CRE Tech Series — Part I

Across corporate real estate and facilities management, artificial intelligence has rapidly shifted from an abstract concept to an operational reality. In just a few years, AI has moved beyond experiments and pilots to become a functional layer embedded in portfolio optimization, occupancy analytics, performance management, operations, and even sales. Today, the question for corporate real estate leaders is no longer if AI will play a role, but about understanding what AI solutions are right for their organization, how effectively they are being applied, how deeply they are integrated, and how they are reshaping client and occupier expectations.

AI is now influencing decisions across the entire real estate value chain. Adoption is accelerating, yet executional maturity varies widely. Some organizations are achieving measurable operational gains, while others remain stalled by fragmented systems, legacy workflows, and unclear data strategies.

As the industry enters a new phase of digital transformation, an emerging “AI baseline” is defining the minimum digital capabilities and intelligence required to remain competitive, efficient, and aligned with enterprise goals. Understanding what now constitutes table stakes — and where early adopters are already moving ahead — is essential for any CRE or facilities organization seeking to modernize its operations.

This three-part series explores that evolution. Part 1 examines current AI trends and adoption patterns, highlighting what’s driving momentum and where organizations are struggling. Part 2 outlines practical strategies to close the AI readiness gap and align technology investments with business goals. Part 3 looks ahead to the next frontier — how leading organizations can move beyond the AI baseline toward a more intelligent, connected, and adaptive future of real estate.

The New AI Baseline — Trends Defining CRE

AI is undeniably here to stay. Yet amid its rapid evolution, the corporate real estate industry is showing early signs of imbalance between hype and practical adoption. The development of AI tools and solutions is accelerating faster than meaningful client demand and, in many cases, faster than the organizational readiness required to apply them effectively.

Many organizations understand AI’s high-level potential for automation, analytics, and efficiency, but struggle to translate that awareness into actionable implementation. Moving from conceptual understanding to piloting, resourcing, and scaling AI solutions remains a significant challenge. As a result, many organizations find themselves in an environment where opportunity and constraint coexist: the tools are available, but their impact is limited by fragmented systems, unclear data strategies, legacy workflows, and constrained budgets.

Recent surveys reinforce this reality. While nearly all firms report having adopted or planning to adopt AI, more than 90% cite barriers to tangible execution (McKinsey, 2025), including limited internal expertise, budget constraints, data quality issues, and inadequate integration across platforms. Many of these challenges stem from years of underinvestment in foundational building systems and application infrastructure, leaving organizations unprepared for the data-driven discipline that effective AI deployment requires.

In November 2025, McKinsey published a comprehensive global survey of 1,993 respondents spanning Technology, Media and Telecom, Healthcare, Energy and Materials, Manufacturing, Professional Services, Travel and Logistics, Pharmaceuticals, Engineering and Construction, Financial Institutions, and Retail — offering a clearer view of the current AI landscape, adoption challenges, and maturity levels across industries. In the sections that follow, we translate several of the survey’s findings into practical insights for CRE and FM leaders.

Summary of Key Takeaways

1. Adoption is broad but shallow. While all surveyed respondents report their organizations are using AI, and many have begun to explore AI agents, the headline overstates the true level of adoption. Nearly two-thirds of organizations remain stuck in the experimenting and piloting phases — testing AI use cases without yet deploying them at meaningful scale. Among those using AI, only about 7% report being fully scaled, while roughly 31% are in the process of scaling deployment across their organization. The remaining 62% are still experimenting or piloting. Fewer than 10% of respondents report scaling AI within any individual business function, revealing a significant gap between stated adoption and operational maturity.

2. Organization size is a decisive factor in AI maturity. Large enterprises — particularly those with revenue above $1 billion — are far more likely to possess the structural maturity, data readiness, and transformation resources required to deploy AI at scale and realize meaningful returns. McKinsey’s data shows that AI scaling correlates closely with company size: among organizations with $5 billion or more in revenue, 39% report scaling AI and 10% are fully scaled. By contrast, among firms with less than $100 million in revenue, only 22% are scaling and 9% are experimenting or fully scaled. Mid-market and smaller firms often adopt AI “in principle” but struggle to move past pilot paralysis due to familiar barriers — limited standards and data governance, fragmented or insufficient data types, inconsistent operating models, narrow or unaligned use cases, and underdeveloped enterprise strategies.

