rolling out pilots because the C-suite insists.
And that’s where the cracks start to show.
The report bluntly states that many CRE teams aren’t adopting AI because they’re ready — but because leadership sees other firms doing it and doesn’t want to fall behind. This leads to rushed pilots, unclear goals, and fragmented tech stacks.
The result? Lots of activity and very little transformation.
Also Read: From Copilots to AI Co-Workers: How Enterprises Are Orchestrating Multi-Agent Workflows
If Everyone Is Piloting AI… Why Isn’t It Working?
Let’s talk about the elephant in the room.
Real estate is not a “move-fast-and-break-things” industry. It’s an operational, risk-sensitive, compliance-heavy environment. So when firms adopt AI the same way they adopt trendy software tools, the results feel… underwhelming.
Here are the biggest reasons CRE teams are struggling, according to JLL and industry experts:
1. Most organizations don’t have the data infrastructure AI requires.
It’s tempting to think that AI can magically fix messy systems.
But the truth? AI amplifies whatever foundation you already have.
Clean, live, connected data → great results
Scattered spreadsheets, inconsistent reporting, legacy systems → faster bad decisions
As one expert bluntly said: “AI can’t fix bad data. It just makes bad decisions faster.”
2. Many teams are trying to leapfrog digital maturity.
JLL warns of a harsh reality: AI isn’t leveling the playing field — it’s widening the gap.
Companies with strong systems are accelerating ahead.
Companies with outdated tech are falling even further behind.
Why? Because AI requires more than good intentions — it requires:
- structured data
- mapped workflows
- system integrations
- continuous validation
- governance
You can’t simply drop AI into a legacy environment and expect magic.
3. CRE teams are choosing high-impact use cases… but skipping foundational steps.
Here’s an interesting twist: unlike many industries, CRE teams aren’t focusing on “easy” GenAI tasks (chatbots, email drafting). They’re targeting portfolio optimization, energy efficiency, and operational workflows — areas with measurable business impact.
Which is good! But these use cases also require:
- real-time data
- integration across systems
- deep understanding of building operations
- trustworthy AI outputs
Without that, pilots stay stuck in experiment-mode.
4. And let’s be honest — AI still feels a little intimidating.
- Data teams feel stretched.
- Ops teams feel overwhelmed.
- Leasing and property managers feel skeptical.
- Executives wanted results yesterday.
It’s no wonder so many AI pilots struggle to scale.
Also Read: Agentic AI vs Copilots: When to Move from Assistance to Autonomous Execution
So… How Do We Fix This?
If there’s one message JLL stresses, it’s this:
You cannot treat AI as a series of disconnected experiments. You must treat it as infrastructure.
And that’s exactly where Dextralabs comes in.
Dextralabs: Turning CRE AI Pilots into Real ROI
Here’s the thing most CRE teams haven’t realized yet:
- AI isn’t about tools. It’s about systems.
- AI isn’t about pilots. It’s about pipelines.
- AI isn’t about demos. It’s about decisions.
Dextralabs helps CRE organizations make that shift, moving from “we’re testing ChatGPT for lease abstraction” to “we’re building a real intelligence layer across our portfolio.”
Here’s what that looks like:
1. Dextralabs builds the knowledge infrastructure CRE teams lack.
Most CRE organizations have tons of data — but it’s scattered across:
- BMS systems
- spreadsheets
- PDFs
- historical reports
- consultant decks
- energy dashboards
- lease documents
- emails
- siloed tools
Dextralabs:
- gathers
- cleans
- structures
- indexes
- and unifies
all of this into a vectorized knowledge engine — a searchable semantic memory built specifically for your CRE operations.
That means your AI isn’t guessing. It’s reasoning with your actual business logic.
2. Dextralabs embeds this intelligence into real workflows.
Unlike generic chatbots, Dextralabs connects AI directly into:
- portfolio reporting
- energy management
- facilities ops
- lease analysis
- asset forecasting
- space planning
- occupancy modeling
So instead of teams asking ChatGPT random questions, AI becomes part of the daily operating system. This is the difference between AI as a toy and AI as a tool.
3. Dextralabs brings governance, accuracy, and trust.
One of the biggest fears CRE leaders have?
Hallucinations.
Dextralabs solves this with:
- business rule engines
- versioned knowledge
- source validation
- retrieval-augmented generation
- role-specific models
Your AI doesn’t just answer questions — it answers them correctly, consistently, and in compliance with your portfolio strategy.
4. Dextralabs helps CRE teams hit both levers of ROI:
- Efficiency: Automating tedious work, reducing manual reporting, eliminating data cleanup.
- Effectiveness: Better decisions, faster insights, more accurate forecasting, smarter optimization.
Most companies only get the first lever. Dextralabs activates both.
Also Read: Is There an AI Bubble? How to Build Durable Enterprise Value with Governance and Measurable Outcomes
A Future Where CRE AI Actually Works
Imagine this:
- Energy usage drops 20–30% because AI is fine-tuning HVAC continuously.
- Lease decisions take hours, not weeks.
- Portfolio scenarios update in real time.
- ESG reporting becomes 80% automated.
- Every building, every asset, every data point is part of a connected intelligence layer.
- Your teams talk to AI the way they’d talk to a colleague — one who remembers everything.
This is not sci-fi. This is what CRE leaders are building right now with Dextralabs. The future of CRE isn’t about running more AI pilots. It’s about graduating from pilots — into scaled intelligence systems that pay off.
Also Read: Build an AI System That Actually Raises Revenue
The Bottom Line
The CRE industry has embraced AI faster than almost any sector. But adoption without strategy isn’t transformation. Pilots without infrastructure aren’t progressing.
JLL’s message is clear: AI success requires system-level thinking, clean data foundations, workflow integration, and organizational readiness.
Dextralabs’ message is even clearer: You don’t need 20 tools. You need one intelligence engine that thinks with your organization.
So if you’re tired of pilots that never scale…
If you want AI that improves decisions, not dashboards…
If you want ROI, not experimentation…
Then the next move is obvious: Build the foundation, build the memory, and build the system. Dextralabs can help.
