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 – 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 (approximately £11.64 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.
Furthermore, our recent report with SAY found that 46% of real estate leaders believe AI will complement rather than replace their role, while 42% express no concern at all. This confidence creates a clear divide – 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.
From Reactive to Proactive Processes: Why AI Readiness Matters
AI readiness is no longer a technical consideration – it is a board-level strategic priority. While AI adoption across real estate and adjacent industries is accelerating, with 50% having implemented AI 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 globally are achieving measurable returns from AI, with success strongly correlated to leadership ownership, data quality and enterprise-wide integration.
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 – limiting its strategic value.
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 believe AI will have a significant impact on data-driven decision-making over the next three years, while 91% expect it to increase the ease and efficiency of data analytics, and a further 96% 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.
Use Cases That Matter Most to CEOs & MDs
In practice, AI readiness delivers measurable value across three key dimensions:
- Operational efficiency – 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.
- Financial & strategic decision-making – Real-time portfolio insights give leaders consistent visibility across BTR, SFH and co-living assets, enabling better forecasting of repairs, renewals and arrears – particularly valuable in volatile interest rate environments.
- Resident experience – AI supports personalised communication, faster service responses and predictive maintenance that reduces downtime, enhancing retention and long-term value creation.
As Neal Gemassmer, vice president & GM international at Yardi, explains:
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 – 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.
The Technology Foundation AI Depends On
According to Morgan Stanley Research, AI could automate 37% of tasks in real estate, unlocking $34 billion (approximately £25.22 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%, while a further 31% of organisations struggle to access the data they need – slowing decision-making and limiting impact. True AI readiness isn’t 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:
- Unified resident, leasing & financial data
A single, governed source of truth that connects resident profiles, leasing activity, rent, arrears and operating costs, ensures AI insights reflect the full resident lifecycle across the portfolio. - End-to-end platform integration
Connected systems break down silos and enable continuous data flow across asset management, property operations, finance and investor reporting, creating the conditions AI needs to operate effectively. - Standardised, automated processes at scale
Digitised workflows for leasing approvals, maintenance triage, rent collection, renewals and month-end close reduce manual effort while producing consistent, high-quality data across assets. - Built-in forecasting & scenario modelling
Integrated analytics allow operators to model cash flow, occupancy, arrears and maintenance demand, providing the historical depth and structure AI requires to deliver predictive insights. - Operational readiness for AI adoption
Teams equipped with intelligent tools, supported by governance, training and leadership alignment, ensure AI is applied appropriately while minimising risk in day-to-day decision-making.
A Practical AI Readiness Roadmap for Leaders
To translate these capabilities into action, BTR leaders can follow a phased roadmap:
- Assess data health – Identify fragmentation, duplication and manual data handling across systems and portfolios.
- Consolidate systems – Establish a single source of truth across finance, operations, leasing and asset management.
- Standardise governance & access – Implement role-based permissions, workflow controls and auditability to support compliance and trust in insights.
- Identify high-impact automation opportunities – Prioritise processes with high manual effort and clear rules, such as maintenance workflows, approvals and reporting.
- Build predictive capabilities incrementally – Start with forecasting, maintenance and risk indicators that directly support operational and financial decision-making.
- Upskill teams & embed adoption – Support change through training, clear ownership and alignment between business and technology teams.
- Activate low-risk, high-value AI initiatives – Launch targeted use cases that deliver measurable outcomes and build organisational confidence ahead of wider rollout.
The Cost of Not Acting in 2026
Delaying AI readiness carries tangible costs – organisations that are slow to adopt AI face higher operating costs, slower response times and growing competitive disadvantage. According to the Forbes Technology Council, AI-enabled predictive maintenance alone can reduce overall operating expenditure by up to 15%. While a TechRadar analysis warns that businesses postponing AI integration risk losing millions annually through inefficiencies and missed optimisation opportunities.
Within real estate, the risk is amplified. Similar to the Boston Consulting Group study, JLL’s 2025 survey also found that nearly 90% of real estate firms are piloting AI. Yet, only a small fraction have successfully scaled it – primarily due to fragmented data and insufficient infrastructure readiness.
However, platforms such as Yardi’s residential suite, with advanced Chat IQ and Yardi’s Data Connect, which centralise data into a single source of truth, illustrate how integration can help unlock AI’s full potential. By unifying financial modelling, operational workflows, and investor reporting, and by implementing a secure system architecture within a single ecosystem, BTR operators can create a reliable foundation for AI-driven forecasting, scenario analysis, and performance optimisation.
AI is not like a magic wand. It’s not that we currently work in a very inefficient way and AI will make everything better. It is a process of hard work and it is a process of a lot of preparation. When it comes to what you need to do? It comes down to the quality of data – if the quality of the data is not the way you want to have it, AI will not fix that for you.
– Neal Gemassmer, vice president & GM of international at Yardi
See how Yardi’s BTR solution can help you prepare for the future & centralise your residential operations.