The results of BOMA Canada’s new Trends in Commercial Real Estate survey show adoption of artificial intelligence (AI) is happening, but it’s a cautious, fragmented and inconsistent process.

The survey, conducted over two weeks in September 2025, garnered 35 responses from a range of real estate-related sectors: property managers; asset managers; technology/innovation consultants; property owners; consultants; facility operators; landlord leasing; directors of technical services; sustainability and environmental, social and governance; and real estate referral agencies.

Portfolio sizes ranged from organizations managing under one million square feet to those overseeing more than 50 million square feet. Respondents were heavily represented by firms holding office portfolios (94 per cent), followed by light industrial (60 per cent), multiresidential (51 per cent) and open-air retail (46 per cent). 

The report – launched through BOMA Canada’s AI4CRE initiative, which is designed to help property owners and managers understand, adopt and benefit from AI-driven solutions – was created to provide practical insights to support smarter decisions across the industry.

Many buildings lack proper infrastructure

Ten per cent of respondents had full AI implementation across their portfolios while 17 per cent had partial implementation, 20 per cent had pilot programs in select properties, 33 per cent were in the early planning stages and 20 per cent had no implementation.

While AI promises big advantages in terms of optimizing the performance of buildings and building management, and many teams are ambitious about its use, the reality is that many buildings simply don’t have the infrastructure in place.

When respondents were asked to quantify their AI utilization, the average portfolio had AI-powered solutions in just 23 per cent of its buildings.

This lack of technology carried through to respondents’ confidence in their portfolio’s technological infrastructure for supporting AI. Just three per cent felt “fully prepared,” while 17 per cent felt “well-prepared,” 60 per cent felt “moderately” or “somewhat” prepared, and 20 per cent admitted they were “not prepared.” 

That suggests significant infrastructure investment will be necessary before AI can reach its full potential. Investments in Internet of Things sensors, cloud connectivity and modern building systems must precede or accompany AI implementation.

Energy management, optimization is top use

Despite the low implementation of infrastructure-reliant enabling technologies, AI has gained meaningful traction in several areas. Energy management and optimization led the way at 70 per cent, which was unsurprising given its clear return on investment.

About half the companies were using AI for property management work flows, while less than 40 per cent were using it for tenant experience applications. Fewer than 30 per cent were using it for predictive maintenance and occupancy analytics, and security and access control.

The top barriers to AI adoption were: 

• a lack of internal expertise (63 per cent);
• unclear return on investment (52 per cent);
• integration challenges with existing systems (44 per cent);
• resistance to change within organizations (33 per cent);
• a lack of compelling commercial real estate-specific AI solutions (33 per cent); and
• data and privacy concerns (26 per cent).

For organizations that have implemented AI solutions, the survey data presents a pattern of modest impact rather than transformative change – so far. The most positive impacts were seen in energy efficiency improvements, which aligns with the high adoption rate of energy management AI solutions.

AI has had a modest impact so far

The dominant response was that AI was having “no” or “minimal” impact on: increased property value; operating cost reduction; enhanced tenant satisfaction; competitive advantage; and improved maintenance efficiency.

Despite these mixed results from current implementations, the industry still shows some willingness to invest in AI technologies over the next two years — albeit at modest levels.

Thirty-two per cent planned no investment, while 46 per cent planned investing less than $500,000, 14 per cent planned investing between $500,000 and $2 million, and four per cent planned investing more than $5 million.

This concentration in the under-$500,000 range suggests organizations are taking measured, experimental approaches rather than betting big on AI transformation.

Despite financial constraints, more than 70 per cent of respondents were interested in implementing smart building automation, advanced analytics, predictive maintenance, and sustainability monitoring and reporting. These processes carry a promise of long-term value through lower operating costs, improved reporting, automation and streamlining, and competitive advantage.

Incremental expectations for AI

Fifty-two per cent of respondents expected AI’s impact to be “incremental,” in that it will improve efficiency but won’t revolutionize the industry. Twenty-six per cent believed it will be a “game-changer,” while 15 per cent were uncertain and seven per cent think it’s “overhyped” and won’t have a major impact on the industry.

Respondents expect AI to most significantly impact sustainability, operations and valuations — areas where data-driven optimization offers clear value.

When asked about AI applications they’re most interested in implementing in the next two years, these four tied at 71 per cent: smart building automation; advanced analytics and reporting; predictive maintenance; and sustainability monitoring and reporting.

Factors needed to accelerate AI adoption

When asked what would accelerate AI adoption, respondents pointed to several critical enablers:

• a clear road map for integration with existing platforms (74 per cent);
• more clearly defined industry-specific AI solutions (70 per cent);
• a clearer understanding of the value proposition through case studies (67 per cent);
• better education and training on AI in commercial real estate (56 per cent); and
• cost reductions in AI technology (41 per cent).

Early adopters have a critical role to play in sharing their knowledge to help other organizations overcome hesitancy in committing resources to AI adoption.

Training programs, industry education initiatives and knowledge-sharing will also be essential for accelerating adoption.