CRE Tech Series — Part II

While AI adoption is accelerating across corporate real estate, a clear gap is emerging between early adopters realizing measurable efficiencies and those still struggling to translate potential into performance. Many organizations have invested in AI tools, but few have integrated them into a coherent technology strategy that supports their operational objectives or aligns with the broader business model.

This “AI gap” is less about access to technology and more about strategic execution. CRE leaders often face fragmented data environments, siloed functions, and legacy operating models that were never designed for real-time decision-making. To close this gap, organizations must first define what success looks like — from identifying functional pain points, to operational inefficiencies, and client-facing objectives that AI can improve. When technology strategy, operating model, and business intent are aligned, AI becomes not just a tool for automation, but a catalyst for performance transformation.

This part of the series explores how to help clients bridge that divide — developing AI strategies grounded in measurable outcomes, operational resilience, and alignment with the enterprise’s overall mission.

Mind the Gap and Bridge the Divide

In Part 1 we outlined the emerging AI trends in CRE, the realities of adoption, and why many organizations struggle to convert potential into performance.

The data paints a stark picture. In 2025, across all major business functions, roughly half of nearly 2,000 respondents reported either higher costs, no change in cost savings, or an inability to quantify AI’s savings impact. About one-third saw cost benefits of 10% or less, roughly 10% reported a cost benefit of 11–19%, and only 8% realized savings of 20% or more. When grouping the results into a single band — from no cost benefit up to 10% savings — more than 80% of respondents fall into this category. The pattern held consistently across functions including knowledge management, risk and compliance, marketing, product development, supply chain, service operations, HR, finance, IT, manufacturing, and software engineering.

Given how few organizations are capturing meaningful gains, the path forward hinges on turning potential into practice through a focused implementation strategy — one that tackles organizational, process, and resource barriers. To close the gap between today’s capabilities and the business objectives of tomorrow, CRE organizations must confront foundational strategic questions: What does success look like? What are the operational pain points and inefficiencies? What are the client-facing objectives? What are the measurable outcomes? How do these objectives and solutions improve operational resilience? Does the process and outcome align with the overall enterprise mission? And what resources are available to achieve success?

These questions are not rhetorical — they are the catalysts for transformation. Each one connects to a deeper challenge that organizations must address. Process mapping is essential: before automating workflows or designing processes, organizations must understand — and often redesign — their business processes. Clear mapping of how processes interconnect is critical to creating a target operating model that leverages automation, structured data, and AI. Deployment complexity increases as organizations scale AI beyond a single function, introducing integration challenges across processes, systems, and teams that create friction, increase change management demands, and heighten the risk of fragmentation. Organizational readiness remains a barrier, as 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. And value realization remains elusive because enterprise-level value differs from functional value; AI investments are often isolated within individual functions, preventing organizations from realizing broader operational or strategic benefits.

Aligning Technology Strategy, Operating Model, and Business Intent

A decade ago, in 2015, the prevailing global CRE trends in operations were focused on addressing key areas across two dimensions: efficiency — including consolidation, speed to compliance, and speed to savings — and growth and innovation — including the desire for IoT and EoT, operational excellence, and resilience.

Ironically, these trends are still relevant, pervasive, and continue to be a challenge in current organizations. When we speak of converting potential into performance, these represent new AI opportunities and solutions that are addressable to unlock previously unobtained efficiencies, growth, and innovation in target operating models.

For organizations, unlocking the next wave of AI value starts with fixing the CRE foundation. Legacy structures with organizational silos, fragmented infrastructure ownership, inconsistent processes, and constrained resources cannot be solved by a single AI solution. Achieving outcomes like compliance, operational excellence, and resilience requires redesigning the full value chain — how inputs are organized and how they are translated into measurable performance. The outputs or outcomes sit on top of strong structural inputs such as integrated data from human experience, service operations, partner networks, contracts, and building-level technology.

Effective AI-enabled CRE transformation follows a clear operational sequence: inputs from people, services, businesses, and partners flow through a process of organizing, identifying, learning, and adapting — ultimately producing holistic, integrated outcomes across assets, equipment, buildings, people, compliance, resilience, and cost savings. The shaping areas along this value chain include work identification and ingestion methods, validation of work, risk identification across assets, buildings, people, financial exposure, and weather, and utilization analysis across assets, buildings, people, space, and productivity. On the technology side, the digital stack must achieve interoperability and vertical digitization, while the digital mesh — encompassing platforms and cross-ecosystem interoperability — and digital fuel from CRE catalogs, service and business line ecosystems, partner ecosystems, critical data elements, and knowledge management all feed the learning process. The learning itself spans manual business intelligence, AI-driven insights, data-derived decisions, process change engagement, change agents both digital and human, and the critical feedback loop of re-ingesting learnings from first-pass AI findings back into the system.

Automation is only effective if these key elements are in place — including providing a clear structure for defining use cases, understanding the value chain, reducing complexity, and linking inputs to outcomes to ultimately define success.