As the hype around generative artificial intelligence (AI) turns into a more sober assessment of its true capabilities, stakeholders in the real estate market are starting to think about where it should be used first. For commercial real estate (CRE) investors in particular, generative AI represents an opportunity to innovate. But why invest in the expense? What are the key justifications? In short, there are three big reasons right now. CRE investors should investigate AI to:
1. Remain resilient in shaky market conditions
Inflation is persistent and eats away at net operating income health. And most critically for CRE investors, the hybrid work revolution has made attendance levels in buildings chronically low. For instance, across all global regions, fewer than 45% of organizations surveyed by JLL reported employees coming into the office five days a week. This current climate is shrinking the demand for space, particularly offices, in a way that challenges the foundation of investor business models. The old real estate investment model that relied on low interest, middling occupancy levels, and highly consistent attendance is no longer feasible.
2. Control costs
Controlling costs can primarily be achieved by gleaning operational and strategic insights about where money is spent. Budgets are tight, and due to market conditions, risks are high. Rather than simply being seen as an experimental expenditure, AI should be seen as a means to alleviate the pain.
3. Boost staff productivity
Fundamentally, AI solutions have been designed, and are being iterated, to be productivity tools. Deployed appropriately, AI tools enable users to interact more effectively with databases, informational documents, and collaborative platforms by quickly summarizing large amounts of data. For example, more than 1,000 employees across Capital Markets and LaSalle -- both divisions within JLL that deal with investor clients -- already use the in-house JLL GPT tool on a weekly basis to complete their work tasks. They use it to automate email creation and accounting invoices and to boost the efficiency of coding production. These are small tasks, but replicated across thousands of employees, this is a large-scale test case for boosting organizational productivity.