Artificial intelligence is beginning to reshape commercial real estate, not through headline-grabbing smart buildings, but by automating the back-office processes that underpin valuations, underwriting, leasing and day-to-day operations.
As financing costs rise, margins tighten and transaction volumes remain subdued, property owners, lenders and operators are under increasing pressure to find structural efficiencies. AI is emerging as a practical tool to compress timelines, reduce manual workload and standardise decision-making across the real estate lifecycle.
Industry estimates suggest the impact could be significant. Morgan Stanley has projected that AI could automate around 37% of tasks across commercial real estate, unlocking up to $34bn in efficiency gains by 2030. Rather than replacing core roles, the technology is being deployed to remove friction from processes that have traditionally relied on spreadsheets, email chains and manual data reconciliation.
Faster valuations, underwriting and due diligence
Valuation, underwriting and due diligence are among the first areas to see meaningful AI adoption. These functions have long been labour-intensive, requiring teams to analyse transaction data, market comparables, zoning rules and macroeconomic indicators under tight deadlines.
AI models are now able to ingest and synthesise these inputs in real time, producing dynamic valuations that update as market conditions shift. According to JLL, AI-driven valuation tools can incorporate signals such as local economic activity, mobility patterns and supply constraints, allowing investors and lenders to respond more quickly to pricing changes and emerging risks.
Similar advances are taking place in underwriting. PwC and the Urban Land Institute have highlighted the growing use of machine-learning systems to automate document ingestion, risk scoring and scenario modelling, reducing friction in deal execution and shortening transaction cycles.
This trend is extending into private credit and non-bank lending, where AI platforms are being used to assess collateral quality, borrower risk and neighbourhood dynamics at greater speed and scale.
Leasing, marketing and ownership models evolve
AI is also changing how properties are marketed, discovered and leased. Static listings and one-size-fits-all marketing are giving way to more adaptive, data-driven approaches that tailor pricing, presentation and recommendations to specific tenant segments.
AI-generated listings can adjust descriptions, imagery and pricing guidance automatically, reducing manual work for brokers while improving conversion rates. Virtual tours powered by computer vision and generative AI are enabling prospective tenants and buyers to explore properties remotely, expanding reach and reducing time on market for commercial and multifamily/Build to Rent assets.
Beyond leasing, AI is beginning to influence ownership and capital structures. In combination with blockchain technology, AI is being used to support tokenised and fractional ownership models, helping manage continuous valuation, compliance and liquidity at scale. These emerging structures depend on automation to function efficiently, particularly where assets are held by a large number of investors.
As AI becomes embedded in core workflows, risk management has come into sharper focus. Firms such as JLL have cautioned that data quality, model transparency and cybersecurity must be addressed as AI systems increasingly influence pricing decisions, leasing strategies and capital allocation.
From automation to execution: AI enters property operations
Looking ahead, AI’s role in real estate is expanding from analysis and recommendation to execution. A recent example comes from Aldar, one of the Middle East’s largest property developers, which has partnered with Visa to pilot voice-enabled, agentic payment technology.
The initiative allows AI agents to initiate and complete transactions through natural language commands, signalling how AI could move beyond supporting workflows to actively managing them.
In a property context, this points to a future where AI systems coordinate rent collection, vendor payments and operational approvals directly within property management and finance platforms. For large operators managing complex residential, commercial and mixed-use portfolios, this model suggests a shift towards more autonomous property operations.
Over time, AI agents could orchestrate financial and operational workflows across accounting, payments and asset management systems, reducing manual intervention while accelerating execution and decision-making at scale.
A structural shift, not a short-term trend
Taken together, these developments suggest that AI adoption in commercial real estate is no longer experimental. It is becoming a structural response to economic pressure, operational complexity and the need for greater consistency across portfolios.
While human judgement remains critical — particularly in areas such as investment strategy, asset selection and relationship management – AI is increasingly handling the repetitive, rules-based tasks that slow organisations down.
For owners, operators and lenders, the competitive advantage is shifting towards those able to embed AI responsibly into their core workflows, turning efficiency gains into durable operational resilience.
