Many corporate teams feel pressure to use AI but few are clear on how to leverage it. As a result, many pilots that appear interesting are launched look interesting but rarely become part of real operations. The demise of those pilots is often rooted in the approach. They tend to be aspirational and do not consider the impediments to employing them in the real world. In corporate real estate, successful AI efforts are practical, focused, and grounded in how teams already operate.
Start With Decision Speed, Not Data. Most CRE teams have many systems, including lease databases, project tools, finance platforms, and document repositories. Substantial data already exist and are available. The challenge is the time required to answer basic questions. For example: In a leased building portfolio, what are the key risk? What projects are over budget? Are our vendors in compliance with our service level agreements? For many teams, answering these questions requires to much analysis and takes too much time. AI tools create value when they reduce the time and effort to answer critical questions. They should not replace systems that already work.
Use AI to Interpret, Not to Decide. Accuracy is essential. Financials, leases, and compliance data cannot be approximate. The strongest AI implementations make calculations, rules, and controls deterministic and auditable. AI is applied after the data are structured and validated. Its role is to interpret results, to summarize findings, and to surface patterns that would otherwise require manual analysis. This approach prevents hallucinations, builds trust in the tools effectiveness, and allows teams to confidently leverage their AI to lighten workloads and reduce cycle times.
Replace Manual Analysis Before Automating Workflows. Many CRE processes function reasonably well but are typically slowed by the manual analysis to which they are tied. AI is most effective when it helps with tasks. For example, reviewing leases or service contracts, reconciling information across systems, identifying risks and exceptions, or answering questions that would normally require a custom report.
A reduction in manual analysis can improve decisions without forcing teams to change how they work. AI can improve and amplify processes that already work well.
Connect to Existing Systems. Large system replacements frequently cost more and take longer than what is feasible. AI efforts that depend on moving data or re-platforming systems often fail. A successful approach is to connect to the company’s current systems and document repositories. That strategy will leverage existing tools rather than trying to replace them, thereby reducing disruption and dramatically increasing the likelihood of adoption.
Favor Explainability Over Complexity. CRE decisions are reviewed by many functions including finance, legal, HR, IT, and leadership teams. Therefore, AI outputs require an easy explanation that people from different professional backgrounds can understand. Core elements of an explanation include what data was used, what documents were referenced, and why a risk or recommendation has surfaced. If AI cannot explain itself clearly, it will not be trusted or used.
Assess Build Versus Buy. Some organizations should buy AI capabilities. Others are better served building internally. Many will do both. The right choice depends on internal skills, security needs, speed to value, and the level of control required. Being transparent about these tradeoffs leads to better decisions and better outcomes.
Measure Value in Decisions, Not Models. AI success in CRE should be measured in business terms. Less time spent reconciling data and faster answers. Earlier visibility into risk. Better informed lease and capital decisions. If AI does not improve how decisions are made, it is not delivering value.
AI does not need to overhaul corporate real estate to be useful. The most effective uses quietly reduce friction, remove manual work, and help teams make decisions with more confidence. When applied with guardrails, use of existing systems, and an understanding of how corporate real estate works, AI can become part of the foundation rather than an experiment.
