The future of corporate real estate in the AI age

Image by Kavidu - stock.adobe.com. This work product was created with assistance from generative AI.

• AI is reshaping work and business operations dramatically and this will have an inevitable impact on real estate over the next decade, from the world’s leading innovation hubs and gateway cities to global capability centers in emerging markets.
  
• The pace of business change will accelerate and corporate real estate portfolios will evolve to accommodate uncertainty and continuous change. Location decisions will increasingly factor in power availability, infrastructure security and AI regulatory frameworks, in addition to talent, cost and scalability.
  
• As AI takes on more routine tasks, workplaces will evolve into spaces that support teams of human experts orchestrating complex AI systems. Creativity-focused environments that enhance cognitive performance, partnership zones for external collaboration and smart workstations enabling seamless human-AI agent interaction will be in high demand.

The future of work isn't being written in boardrooms or strategy documents. It's being lived right now in the bustling tech corridors of the San Francisco Bay Area in the United States and the innovation labs of Bengaluru in India, where organizations are grappling with AI transformation in real-time.

Through intensive learning expeditions to these two AI epicenters, our JLL Future Vision team conducted field research with over 40 senior executives, entrepreneurs and academics, leveraging our global network of 500+ researchers. What we discovered highlights a significant shift taking place: the AI revolution isn't just changing how people work – it is impacting where they work, how they organize and what they need from their physical environments.

Two AI pathways reshaping real estate strategy
 

The San Francisco Bay Area model of high-velocity innovation operates under compressed business cycles where breakthrough development requires proximity to elite talent, venture capital and research partnerships. Organizations here operate in 12-24-month business cycles that create fundamental misalignment with historically long lease lengths.

The Bengaluru model of systematic scaling demonstrates how companies move up the value chain through AI adoption while maintaining cost optimization and developing strong educational alliances. This approach enables the creation of a sophisticated network of specialists and expertise on a global scale, with efficient cost structures built around talent clusters.

Three disruptions that corporate real estate leaders will need to address
 

AI demands new talent strategies with unprecedented agility. Organizations need simultaneous access to elite AI talent for breakthrough innovation, cost-optimized scaling capabilities and locations where power grids and energy supply drive competitive advantage. 12-24-month business cycles challenge traditional real estate planning time frames and also require instant facility reconfiguration capabilities that support intensified collaboration.

Organizational hierarchies morph into flexible networks. AI will dismantle many traditional management structures and department boundaries, forcing companies to redesign physical environments originally built to support hierarchical organizations. The mismatch between organizational evolution and workspace design creates new facility challenges – in a context where the need for face-to-face collaboration intensifies.

Workers transform into AI orchestrators. Traditional job categories will dissolve as employees become System Thinkers, System Builders and System Operators - entirely new roles requiring workspace configurations that support new human-AI ways of working. Physical presence becomes more critical and spaces must adapt to enable seamless interactions between humans, AI agents and robotic counterparts.

The evidence from San Francisco and Bengaluru - as well as from other innovation hotspots - points toward a fundamental question that every leadership team must answer: how quickly can you adapt your real estate strategy to support more intensive human collaboration in an AI-augmented future that's arriving faster than anyone anticipated?

Concentration vs. Distribution
 

Our research in the San Francisco Bay Area and Bengaluru highlights how AI transformation is reshaping global talent distribution patterns. These emerging patterns point toward new strategic choices that will influence corporate real estate portfolios across all markets.

Premium talent hubs will intensify their pull. In San Francisco, we observed markets where AI innovation requires proximity to both elite talent and capital. As Robert Cormican, CEO & Founder of Forge Robotics, explained during our visit: "San Francisco provides the optimal convergence of capital availability, technical talent concentration and stakeholder trust necessary for breakthrough innovation."

By 2030, this concentration effect will extend globally. Large internationally connected hubs—New York, London, Tokyo - as well as rapidly growing destinations like Austin, Copenhagen and Brisbane will intensify their attraction of AI-ready talent and provide enhanced access to venture capital and research partnerships essential for cutting-edge development.

