What It Takes to Operationalize AI in Real Estate

Real estate has long been a relationship-driven industry, built on expertise, market knowledge, and trusted human judgment. But that is beginning to change.

Real estate has traditionally relied on market expertise, local knowledge, and relationship-driven decision-making. But as the industry becomes increasingly data-intensive, artificial intelligence is beginning to reshape how organizations analyze opportunities, serve clients, manage assets, and operate at scale.

While many companies are still exploring AI’s potential, some have moved beyond experimentation and are integrating AI into everyday workflows across the enterprise. That shift raises important questions for technology and business leaders alike: What does it take to operationalize AI at scale? How do organizations move beyond pilots? And how can AI enhance human expertise without diminishing the value clients place on personal relationships and professional judgment?

Few leaders have had a front-row seat to that transformation like Yao Morin, Chief Technology Officer at JLL Technologies. In this conversation, she discusses operationalizing AI at scale, empowering employees to become innovators, and building a future where technology enhances human expertise rather than replacing it.

Building the foundation for AI in real estate

As AI adoption accelerates across the industry, technology leaders are increasingly recognizing that long-term success depends less on deploying the latest tools and more on creating the infrastructure, processes, and organizational alignment needed to support them. The challenge is not simply implementing AI, but building an environment where it can deliver value consistently and at scale.

Without strong data governance, clear ownership, and accessible platforms, even the most promising AI initiatives can struggle to move beyond the pilot stage.

For Yao, that work began long before AI became a boardroom priority.

Enterprise AI adoption often stalls after the pilot phase. What operational or governance mechanisms helped you move from experimentation to organization-wide AI integration?

Morin: Our ability to move beyond experimentation was rooted in building for scale from the outset. Our shared platform approach through JLL Falcon and alignment across our business units gave us a consistent way to deploy AI into real workflows and products. Rather than creating disconnected solutions across the organization.

But infrastructure alone doesn’t drive adoption. A core part of how we scaled was activating a citizen-driven AI model by empowering our employees at every level to use and apply AI solutions in their daily work. Through communities of practice led by our central AI team, employees could exchange skills, prompts and use cases organically across the organization.

We also built self-service capabilities directly into JLL GPT. So that employees without technical backgrounds could create their own custom assistants. Today, more than 900 custom assistants have been created by employees who identified gaps in their own workflows and developed purpose-built tools to address them. That bottom-up innovation engine, paired with our top-down platform strategy, is what helped AI move from enterprise infrastructure into everyday work.

Our Global Real Estate Tech Survey reinforces why this matters. While AI adoption is accelerating with nearly 90 percent of CRE companies testing AI, many organizations still struggle to move beyond pilots because legacy infrastructure and weak data foundations create barriers.

For us, the difference was treating AI as enterprise infrastructure from the start. And then ensuring our people had the tools and community to build with it.

What were the biggest technical and cultural challenges in moving from experimental AI deployments to a platform that employees rely on daily?

Morin: The journey to grow JLL GPT into a platform that thousands of employees rely on daily meant solving for enterprise reality. And not just pilot success. That required building something reliable, secure, and scalable for use across a global organization.

Equally important was ensuring it was directly embedded into the products, tools, and workflows our teams already use. AI adoption accelerates significantly when there are clear use cases for how the tool will transform the way work gets done. Hence, we’ve focused on creating transparency and showcasing how JLL GPT is solving the everyday challenges across our organization.

As these use cases multiply, our employees have become our AI champions, encouraging adoption within their own teams. These AI champions are the catalyst for driving meaningful change and building trust. When someone sees a peer using AI to solve a common challenge, they’re more likely to experiment with it on their own. Giving people the space to learn from each other and to see AI applied to their own work was one of the most important things we did

The key to sustained adoption was ensuring employees had clear guardrails and understood where AI could create real value in their work. Equally important was building confidence that human judgment remains at the heart of every decision.

Once people see the practical benefit for themselves and can use AI confidently in their normal workflows, adoption flows naturally.

Enterprise adoption and workforce transformation

Many organizations measure AI success through productivity gains. How do you evaluate whether efficiency improvements are translating into meaningful business outcomes?

Morin: We measure AI ROI by looking at tangible business outcomes, not just time saved.

A few examples show how this plays out in practice.

Our teams are cutting RFP response times from four days to under two hours, which directly improves our competitiveness in winning new business.

One AI-driven HR project delivered $2.4 million in savings while reducing cost-per-hire by 42%. And perhaps most telling, JLL saw a 15 percent year-over-year increase in Q4 2025 transaction volume without adding new broker headcount, indicating our teams are handling growth more effectively. And this is because AI is absorbing work that would have otherwise required hiring additional staff.

Nearly 30,000 employees have already been trained on JLL’s AI tools. What does effective AI upskilling look like in a global enterprise? And how do you ensure training leads to sustained usage?

Morin: We’ve made training a core part of our AI strategy. This is because successful enterprise adoption depends on both enablement and innovation. We have been very intentional about making training not just about how to use AI, but why employees should use it, what’s in it for them, and how it can support both internal and client-facing work.

We reinforce that through multiple learning pathways. There are self-paced courses, role-specific resources, interactive webinars, hands-on workshops, and internal channels for sharing best practices and use cases.

Likewise, our annual hackathon is another important part of that model. It gives employees hands-on opportunities to apply AI to real business challenges and helps build confidence well beyond the technology organization.

Currently, more than 41% of our addressable population uses our proprietary AI tools daily. For us, that is the clearest sign that training leads to sustained usage. The key is making the training practical, accessible, and directly connected to real work processes.

