{"id":51635,"date":"2026-01-27T10:17:48","date_gmt":"2026-01-27T10:17:48","guid":{"rendered":"https:\/\/www.yardi.co.uk\/?p=18321"},"modified":"2026-03-10T15:26:19","modified_gmt":"2026-03-10T15:26:19","slug":"ai-readiness-for-build-to-rent-why-2026-is-the-year-to-act","status":"publish","type":"post","link":"https:\/\/www.yardi.com\/blog\/en-gb\/ai-readiness-for-build-to-rent-why-2026-is-the-year-to-act\/","title":{"rendered":"AI Readiness for Build to Rent: Why 2026 Is the Year to Act"},"content":{"rendered":"
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The residential rental sector is no longer asking whether artificial intelligence will have an impact; it is now asking how. The pressing question for founders, managing directors and CEOs is when AI becomes a defining competitive factor. Across build to rent (BTR), single-family housing (SFH) and co-living portfolios, the answer is increasingly clear \u2013 2026 will be the year AI adoption consolidates from experimentation into an operational standard. According to PwC, AI could contribute up to $15.7 trillion<\/a> (approximately \u00a311.64 trillion) to the global economy by 2030, with real estate among the sectors poised for significant productivity gains. As a result, timing and preparedness are critical.<\/p>\n

Furthermore, our recent report with SAY found that 46% of real estate leaders<\/a> believe AI will complement rather than replace their role, while 42%<\/a> express no concern at all. This confidence creates a clear divide \u2013 organisations that act now will enter the next phase with scalable platforms, trusted data and confident leadership teams, while those that delay risk falling behind competitors already using AI-driven insights to protect margins, strengthen investor confidence and deliver superior resident experiences.<\/p>\n

From Reactive to Proactive Processes: Why AI Readiness Matters<\/h2>\n

AI readiness is no longer a technical consideration \u2013 it is a board-level strategic priority. While AI adoption across real estate and adjacent industries is accelerating, with 50% having implemented AI<\/a> tools and a further 65% using AI tools for daily tasks, research highlights a significant gap between experimentation and value creation. A 2025 Boston Consulting Group study found that only 5% of companies<\/a> globally are achieving measurable returns from AI, with success strongly correlated to leadership ownership, data quality and enterprise-wide integration.<\/p>\n

For BTR, SFH and co-living operators, this gap has direct implications for decision-making quality, risk oversight and investor confidence. Stakeholders increasingly expect real-time visibility into portfolio performance, forward-looking insights and transparent, consistent reporting. Without clean, connected and governed data, AI cannot reliably support forecasting, scenario modelling, or asset optimisation \u2013 limiting its strategic value. <\/p>\n

As operational complexity rises and margins tighten, AI readiness is enabling leadership teams to shift from reactive oversight to proactive control. This shift is already reflected in industry sentiment. The SAY report found that 31% of real estate businesses<\/a> believe AI will have a significant impact on data-driven decision-making over the next three years, while 91%<\/a> expect it to increase the ease and efficiency of data analytics, and a further 96%<\/a> believe it could significantly improve day-to-day task efficiency. Therefore, organisations that prioritise AI readiness at a board level are better positioned to scale, respond to market volatility and demonstrate operational maturity to investors and lenders.<\/p>\n

Use Cases That Matter Most to CEOs & MDs<\/h2>\n

In practice, AI readiness delivers measurable value across three key dimensions:<\/p>\n

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  1. Operational efficiency<\/strong> \u2013 Automated maintenance triage prioritises issues by urgency and cost impact. AI-powered leasing assistants accelerate enquiry handling and improve conversion. Finance teams benefit from faster closes and streamlined reporting, reducing reliance on spreadsheets and manual reconciliations.<\/li>\n
  2. Financial & strategic decision-making<\/strong> \u2013 Real-time portfolio insights give leaders consistent visibility across BTR, SFH and co-living assets, enabling better forecasting of repairs, renewals and arrears \u2013 particularly valuable in volatile interest rate environments.<\/li>\n
  3. Resident experience<\/strong> \u2013 AI supports personalised communication, faster service responses and predictive maintenance that reduces downtime, enhancing retention and long-term value creation.<\/li>\n<\/ol>\n

    As Neal Gemassmer, vice president & GM international at Yardi, explains:<\/p>\n

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    AI is increasingly shaping how organisations operate across three key areas. First, it is being embedded directly into core property management platforms to automate and optimise critical business processes. Second, there is growing focus on agentic AI \u2013 using orchestration layers that allow teams to build, manage and scale AI agents that address inefficiencies across existing workflows. Third, organisations are connecting large volumes of enterprise data from their management platform to large language models through secure connectors. Together, these approaches allow businesses to adopt AI in a practical, governed way while learning how to apply it most effectively over time.<\/em><\/p>\n

    The Technology Foundation AI Depends On<\/h2>\n<\/blockquote>\n

    According to Morgan Stanley Research<\/a>, AI could automate 37% of tasks in real estate, unlocking $34 billion (approximately \u00a325.22 billion) in operating efficiencies. However, this potential remains largely unrealised. Fragmented systems and a pronounced skills gap across leasing, property operations, finance and resident engagement continue to undermine data accuracy by 41%<\/a>, while a further 31%<\/a> of organisations struggle to access the data they need \u2013 slowing decision-making and limiting impact. True AI readiness isn\u2019t about deploying isolated point solutions, but about building an integrated technology foundation that enables portfolio-level insight, operational consistency and long-term asset performance, supported by capabilities such as:<\/p>\n