Artificial intelligence is rapidly reshaping the corporate real estate (CRE) sector, moving from theoretical discussions to practical applications. While a significant 92% of CRE teams are now piloting AI initiatives or plan to do so this year, only a small fraction, about 5%, report achieving most of their program goals. This rapid adoption, driven by C-suite mandates and competitive necessity, highlights both the immense potential and the considerable challenges in integrating AI effectively into real estate operations.
Key Takeaways
- 92% of CRE teams are piloting AI, but only 5% achieve most goals.
- Top AI priorities include data standardization, portfolio optimization, and energy management.
- Successful AI adoption requires mature data infrastructure and systematic planning.
- Talent gaps and legacy systems hinder broader AI success.
- Long-term AI strategy is crucial for competitive advantage.
The Rapid Shift to AI in CRE
Just two years ago, fewer than 5% of occupiers had plans to embed AI into their CRE operations. Today, the landscape has dramatically changed. The overwhelming majority of CRE teams, 92%, are actively exploring or implementing AI. This swift pivot surpasses even the most optimistic industry forecasts.
This urgency has directly impacted technology budgets. The top five budget priorities for CRE now all relate to AI implementation or preparing for its effects. This includes upgrading cybersecurity and digital infrastructure to support new AI tools. However, this prioritization also reveals a significant gap: many organizations are investing in AI without a comprehensive strategy.
Many CRE teams are adopting AI not by choice, but due to mandates from senior leadership. This often views AI adoption as a competitive necessity. This strategic gap directly contributes to execution challenges, limiting the scale and success of many pilot projects.
AI Adoption Snapshot
- 92% of CRE teams are piloting AI or plan to start this year.
- Only 5% of these teams report achieving most of their AI program goals.
- Real estate tech budgets are now heavily reorganized around AI initiatives.
Prioritizing High-Impact AI Use Cases
Despite limited resources and uncertain outcomes, CRE teams are focusing their AI efforts on areas that address their most pressing business challenges. Research identifies around 27 potential AI use cases across the CRE value chain. On average, occupiers are pursuing five pilot projects simultaneously.
Conventional wisdom suggests starting with simple, low-risk applications, often called 'low-hanging fruit.' Lease abstraction, for example, is an ideal Generative AI application due to its document-heavy nature. However, the industry is seeing a different approach. CRE leaders are targeting more complex, high-impact areas.
Data Standardization and Anomaly Detection
One top priority for AI pilot selection is enhancing data quality and integration. CRE teams manage vast, complex datasets, covering everything from energy consumption and employee satisfaction to space utilization and payments. Historically, this data has been fragmented and inconsistent, hindering accurate portfolio-level insights.
AI offers groundbreaking capabilities to standardize data, detect anomalies, integrate diverse data sources, and automate reporting. These initiatives may not deliver immediate cost savings. However, they build the essential data infrastructure for all future AI applications. For instance, JLL's Azara platform consolidates fragmented data from multiple systems into a unified cloud platform, offering preconfigured integrations.
"For a client in the automobile industry, Azara transformed over 20 years of low-quality operational data into a single source of truth, providing unprecedented transparency and multi-dimensional visibility that enabled strategic decision-making for the first time."
Portfolio Optimization and Agility
Another critical area for AI is portfolio optimization. Ongoing market challenges demand that CRE portfolios become more agile, fluid, and liquid to reduce operational costs. Business leaders expect portfolio optimization to be a top priority over the next three years.
Space planning and location strategy are no longer once-a-decade tasks. They are becoming quarterly requirements, or even continuous assessments for large occupiers. This helps right-size their footprint and manage costs. The sheer volume of data involved in these processes makes AI a powerful tool for efficiency.
Many CRE leaders are piloting AI for portfolio analysis, optimization strategy, and capital planning. Solutions like JLL's Azara provide real-time utilization monitoring, occupancy metrics, and portfolio-wide trend analysis. It integrates facilities management data with market intelligence, offering total cost of occupancy insights. This enables continuous assessment and proactive five-year capital planning, which is essential in today's dynamic market.
Energy Management and Decarbonization
Sustainability, energy efficiency, and decarbonization remain key drivers for technology adoption, with 93% of occupiers agreeing on their importance. Many are now turning to AI to accelerate progress in these areas. Energy management is crucial for both environmental compliance and cost reduction.
Current initiatives focus on AI use cases that deliver long-term resilience. These include AI for energy tracking and analytics, decarbonization roadmap planning, and automated HVAC control. Unlike data workflows or portfolio optimization, energy management often offers more immediate and measurable returns on AI investment. It is considered one of the most mature categories of AI use.
