Only 5% of CRE Firms Achieve AI Goals by 2025

Just 5% of Commercial Real Estate Companies Have Achieved Their AI Goals: Analyzing the Gap Between Ambition and Reality

Despite a rapid surge in artificial intelligence (AI) adoption within the commercial real estate (CRE) sector, only 5% of companies have fully achieved their AI program goals as of 2025, according to recent industry surveys. This stark contrast between widespread AI experimentation and limited success reveals the significant challenges that CRE companies face in translating AI investments into meaningful business outcomes.

The Surge in AI Adoption Across CRE

The commercial real estate industry, historically cautious about technology adoption, has undergone a remarkable transformation in AI integration over the past two years. A 2025 JLL survey of over 1,500 senior CRE decision-makers found that 88% of investors, owners, and landlords are currently piloting AI, a dramatic rise from just 5% two years ago. Moreover, corporate real estate occupiers report even higher adoption rates, with over 90% running an average of five simultaneous AI use cases.

This rapid adoption reflects a strategic shift within CRE companies from viewing AI primarily as a tool for operational efficiency to leveraging it as a driver of business growth and revenue generation. The top AI objectives now focus on competitive positioning, market intelligence, asset optimization, and tenant experience enhancement rather than just cost reduction.

Why Are So Few CRE Companies Achieving Their AI Goals?

Despite the enthusiasm and investment, only 5% of CRE companies report full success in meeting their AI program objectives, while nearly half achieve only partial progress on two to three goals. Several factors contribute to this gap:

Strategic and Organizational Readiness Issues: Over 60% of CRE firms acknowledge they are not fully prepared—strategically, organizationally, or technically—to scale AI beyond pilot projects. Many lack clear governance structures, integration plans, and alignment between AI initiatives and broader business strategies.
Complexity of Commercial Real Estate Data and Workflows: CRE involves multifaceted lease agreements, tenant relationships, regulatory compliance, and property management complexities that complicate AI implementation. Agentic AI technologies that automate workflows and optimize asset management are still emerging and require significant customization.
Technology Integration Challenges: Embedding AI into industry-specific applications such as property management systems or tenant engagement platforms demands advanced capabilities. Only 22% of AI functionalities are currently embedded in such tailored applications, limiting full automation potential.
Talent and Capability Gaps: Building and maintaining AI models require specialized skills. Many CRE firms struggle to recruit or train personnel capable of managing AI projects effectively, slowing progress from pilot to production.
Economic and Market Uncertainties: The volatile real estate market environment in 2025 also causes firms to be cautious in committing fully to AI-driven transformations, impacting investment scale and pace.

How AI Is Transforming CRE Business Models

Despite the challenges, AI promises to fundamentally reshape CRE business models. Advanced "Agentic AI" systems are enabling firms to:

Perform data-driven investment modeling, assessing returns, risk-adjusted yields, and market trends to inform asset acquisition, holding, or disposal decisions.
Manage smart buildings through integration with IoT sensors and building management systems to optimize energy use, maintenance, tenant satisfaction, and operational efficiency.
Automate back-office functions like maintenance requests, tenant screening, rent collection, and occupancy optimization, improving margins and service offerings.

This evolution positions CRE companies less as passive landlords and more as strategic asset allocators and service platforms managing portfolios with AI-augmented insights and automation.

Industry Outlook and Moving Forward

The CRE sector stands at a critical inflection point regarding AI adoption. With nearly all companies increasing technology budgets to fund AI initiatives, the widening gap between AI leaders and laggards may determine future competitive positioning. Firms that can overcome organizational and technical hurdles to scale AI use cases effectively are likely to reap significant advantages in market intelligence, operational performance, and tenant engagement.

As Yao Morin, JLL's Chief Technology Officer, noted, the rapid adoption rate itself is "quite surprising" given the industry's traditionally slow technology uptake, signaling a paradigm shift in CRE's approach to innovation. However, achieving tangible AI benefits will require clear strategic focus, investment in talent, and robust integration across CRE business functions.

Visuals to Complement This Report

JLL’s AI Adoption Survey Infographic showing the growth of AI pilot programs in CRE.
Agentic AI system dashboards used for real estate portfolio management.
Photos of smart commercial buildings integrating IoT and AI technologies.
Portrait of Yao Morin, JLL CTO, to highlight expert commentary.
Graphs illustrating the gap between AI adoption rates and goal achievement in CRE.

The commercial real estate industry's AI journey is accelerating, but success remains limited to a small fraction of firms. The challenge now is to transform AI experimentation into scalable, value-generating solutions that can redefine CRE for the digital age.