AI for business growth: Are real estate investors ready to gain a competitive edge?

• AI adoption has accelerated beyond expectations. Despite real estate's reputation for slow technology adoption, 88% of investors have already started piloting AI, pursuing an average of five use cases simultaneously across the entire real estate value chain.
• Strategic priority pivots from efficiency to growth. Real estate investors are shifting their AI focus from operational improvements to revenue-generating applications, with 5 of the top 6 AI objectives now directly related to growth and competitive positioning rather than cost reduction.
• Preparation gap threatens competitive position. While 87% of companies are increasing their real estate technology budgets because of AI, over 60% remain strategically, organizationally and technically unprepared for scaled AI implementation beyond pilots, creating a widening gap between leaders and laggards.
   

Amid all the market uncertainties in 2025, real estate investors face critical questions. Will AI provide the competitive advantage that separates market leaders from followers, or is it just another technology hype destined to underwhelm? How should my company justify this investment in the current economic environment?

To understand how real estate investors are navigating the growing momentum around AI adoption, JLL surveyed over 500 senior decision-makers across 15 markets, spanning private, public and institutional investors and investment management firms. The findings reveal how investors are currently experimenting with AI, their key obstacles and the capabilities they will need to remain competitive as AI redefines how real estate businesses create value.

The question isn't whether AI will reshape real estate investment. It is whether your organization will harness the benefits from this transformation or be left behind.

For the past few years, real estate investors have faced volatile markets, swayed by geopolitical pressures and fluctuating occupier demands.

Yet buffeted by the same economic headwinds, organizations with successful technology programs report greater confidence in navigating market volatility and risks. 93% of investors say that high-quality tech-enabled properties deliver stronger performance and returns, which is in line with occupier sentiment: 94% are willing to pay a premium for tech-enabled space that provides better energy efficiency and tenant experience.

This has driven the demand for bespoke technology capabilities: tailored tech solutions that are precisely matched to an organization’s specific business model, investment strategy and operational requirements. Being bespoke—rather than following a generic approach to tech implementation—provides a critical competitive edge, allowing for more effective investments and risk management in uncertain markets.

While improving bottom-line profitability is the top business priority over the next three years for all investor types, building bespoke technology capabilities ranks among their top 5 priorities, signaling that tech enablement is now indispensable to stay competitive in an increasingly data-driven, complex market.

The most outstanding theme in these ‘bespoke technology capabilities’ is AI. Despite a reputation as cautious tech adopters, an impressive 88% of investors, owners and landlords are already piloting AI, a big jump from 5% in 2023. 

This uptick is largely a response to the accelerating breakthroughs of AI technologies since 2023. While many current pilots focus on established analytics AI (e.g., machine learning) and the more recent generative AI, the conversation among industry leaders is already starting to shift to what comes next: agentic AI systems that can reason and independently execute multi-step strategies using multiple tools and data sources.

JLL’s analysis of AUM growth over the last economic cycle shows that aligning investments with the right thematic trends—such as urbanization and sustainability—has been the primary driver of portfolio growth. Now, AI represents one of the most significant themes for the next few years. The advancements of AI and their widespread adoption across industries signal a paradigm shift on the horizon—which many believe to be the largest since the Industrial and Digital Revolutions.

Just as the internet gave rise to entirely new business models such as e-commerce, social channel monetization and the sharing economy, AI is poised to disrupt the real estate competitive landscape and how the real estate industry creates value.

This conviction in AI's long-term potential has triggered a ‘fast-forward moment’ in commercial real estate. While some still view it as hype, the overall industry commitment is real. Across all investor types and geographic markets, 87% of companies are increasing their real estate technology budgets to adopt AI, with the top 5 items focused on implementing AI or preparing for its impact through, for example, upgrading cyber and data security measures.

This leads to the next question: where exactly are investors focusing their AI efforts?

JLL has identified 28 AI use cases across the real estate value chain, with most companies actively pursuing five pilot projects simultaneously. Current adoption patterns show which applications are gaining the most traction in 2025.

This adoption pattern also reveals a strategic rebalancing of AI priorities from operational efficiency to revenue and growth opportunities.

Back in 2023, the majority of discussions about AI in real estate were focused on efficiency. Through 2024, JLL research shows that investors were primarily testing out AI across four areas, which are all operational in nature:
- Automating routine property management tasks 
- Optimizing the delivery of property services
- Simplifying data to strengthen risk monitoring 
- Training AI on centralized database to improve data queries

Today, this focus is notably shifting. Of the top 6 objectives driving current AI efforts, 5 are directly related to revenue generation and growth opportunities—a significant reorientation towards achieving more complex outcomes linked to business model innovation.

