Multifamily property operators are intensifying their adoption of artificial intelligence as the sector confronts persistent operational pressures and evolving stakeholder expectations, according to research from Grant Thornton. The accounting and advisory firm's 2026 AI Impact Survey found that half of construction and real estate leaders are already piloting AI in select use cases, while 36% are scaling the technology across multiple functions.

The acceleration follows several years of record construction activity, rising operational costs, and a challenging capital environment that forced owners and property managers into reactive mode. Now that market conditions are stabilising, multifamily leaders are deploying AI to address competing demands from investors seeking transparency and returns, residents expecting personalised service, and property teams managing complex portfolios without additional resources.

Greg Ross, Head of the Construction and Real Estate industry at Grant Thornton LLP, characterised the sector's position: "The companies we work with have spent the last few years just trying to keep up. Now that market conditions are stabilising, multifamily leaders are focusing on upleveling the ways they meet stakeholders' evolving expectations. They're juggling investors who want greater transparency and better returns, residents who expect more personalised service and property teams who are being asked to manage increasingly complex portfolios with the same resources."

Implementation challenges have emerged as vendors rapidly embed AI tools into existing platforms without preparing property managers to govern them across organisations. Most construction and real estate firms purchase rather than build their technology, creating adoption dynamics where property teams deploy tools before underlying processes, data infrastructure, and governance structures are ready to support them.

The governance deficit carries measurable risk. Only 13% of construction and real estate leaders surveyed expressed confidence they could pass an AI governance audit within 90 days—a concern amplified in a sector heavily reliant on third-party technologies deployed across multiple owners and properties. Firms achieving returns on AI investments are those building governance foundations and establishing clear ownership and measurement definitions from the outset, rather than those deploying tools most rapidly.

Early AI adoption concentrated in back-office functions, automating accounting, procurement, and accounts payable activities. Sixty-three percent of construction and real estate leaders report efficiency gains as a primary benefit of AI deployment. However, only 28% cited revenue growth and 36% identified cost reduction as outcomes—a gap prompting multifamily firms to redirect AI investments toward front-office applications with more direct business impact.

Cody Root, Tech Strategy Manager at Grant Thornton LLP, distinguished the evolution: "Firms have been talking about back-office automation for years. AI just makes it more accurate and efficient. Many front-office use cases are genuinely new, with advanced capabilities like agentic automation for leasing, guided AI tours, instant response to leads and generating renewal packages automatically."

Grant Thornton detailed a representative implementation for a national multifamily firm facing increasing demand across its portfolio. The operator required a more scalable approach to managing leasing inquiries, resident communications, renewal activity, and maintenance-related follow-up. The firm deployed an AI-enabled leasing and resident engagement solution designed to improve response times, accelerate lead conversion, streamline renewal communications, and provide more timely resident support.

The implementation delivered higher lead-to-lease conversions and a more consistent renewal experience, allowing site teams to shift from administrative tasks to higher-value resident retention activities. The case illustrates the operational leverage available when AI tools address revenue-adjacent functions rather than purely cost-centre activities.

Technology vendors are now pushing expedited implementations of AI-embedded platforms as multifamily firms pursue front-office opportunities. The vendor-driven adoption cycle creates tension between the pace of tool deployment and the development of organisational capacity to govern, measure, and extract value from artificial intelligence at scale.