Distributed capability networks will expand worldwide. In Bengaluru, we witnessed a systematic scaling model where organizations achieve productivity gains through AI-augmented operations while accessing cost-optimized talent pools. As Harsha Arora, Hub Leader of Global Enterprise Services at Merck, described to us: " We evolved from transactional order management support to more scientific roles, having end-to-end ownership, enhancing agility, compliance and time-to-market. By delivering quality service, we earned the right to play—and were able to go up the value chain and gain increased confidence to innovate."

This Bengaluru model will start to expand globally. Capability centers will emerge in secondary markets worldwide with diverse industry presence, offering alternatives to premium hubs across Eastern Europe, Southeast Asia and Latin America.

The third path: infrastructure-first locations. Infrastructure is gaining increasing weight in location decision-making, sometimes outweighing traditional resourcing considerations. As Manish Sonkar, CEO of NeoErng, told us in Bengaluru: "This entire AI race is nothing but an energy demand race." Meanwhile in San Francisco, Robert Cormican, CEO & Founder of Forge Robotics, observed: "People underestimate how much AI progress depends on physical infrastructure."

Our research points toward an emerging third model that could disrupt the talent-versus-cost paradigm entirely: locations chosen primarily for infrastructure capability when elite talent proximity isn't critical. This means identifying infrastructure-optimized locations where grid stability and energy availability, fiber connectivity and AI-friendly regulations enable lean operations for functions that don't require access to premium talent clusters.

Leading organizations will employ conscious portfolio orchestration across all three models: premium talent hubs for breakthrough innovation, capability centers for systematic scaling and infrastructure-optimized locations for AI-intensive operations. Success will require mapping specific business functions to their optimal environments while maintaining seamless coordination across these distributed models, each offering distinct competitive advantages.

We asked AI to help us visualize how AI transformation could impact cities, workplaces and employees.

When compressed business cycles require elastic portfolio strategies
 

The most immediate challenge we observed was the growing collision between AI-accelerated business cycles and traditional real estate planning horizons - a tension already reshaping how organizations approach their real estate commitments.

Compressed timelines: Organizations have been increasingly seeking lease flexibility to accommodate accelerating business transformation, with more companies demanding break clauses, shorter initial terms and expansion options rather than traditional long-term commitments

In San Francisco, AI is now intensifying this trend dramatically by compressing business planning cycles from the traditional 3-5 years to just 12-24 months in innovation-intensive sectors. As Gadi Amit, President & Principal Designer of NewDealDesign, told us: "The actionable future is only 18 months away. Market success requires immediate excellence without iteration opportunities." In the coming months and years, this acceleration will push flexibility requirements to new levels, as organizations can no longer risk committing to assumptions about workforce size, collaboration patterns and operational requirements that AI may change within months.

Flexibility Imperative: Organizations will need a more nuanced portfolio strategy: maintaining longer-term commitments where workforce needs are more predictable, while developing flexible arrangements with built-in adjustment mechanisms for uncertain disruption scenarios.

This will drive increasing demand for:
 

• Pre-fitted facilities operational within weeks, not months
• Reconfigurable buildings able to support business model pivots
• Facilities housing entire workflows to compress decision-to-execution cycles and support value chain integration.

From pyramids to networks
 

Our expeditions highlighted how some organizations are already undergoing radical structural transformation as AI assumes traditional coordination functions. This shift creates a mismatch between how companies organize people and how they organize space.

The management layer upheaval: In both San Francisco and Bengaluru, we witnessed the systematic dismantling of hierarchical structures. As Barry Katz, Professor of Industrial and Interaction Design at Stanford University, told us: "Organizational structures are flattening, eliminating middle management layers." And this wasn't gradual evolution. The founder of DataGrid, Thiago Da Costa, described their radical transformation: "We have no middle management, only individual contributors. AI can provide better feedback than poor management structures." This hierarchical disruption is likely to broaden across industries as AI capabilities assume coordination, oversight and decision-routing functions previously managed through human management layers.

The small team revolution: We discovered organizations restructuring around dramatically smaller, more nimble units. Sviat Dulianinov, Chief Executive Officer at Bright Machine explained: “Speed matters, and software-defined manufacturing enables smaller, more agile teams to outperform traditional operations built on scale alone.” But these teams require infrastructure that supports intensive collaboration, not reduced space.