JLL's Hank platform demonstrates this value by applying machine learning to solve HVAC programming inconsistencies and energy performance inefficiencies. By creating digital twins of buildings and integrating with existing building automation systems, Hank automatically optimizes energy efficiency, air quality, and maintenance costs while improving tenant comfort.
The Role of Foundational Technology
While AI promises significant advancements, its success often relies on strong underlying technology infrastructure. Companies that already have mature data systems and established processes are better positioned to integrate AI effectively. This highlights the importance of addressing fundamental technology issues before diving into complex AI implementations.
Bridging the Gap: Leaders vs. Laggards
The promise of technological leapfrogging, where organizations bypass intermediate steps to adopt cutting-edge solutions, has long appealed to business leaders. In theory, AI offers this opportunity. Companies with outdated systems could jump directly to AI-powered solutions.
However, research reveals a different reality. AI adoption is widening the gap between technology leaders and laggards. Companies with successful technology programs are pulling further ahead in AI outcomes. This divergence occurs amid significant resource constraints. About 65% of organizations report experiencing CRE tech budget pressures over the past two years. This forces difficult prioritization decisions precisely when AI investment demands are high.
These budget pressures, combined with operational challenges, have slowed decision-making. More than half of companies report longer tech procurement periods compared to pre-COVID timelines. This creates a paradox: organizations need to move quickly on AI, but internal processes have become more cautious.
Two factors contribute to this slower pace: persistent talent gaps limit organizations' ability to evaluate and implement new technologies. Also, increasingly stringent ROI expectations require more extensive business case development before approval.
Despite these pressures, organizations with successful technology programs achieve considerably more with their AI efforts. These companies possess the foundational capabilities AI success requires: mature data infrastructure, established change management processes, and experienced teams. Conversely, over 60% of companies must first address fundamental technology issues, such as duplicated functionality or dormant systems, before fully leveraging AI capabilities. They face a double burden: catching up on fundamentals while competing in AI innovation.
Strategic Priorities for Effective AI Integration
Companies with successful CRE tech programs demonstrate a systematic approach to integrating new tools. They define roadmaps with clear success metrics, robust change management strategies, and processes for stakeholder engagement. Securing sponsorship from at least one C-suite leader is particularly vital. This systematic approach is key to successful AI implementation.
Research highlights four priorities for occupiers to establish this foundation:
- Balance Quick Wins with Foundational Systems: The most effective AI programs balance immediate, confidence-building wins with longer-term foundational systems. These require more effort but ultimately drive greater business value. For example, an occupier might implement AI for optimizing energy consumption, which is straightforward to assess, alongside solutions for more complex outcomes like increasing portfolio agility.
- Invest in AI Skills and Capabilities: Leading companies are better resourced in terms of AI skills. The greater the priority on nurturing innovation, the greater the return. Currently, only 33% of the workforce feels adequately trained on AI. Most occupiers (70%) use multiple strategies to source AI capabilities, including internal training, custom tool development, hiring AI talent, and external partnerships.
- Modernize Digital Infrastructure: AI innovation needs robust digital infrastructure to protect data and corporate systems. Upgrading or retiring legacy tech systems in CRE is an imminent challenge. Leaders must undertake this without disrupting business functions or losing data. Legacy systems represent key barriers to AI adoption, with 81% of companies reporting at least three existing systems not generating expected results. 88% are allocating budget to upgrade these technologies.
- Align AI Rollouts with Organizational Change: Technology adoption requires multi-level stakeholder buy-in and effective change management. Survey respondents noted that the best time to adopt new technology is during other major business changes. These include IT system overhauls, leadership restructuring, responses to new regulatory requirements, or capital planning cycles. CRE professionals who align AI rollouts with planned organizational changes are best placed to secure resources and engage their workforce.
Looking Ahead to AI's Future in CRE
Some find comfort in AI pilot failures, dismissing meaningful actions by claiming the technology is not yet mature. However, the reality is that AI transformation will only deepen. Looking towards 2030 and beyond, the purpose of current pilots extends beyond immediate ROI. They provide critical learnings to inform a more encompassing, longer-term AI strategy for CRE.
Occupiers who wait idly for technologies to mature, hoping for a 'second mover advantage,' risk competitive obsolescence. They miss the chance to experiment and understand how AI can deliver value for their unique operations. The true 'second mover advantage' lies in resisting AI hype while strategically testing chosen AI use cases and nurturing CRE teams' capabilities.
In the long run, AI's most enduring value will belong to companies that build adaptive capacity for waves of change that are yet to be fully predicted. It is not just about becoming more efficient or growing faster. It is about developing the organizational DNA to continuously evolve as AI capabilities advance.