3. Efficiency drives most AI investment; workforce impact is gradual, not sudden. Across respondents, more than two-thirds indicate that efficiency and cost reduction was the primary AI enterprise objective. Growth, innovation, and business model transformation remain secondary or tertiary priorities (McKinsey, 2025). With use cases focused on efficiency rather than transformation, the workforce impact does not appear to be a straightforward automation story. Instead, expectations point to a gradual transition that includes changes to the mix of job types — with fewer task-based roles and a continuing shift toward more skilled and supervisory positions rather than large-scale job displacement. Looking forward into 2026, respondents shared their outlook on workforce changes: 32% anticipate a decrease in workforce size; 43% expect no change; and 13% expect an increase (McKinsey, 2025).

4. Three distinct industry clusters are emerging, each on a different AI trajectory. In examining AI adoption patterns across industries and business functions, a key insight emerged: three distinct clusters of adoption and correlation are forming, each accelerating along a different path shaped by data maturity, regulatory context, and operational complexity.

Group 1 — Technology, Media, and Telecom (Digital Industry Vertical). These industries operate as inherently data-driven businesses and consistently demonstrate the highest AI value. Their early adoption patterns, strong use-case profiles, and mature data ecosystems position them as data-rich verticals. Their AI maturity is elevated by digital-first business models, emphasis on customer engagement, and use cases that naturally align with automation, analytics, and enhanced human experience. IT functions within this group lead all industries, with 22% of technology respondents reporting scaled AI deployment in IT alone.

Group 2 — Healthcare and Insurance (Regulated and Risk Industry Vertical). These sectors follow risk-driven models, resulting in slower but steady AI adoption. They have strong potential in areas such as underwriting, fraud detection, diagnostics, and care optimization. However, healthcare adoption in particular is constrained by regulatory complexity, privacy requirements, and stringent governance frameworks.

Group 3 — Industrial and Resources (Operational Industry Vertical). This group spans advanced manufacturing, energy, and materials — all asset-intensive sectors. Their AI maturity is comparatively lower, with use cases centered on physical systems and data from building and equipment maintenance. Adoption is likely constrained by regulatory obligations, data variability, operational complexity, and the influence of long capital investment cycles.

Among business functions across all industries, IT leads at 9% overall scaled adoption, followed by knowledge management at 8%, and marketing and sales at 7%. Functions further from digital workflows — such as supply chain/inventory management (2%) and manufacturing (2%) — show significantly lower scaling rates, reinforcing the relationship between data readiness and AI maturity.

5. AI adoption is broad but depth remains limited. In 2025, nearly 88% of respondents report using AI in at least one business function. However, adoption drops sharply as organizations attempt to scale across functions. While 70% deploy AI in two or more functions, only 51% deploy across three or more — a 73% decline from single-function adoption. Only 20% report deployment across five or more functions, reflecting a dramatic 340% decline from the single-function baseline.

This sharp drop-off underscores the operational and organizational challenges companies face when moving from isolated AI use to enterprise-wide integration. Key implications include:

Process mapping — before automating workflows or designing processes, organizations must understand — and often redesign — their business processes. Clear mapping of how processes interconnect is essential to create a target operating model that leverages automation, structured data, and AI. A new “blueprint for change” is required to unlock the full impact of workflow automation and data-enabled decision-making.

Deployment complexity — scaling AI beyond a single function introduces integration challenges across processes, systems, and teams. This creates friction, increases change management demands, and heightens the risk of fragmentation.

Organizational readiness — few organizations have the expertise, governance structure, or data foundation required to support multi-function AI deployment. Limited internal skills, unclear ownership, and gaps in data infrastructure continue to constrain maturity.

Value realization — enterprise-level value differs from functional value. Many organizations struggle to achieve cross-functional or enterprise-wide impact. AI investments are often isolated within individual functions, preventing organizations from realizing broader operational or strategic benefits.

The data cited throughout this analysis is sourced from McKinsey Global Surveys on the state of AI (2017–2025) and “The state of AI in 2025 — Agents, innovation, and transformation” published by QuantumBlack AI (McKinsey, November 2025).