Multi-faceted roles: The most striking organizational change we observed was the dissolution of traditional role boundaries as AI enables individuals to perform functions previously requiring specialized expertise. As Alex Kass, Fellow & Principal Director at Accenture Lab San Francisco said: “We're transitioning from individual contributors to managing AI agents - or being managed by them when they assign us tasks. It creates richer, more diverse interactions." In the coming years, organizations will need facilities that support this role fluidity rather than siloed departments.

The physical presence imperative: Despite extensive automation capabilities, our research shows that successful AI adoption intensifies rather than reduces the need for human collaboration - but changes its nature fundamentally. The ongoing San Francisco renaissance reflects this reality, as organizations return to physical presence not for traditional reasons, but because AI complexity requires cross-pollination of perspectives. As Thiago da Costa, CEO and founder of DataGrid, recognized: "Remote work culture is a real challenge to keep people engaged. I don't think distributed teams are that efficient. The complexity we address with AI is so high that you need to share and cross perspectives."

Designing for organizational fluidity
 

The organizational transformations we observed require a significant rethink of how space supports work. More organizations will move away from fixed departmental layouts in favor of adaptable environments that reconfigure as teams form, dissolve and reform around specific projects and AI capabilities. They will need workspace configurations that support three distinct operational requirements emerging from AI transformation:
 

• Intensive team collaboration zones supporting small self-contained teams, each requiring dedicated collaborative zones with integrated technology, flexible seating, and all operational needs - from innovation activities to social interaction - contained within the team environment.
• Individual AI-augmented workstations enabling rapid task switching between strategic thinking, technical implementation and collaborative problem-solving. Workers will need specialized environments optimized for human-AI interaction, featuring multimodal interfaces and enhanced computational access and robotic integration capabilities that support seamless transitions across diverse cognitive modes within single projects.
• Partnership integration areas: AI complexity will intensify the need for external collaboration with universities, startups and vendors. Organizations will require secure spaces specifically designed for cross-organizational work, with appropriate access controls and intellectual property protection while enabling the intensive coordination that AI-augmented innovation demands.

Three new types of workers
 

Traditional job categories are changing already as AI transforms how humans interact with technology and each other. What emerged from our expeditions were three distinct types of human-AI collaboration that will reshape workspace requirements.

• System Thinkers: In Bengaluru, we witnessed professionals evolving from operational roles into strategic orchestrators. As Venkatesh Kumar, CEO of StackBox, told us: “The future belongs to System Thinkers - professionals who can envision, strategize and orchestrate entire ecosystems. These are the people we're actively seeking because they understand not just how to build or operate, but how to conceptualize and guide intelligent systems toward business." These System Thinkers require workspace configurations that support complex decision-making processes, strategic visualization capabilities and oversight areas where they maintain control over AI operations while leveraging machine intelligence for analysis and recommendations.
• System Builders: We observed a new category of professionals who create and optimize human-AI workflows. As Gregory Renard, Research Associate at Stanford, described: "Today, I create my AI assistants - I no longer hire assistants because I lose too much time. 100% of jobs will transform." System Builders will need specialized environments featuring rapid prototyping spaces, development areas for interface design and process optimization zones where they can continuously refine how humans and AI collaborate across different functions.
• System Operators: While many operational roles are being automated, we discovered that System Operators - humans who manage AI systems while maintaining critical oversight—remain essential. These professionals require role-specific zones supporting system monitoring, quality control and autonomous execution management with immediate intervention capabilities.
   

Beyond keyboards and screens
 

Perhaps the most striking transformation we observed was how human-AI interaction is moving beyond traditional computer interfaces toward more natural, integrated collaboration methods.

Multimodal workspace: We witnessed organizations experimenting with voice controls, gesture recognition and ambient AI systems. As Alex Kass, Managing Director at Accenture Labs, explained: “It's a whole reinvention of what work means beyond just replicating processes. We're capturing neurosensing AI to give more context through smart devices, creating enriched experiences that help AI to become more relevant and help humans work better together." Workspaces will need to support seamless transitions between different interaction modes - voice commands for rapid task delegation, gesture controls for spatial problem-solving and traditional interfaces for detailed analysis - all within single work sessions.

