Link Logistics on Building the Data Foundation AI Actually Needs – Podcast Recap

April 27, 2026

In today’s rapidly evolving real estate landscape, the buzz around artificial intelligence and data-driven insights is louder than ever. But how are leading firms actually leveraging these technologies to gain a competitive edge? In this post, we’ll explore the fascinating approaches of Link Logistics, part of Blackstone, and their commitment to transforming their operations through data and AI. Drawing on insights from conversations with Matt Rand, Head of Research and Analytics, and Matt Ostrower, CFO, we’ll unpack what it takes to embed data at the core of decision-making, how organizational culture shapes AI adoption, and what the future holds for commercial real estate (CRE) Data Maturity.

Watch and listen to the full episode on Spotify below.

The Data Journey Begins: Building a Foundation for AI Success

One of the key takeaways from Link Logistics is that successful AI implementation starts with a solid data infrastructure. Matt Rand emphasizes the importance of first “eating your spinach”—that is, developing and understanding your data before jumping into advanced AI applications.At Link, this process involved over 70 projects under the umbrella of “Project Infinity,” aimed at cleaning, standardizing, and making data accessible across the organization. Through these efforts, they created a landscape where data points are consistently defined and reliably visualized, using tools like Power BI for initial insights.This foundation is critical because, as Rand notes,

“data is process, and process is humans,”

highlighting that technology alone isn’t enough. Organizational workflows, data quality, and culture are vital ingredients for enabling AI to thrive.

From Dashboards to Dessert: How AI Changes the Way We Work

Once a robust data culture is established, AI becomes the “dessert,” a powerful, yet sometimes intimidating, tool for reinvention. According to Ostrower, AI’s role is evolving rapidly, from automating routine tasks to reshaping entire processes.For example:

Building dashboards dynamically on the fly, moving away from static visualizations.
Automating complex, end-to-end workflows in weeks rather than months.
Extracting deeper insights and making better decisions with less resource expenditure.

This shift requires not just technical readiness but also cultural adaptation. The firm encourages grassroots adoption—where individuals learn to develop their own tools—while also rethinking top-down strategies to embed AI into core processes.

“The AI journey starts with developing your data,”

Ostrower emphasizes.

“Once that’s in place, AI can truly be dessert.”

The Organizational Culture Pillar: Leadership and Change Management

Implementing data and AI at scale mandates strong leadership commitment—something rare at the CFO level. Ostrower states,

“This may be the first time a CFO is deeply involved in data conversations,”

and predicts it won’t be the last.For successful adoption:

Leadership must understand that AI-driven insights are not meant to replace but to empower decision-makers.
Change management principles—like explaining why, how, and what to expect—are crucial.
Building curiosity and a culture of experimentation helps overcome resistance and enables widespread use.

Rand adds,

“It’s about creating a culture where curiosity is encouraged, and data is seen as a strategic asset.”

Leveraging Scale and Data for Competitive Advantage

Link’s large-scale operation—over 400 million square feet of assets—provides a unique advantage. Blackstone’s support grants access to vast data resources and the capacity to invest in sophisticated AI tools.Rand notes,

“Scale allows us to buy expensive data and develop models that cover entire markets,”

which creates strong competitive moats.The firm uses its models to:

Price assets accurately—”how much should this space rent for today and in the future.”
Support strategic decisions, from acquisitions to lease negotiations.
Detect signals early in markets, giving their team a crucial edge.

This approach aligns with Blackstone’s broader vision:

“Data is our future, and scale amplifies our ability to use it.”

Practical Applications: Real-World Use Cases of AI at Link

Property Valuation and Market InsightsUsing the platform called

Olinda (short for “Optimized Learning for Industrial Assets”), Link can estimate rental prices and asset values with accuracy within 20%. While not an automatic decision-maker, it serves as “an objective voice in the room,” augmenting human judgment. Olinda incorporates numerous data sources:

Lease agreements
Geographic and building features
Operational expense data
External sources like open-source building data and supply chain insights

This multi-layered modeling supports various decisions:

Prioritizing assets for due diligence
Monitoring market trends
Simulating future rent trajectories

Speeding Up TransactionsBy integrating AI into workflows, Link aims to reduce transaction timelines significantly—from six months to three or even one month—by automating processes and generating rapid insights.

“AI compresses timelines, increases liquidity, and creates a flywheel of data growth,”

says Ostrower. This cycle accelerates market activity and decision speed, key advantages in competitive CRE.

Lessons for Other Firms: Starting Your Data and AI Journey

For firms early in their CRE data maturity curve, Rand and Ostrower recommend starting with fundamentals:

Develop an enterprise-wide glossary of key terms and definitions.
Assess data quality and identify gaps (“Eat your spinach”).
Invest in organizing and standardizing data before jumping to AI.

Rand sums it nicely:

“Start with your data house in order, then layer AI on top.”

Without high-quality data, AI outputs become garbage, underscoring the importance of a disciplined, phased approach.

The Road Ahead: Creating a Data-Driven Future in CRE

The consensus from Link is clear: “We are just at the beginning, it’s a nerd decade.” As AI becomes more integrated into the fabric of CRE, expect to see faster decisions, increased transaction volume, and more sophisticated insights.Rand and Ostrower emphasize that success hinges on:

Organizational culture embracing data.
Leadership commitment, especially at the CFO level.
Investing in scalable, high-quality data infrastructure.

In short, those who harness data and AI effectively will be the ultimate winners in the next era of commercial real estate.

Final Thought: Embrace the Data Revolution

The journey to a fully data-driven CRE operation is complex, requiring patience, discipline, and a cultural shift. However, the rewards—faster decisions, better investments, and a sustainable competitive advantage—are worth the effort.Are you ready to start your data journey? Remember: it all begins with understanding what data you have and making sure it’s good.

Want to learn more about how AI is transforming real estate?Check out the full conversation with Matt Rand and Matt Ostrower here, or explore related insights on our blog.

FAQ: Demystifying AI and Data in CRE

What is the first step for a CRE firm to adopt AI successfully?Understanding and organizing your current data is critical. Build a clear data glossary and assess data quality before deploying AI tools.

How does scale benefit a real estate company’s AI initiatives?

With more assets and data points, you can develop more accurate models, buy better data, and generate insights that cover entire markets, creating a decisive competitive advantage.

Can AI replace human judgment in CRE?

Not entirely. AI serves as an objective voice that augments, not replaces, human expertise. It helps decision-makers focus on strategic thinking and complex problem-solving.

What are common challenges firms face when implementing AI in CRE?

Data quality, organizational resistance, and change management are key hurdles. Successful firms address these through disciplined processes and cultural buy-in.

How long does it typically take for a CRE firm to see ROI from AI investments?

It varies, but the most successful organizations see meaningful results within 1-3 years, starting with foundational data work and gradually expanding AI applications.