AI is transforming the working life of real estate professionals. We are all going to have to rethink and relearn our jobs.

Emerging academic research supports what many of us are finding out for ourselves; effectively utilizing AI allows people to do their work better and faster. Whether undertaking market analysis, building discounted cash flows, compiling fund reports, or writing thought pieces, early adopters are achieving significant efficiency gains.

Once these efficiencies emerge, what next? As the costs of completing tasks fall, new use cases will arise. This has happened in the past; when spreadsheet software first emerged, basic bookkeeping tasks were displaced. However, new jobs were created through broader small business adoption of accounting services. Spreadsheets also allowed accountants to focus more on advising clients.

While people needed to retrain, spreadsheet software had a net positive impact on accounting employment; lower costs for accounting activities increased overall demand. Experts expect similar dynamics as AI becomes ubiquitous in real estate. While AI reduces resource constraints, professionals can redeploy efforts to add more value. Your organization can find a competitive advantage in intelligent adaption as well as rapid adoption. It is time to ask what you will do now that you can do so much more.

Here are five ways workflows and processes are set to change as AI unlocks new possibilities:

1. From simplistic assumptions to robust risk analysis

The application of AI is likely to revolutionize underwriting and investment analysis in commercial real estate. No longer will investment decisions be based on a simple Excel model with a few hard assumptions.

Rather than relying on a single base case, investors can use AI systems to generate hundreds of realistic scenarios informed by both quantitative data and qualitative insights. AI can rapidly assess how changes in rental growth, vacancy rates, operating expenses, capital expenditures, exit cap rates, and other variables impact asset performance. Investors will understand ahead of time how much these factors can swing returns, enhancing risk management and resulting in decisions made from a richer information set.

With AI systems, investors can embed probabilistic thinking into underwriting, moving beyond simplistic point estimates to model distributions and correlations. Investors will comprehensively map the risk landscape and understand tail risks before acquisition.