As AI automates, how do you think roles will change within large service organizations? All without disrupting the human expertise that clients value?

Morin: We firmly believe technology, like AI, is an enhancement and not a replacement for our people, who remain our greatest asset.

As AI takes on more repetitive and time-intensive tasks, roles will increasingly shift toward higher-value work. We’re thinking proactively about what that means for our people. Which is why we are investing in upskilling and supporting career mobility as work evolves. In practice, that means less time spent producing inputs and more time spent advising clients, interpreting information, and applying judgment in context.

In a service business, that human expertise is still the value clients are paying for. The opportunity with AI is to create more space for that work, not less.

Business impact and industry transformation

What lessons from that deployment can be applied to other enterprise functions?

Morin: The biggest lesson is that AI is only as good as the data behind it.

At JLL, we invested early in building the architecture needed to connect decades of proprietary market intelligence, property data, and transaction histories. That foundation is what made it possible to deploy AI in ways that are genuinely useful, not just technically impressive. Today, our teams can access more than 20 years of comparable sales data without relying on time-consuming manual processes. And this changes both the speed and quality of work.

From there, the question becomes how to integrate AI in a way that fits naturally into the function, rather than adding another disconnected tool. And that applies across the enterprise – whether the goal is improving client delivery, streamlining operations or making internal teams more effective.

Where do you think AI will have the most transformative impact across the real estate value chain in the next five years?

Morin: I am especially focused on the shift from AI that supports decisions to AI that can help move work forward. Agentic AI is a compelling part of that evolution.

Because it creates the potential to act within defined guardrails across parts of the real estate lifecycle that are still slowed down by fragmented information, manual processes, and complex coordination. That is very much in line with where we are headed as a business. Our goal is to use data and AI to turn insights into action – faster and more effectively. This aligns with Accelerate 2030, our multi-year strategy designed to advance JLL’s competitive position and drive value creation.

Through our citizen-driven AI model, we’re ensuring agentic capabilities are not limited to a small group of specialists. In fact, more than 900 custom assistants have already been created across the organization. These solutions were built by employees to address the workflow gaps and challenges they encountered firsthand. And we expect their adoption to continue growing over time.

Leadership vision

You’ve been leading your company’s AI and digital transformation efforts. What initially drew you to the challenge of applying advanced technologies to the real estate industry?

Morin: What drew me to JLL was the opportunity to help build something foundational at scale. It was also a moment when property tech had the potential to fundamentally change how the commercial real estate industry operates.

The real estate industry is highly complex and fragmented. And this makes it an exciting space for innovation if you can get the data and technology strategy right. I’ve always been motivated to use technology to solve meaningful problems. And commercial real estate offered a chance to do that in an industry where decisions can have a very real impact for clients and across the built environment.

After leading the software engineering and data teams at StubHub, joining JLL as its first Chief Data Officer felt like a chance to build a modern, data-driven foundation for the business and shape how the industry evolves.

As a Chief Technology Officer, I now channel innovation directly into strategic business value. The foundation we built as a data organization is manifesting in exciting ways as we transform how we work and serve our clients. I continue to be inspired by what we’re building together.

How would you describe your broader vision for AI and digital innovation at JLL?

Morin: Over the past decade, JLL has built a powerful competitive advantage on data and AI. Our vision is clear.

AI is not a separate initiative; it is central to how we operate, innovate, and create value for clients. Our AI strategy is built around two core objectives. The first is to leverage GenAI products and purpose-built agents to increase internal productivity. While the second is to drive deeper client insights and better outcomes.

Our foundational AI platform, JLL Falcon, sits at the center of that strategy. It brings together proprietary real estate data and advanced AI capabilities to deliver value for both employees and clients.

Today, that strategy is already operating at scale. Over 50,000 employees are actively using JLL GPT today. Meanwhile, JLL Falcon is powering the deployment of more than 40 applications across the enterprise.

The next step is to integrate AI more deeply into the way we work across the business. That will help us transform data into meaningful insights and create greater value for our clients.

Mentorship/Advice

If you had to give advice to future tech leaders, what would that be?

Morin: My advice boils down to three things:

Build on data before you build on AI: The most important investments I have made weren’t in models or tools. They were in the data foundations that make those tools work. If your data is fragmented, incomplete, or untrusted, no AI strategy will save you. Getting that architecture right early is what separates organizations that can scale AI from those that cannot.

Lead with business problems, not technology: The leaders who create the most impact aren’t the ones who chase every new capability; they’re the ones who stay relentlessly focused on what their organization and clients need. Technology is only transformative when it’s connected to a real outcome someone cares about.

Invest in your people as much as your platforms: The biggest unlock for us wasn’t a technical decision: it was empowering employees at every level to build, experiment and innovate with AI themselves. When you democratize access and build a culture of hands-on learning, you get compounding returns that no top-down rollout can replicate.

Above all, stay curious and stay humble. The pace of change in AI is extraordinary. The leaders who will thrive are the ones who keep learning alongside their teams, aren’t afraid to ask questions and recognize that today’s best practices may need to evolve significantly tomorrow.

Key takeaway

Yao Morin’s message is clear: organizations do not become AI leaders by deploying the latest models. Instead, they become AI leaders by building the right foundations. Throughout JLL’s transformation journey, three principles stand out: invest in trusted big data before AI, focus relentlessly on solving real business problems, and empower employees to become active participants in innovation.

At its core, AI transformation is not just a technology challenge; it’s a leadership challenge. The real factor is not simply adopting AI, but creating a culture where people and technology work together to drive lasting impact. Those who fail to build these foundations risk remaining stuck in pilot mode as more agile competitors redefine their industries.