Conversation design challenge: We observed professionals spending increasing time "conversing" with AI systems to refine outputs, troubleshoot problems and explore possibilities. This requires workspace design that supports extended dialogue with machines while maintaining human cognitive performance and social connection. Facilities will need acoustic design that enables private AI conversations without disrupting nearby collaborative work, along with visual privacy solutions for sensitive AI-assisted decision-making processes.

Continuous learning and knowledge capture infrastructure: Successful AI adoption creates an accelerating need for continuous learning, as AI capabilities evolve rapidly and human roles adapt in response. But this is not traditional training - we observed organizations implementing learning systems where AI helps humans to develop new capabilities in real time as work requirements evolve. We also discovered organizations systematically converting workplace interactions into organizational intelligence. Facilities will need embedded systems that capture knowledge from all workplace interactions—meetings, problem-solving sessions, creative collaborations - and convert this into AI-accessible organizational memory that enhances future human performance.

Designing for new cognitive human-AI partnerships

The most complex challenge we observed was designing physical environments that optimize the intricate balance between human creativity, AI capabilities and collaborative decision-making. In both markets, we witnessed organizations investing heavily in environments that enhance human cognitive performance while integrating AI capabilities – generative AI, agents or robots - seamlessly. We also observed organizations developing sophisticated workflows where AI provides analysis and humans make strategic choices, but the handoffs between machine processing and human judgment require carefully designed physical environments that support those new alliances.

More and more workspaces will need three distinct zones for optimal human-AI collaboration:
 

• Deep work environments: Individual spaces optimized for intensive human-AI interaction, featuring advanced interfaces, robotic integration and distraction-free design for complex problem-solving.
• Collaborative intelligence areas: Team spaces where multiple humans work with AI systems, agents and robots simultaneously, requiring shared visualization capabilities and coordinated access to machine intelligence.
• Human creativity amplification: Environments designed to enhance human creative capabilities that AI cannot replicate, featuring natural lighting, biophilic design elements and flexible spatial configurations that amplify unique human cognitive strengths.

Our field research in the Bay Area and Bengaluru highlighted how AI transformation is already impacting traditional corporate real estate strategy. Organizations that recognize these shifts early and adapt their portfolio strategies accordingly will capture decisive competitive advantages.

In the coming months and years, organizations will be differentiated by how effectively their physical infrastructure enables AI-augmented human performance. Those that master the integration of adaptive location selection, flexibility-first portfolio design and human-AI collaboration optimization will operate with significant advantages in speed, cost and innovation capability.

The transformation is accelerating faster than most organizations anticipate. The strategic advantage will belong to those able to act decisively and reimagine their real estate strategy sooner rather than later.

The convergence of talent and innovation has long shaped real estate investment strategies. Cities with the highest innovation activity - San Francisco, Boston, London and Seoul - consistently attract substantial institutional capital and command premium pricing across asset types.

While the pandemic induced a surge in migration to lower-cost, lifestyle-focused cities in the Sun Belt of the U.S., Southern Europe and secondary Australian and Canadian markets, many shifts have stabilized. Corporate demand, leasing activity and transaction volumes are again concentrating in gateway markets where innovation ecosystems thrive.

AI amplifies existing patterns: As AI reshapes work and corporate growth strategies, real estate investors will increasingly focus on innovation nodes. The strong relationship between talent concentration and breakthrough development - essential for institutional exit opportunities - makes these locations even more valuable.

The market bifurcation between sought-after asset subtypes and segments with limited liquidity will intensify. AI's impact on space utilization, team structures and location preferences will create a new hierarchy of outperformers, requiring investors to carefully evaluate which assets can adapt to evolving workplace demands.

Success will depend on identifying properties that can support the three imperatives: speed and agility, AI-powered collaboration and human-machine integration. Investors positioned in locations and assets that enable these capabilities will capture the value creation opportunities of the AI transformation.

We extend our thanks to Matthieu Aguesse and Alexandre Heully from Schoolab for their contributions to this work.