This strategic shift is reflected in the specific applications investors are pursuing. The top 8 most piloted AI initiatives include:
1. Market trend analysis
2. Risk modeling and forecasting
3. Integrating different data sources
4. Data standardization and anomaly detection
5. Automated property valuation models
6. Portfolio optimization recommendations
7. Static document digitization
8. Document summarization and insights extraction

Instead of cost saving, growth-focused AI pilots are aimed at delivering competitive differentiation for business success. Ultimately, growth is expected to have stronger ROI than efficiency gains.

But do ambitious objectives translate into successful outcomes? The survey reveals a more complex picture.

Growth-focused applications aren't inherently harder or easier to achieve than efficiency-focused ones. Some growth-focused applications, like automated market analysis or risk forecasting, can operate as standalone tools that feed insights to decision-makers without disrupting established processes. These can be easier to implement than efficiency applications that require fundamental changes to daily operations. The real complexity lies in infrastructure and workflow integration—AI applications that require restructuring existing processes, retraining staff or fundamentally altering how teams collaborate present the greatest implementation challenges, regardless of their strategic purpose.

Success depends on clear orchestration and phased planning rather than choosing between efficiency and growth. Leading companies balance quick wins that build confidence and momentum with the more complex, transformational capabilities that will ultimately drive the most significant business value. However, this approach is particularly challenging for companies that are less prepared strategically, organizationally and technically.

The reality is that 60% of investors across all types and sizes remain unprepared, lacking technology roadmaps and strategies that would pave the way for effective implementation of AI into
business workflows.

The rush to launch AI pilots—often driven by executive mandates—does not guarantee the foundational preparation necessary for success. Without comprehensive strategies to procure and integrate new technologies, most organizations will struggle to progress beyond isolated pilots towards scaled business impact.

Meanwhile, prepared organizations are pulling ahead. Companies with successful established technology programs have achieved significantly more with their current AI initiatives, creating a widening competitive gap. These leaders take a systematic approach to AI, with defined roadmaps, strategic resource allocation, stakeholder engagement and robust infrastructure supported by change management processes.

For lagging investors, the path forward requires building these fundamentals rather than attempting to leapfrog directly to advanced applications. Success demands anchoring AI efforts within strong technical and strategic infrastructure.

Integrating AI into an organization’s tech stack requires an approach tailored to the complexity of the AI use case, its data requirements and the impact on IT infrastructure. While specific tactics vary, research identifies three core principles of effective AI programs:

• Strategy: efine a vision and get buy-in. Rather than pursuing isolated pilot programs, leading firms develop comprehensive roadmaps that link business objectives with how AI tools can help achieve them. Success requires the right organizational structures, securing buy-in from key stakeholders and sponsorship from senior leadership to ensure adequate resources and organizational support. Engaging operational teams early builds the cross-functional collaboration essential for moving from pilots to scaled implementation. 
• Foundation: strengthen data, IT and security frameworks. Before scaling AI, organizations must ensure three critical pillars are in place – ability to capture and integrate high-quality, consistent data; digital infrastructure that can support increased computational demands and sophisticated analytics; and comprehensive protocols for data protection, a significant barrier to adoption for many. 
• Resourcing: draw on internal skills and external partnerships. Upskilling team members in AI will leverage the value of a workforce that already possesses deep real estate knowledge. Where AI capabilities are too expensive to develop internally, partnerships with AI experts and technology providers can bridge the talent gap and accelerate implementation. Structuring governance to include AI protocols will ensure initiatives align with business strategy while managing risks and compliance.

Ultimately, real estate investors who integrate AI successfully view it as an ongoing process of building tech capabilities, not a one-off initiative. 

All of the above-mentioned actions take time and resources to implement. Investors who prepare their business infrastructure for AI stand to gain a significant competitive edge – but the window to capture advantage will narrow soon.

Within the next three years, and certainly by 2030, commercial real estate markets will be reshaped by AI-enabled strategies that outpace traditional models in speed and quality of insights. The exponential pace of AI development means early preparation becomes increasingly valuable while delayed action becomes increasingly costly.

The data is clear: prepared organizations are already pulling ahead with their AI implementation.
The question isn't whether AI will transform real estate—it's whether your organization will lead or follow that transformation.