From Concept to Competitive Advantage

TRIA’s Emma Pina (COO & Associate Principal) recently attended a roundtable discussion, “AI in CRE,” hosted by the Building Owners and Managers Association of Boston (BOMA Boston). Our key takeaways:

The implementation of artificial intelligence (AI) in commercial real estate is no longer speculative or experimental; it’s a fully realized shift, actively reshaping how buildings are operated, designed, and experienced. From owners to architects, every player in the CRE ecosystem now faces the same reality: AI isn’t just a tool, it’s a strategy.

Across portfolios, AI is being used to enhance decision-making, streamline operations, and unlock value from data that until recently was siloed or underutilized. Whether it’s optimizing energy consumption in office towers, forecasting lease renewals, informing smarter site selection, or designing a space, AI-driven insights are pushing the industry toward greater efficiency and intelligence.

For building owners, architects, and the ecosystem the question is no longer how to use AI but rather how do we implement it across our process. The endgame for early adopters is clear: faster, more accurate decisions; cost reductions; and increased returns on investment. In other words, those who move early gain a distinct competitive advantage.

Preparing for AI integration requires more than just purchasing software or hiring consultants. It starts with understanding what problems AI can solve within your specific portfolio. Clear goals must be defined, current performance benchmarks established, and existing workflows examined for compatibility. As one panelist in a recent industry roundtable put it, AI works best when it’s embedded seamlessly into business processes—not when it’s bolted on as an afterthought.

Central to this is data. Without accurate, well-structured, centralized data, even the most powerful AI platforms will fall short. The value of AI is only as strong as the data it pulls from. Companies must prioritize data normalization, creating consistency across their systems to ensure AI outputs are reliable, actionable, and trustworthy.

While owners and operators focus on systems and strategy, architects occupy a critical space in the AI transformation. We are not only expected to understand and adopt emerging AI tools for our own workflows, but we also influence how technology is integrated into the buildings themselves. Architects are active participants in this evolution, and we have a responsibility to stay informed and lead conversations about how AI can enhance smart building operations, tenant engagement, and long-term sustainability.

In practice, AI is already reshaping design itself. Data gathered from sensors, occupancy analytics, and user behavior feedback helps architects rethink how space is planned, utilized, and experienced. We’re designing differently—more responsively, more dynamically—because we’re no longer guessing how a space will be used, we’re learning from it. Air quality, daylight access, thermal comfort, and acoustics are no longer abstract design considerations; they’re measurable, data-informed variables that can be continuously optimized.

But successful AI deployment goes deeper than choosing the right tools. As several experts in our roundtable discussion (Aaron Franczyk, BrainBox AI/Trane; Dave Miller, Leading Edge Design Group; Timothy Shaw, Metropolis; Jeff Thompson, JLL) emphasized, there’s a science to getting it right. First, define the specific business outcomes AI should support. Then, integrate AI into processes in a way that complements (not complicates) existing workflows. Most importantly, ensure that everyone in the organization can understand and use AI outputs. AI should simplify decision-making, not obscure it.

Panelists also emphasized that AI security is a critical issue. As AI adoption increases, so do the risks associated with data management, especially when data is fed into third-party or non-proprietary platforms. This raises a vital need for companies to develop internal AI governance policies. Clear boundaries must be established around how client data is handled; who has access; and how information is stored, shared, and protected. The future of AI in real estate will likely involve proprietary platforms, secure environments, and tokenized access systems that provide both innovation and data control.

At the executive level, one persistent question remains: Is it working? To answer this, businesses must establish baseline metrics before deployment of KPI. Only with a clear understanding of current performance can they measure the true impact of AI, whether that’s reduced energy costs, higher occupancy rates, or faster lease turnarounds. Without this, AI risks becoming another buzzword rather than a business enabler.

Ultimately, as AI continues to integrate into the daily workflows of commercial real estate, from design studios to asset management teams, its impact will be transformative. But it won’t be automatic. Success depends on thoughtful deployment, secure infrastructure, and a shared commitment to using data ethically and intelligently.

Yes, challenges remain especially around data fragmentation and access, but they’re solvable. And for those willing to lead rather than follow, the benefits of AI adoption are real: better buildings, more efficient operations, higher margins, and a stronger, more responsive relationship with tenants. The AI era in CRE is here; now it’s about how well we adapt.

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Since 2015, TRIA has been at the forefront of designing the human experience toward a better future, creating environments that inspire, engage, and elevate communities. From science and innovation to corporate workplace, multifamily, hospitality, mixed-use, and master planning, TRIA partners with organizations nationwide to create meaningful, enduring spaces that drive progress and transformation.