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Artificial intelligence is showing up in a lot of industry conversations. But for teams doing commercial real estate appraisal, valuation and analysis work, the real question isn’t whether AI will matter, but how it can be used without creating more problems than it solves.
A recent CPE Voices webinar, “Harnessing the Power of AI in CRE,” focused on exactly that: how to bring AI into CRE practices in ways that actually deliver value. The discussion included leaders from across the industry, including Cliff Taylor from Yardi, along with experts from other major firms and academia. What came through clearly and consistently was a grounded message: AI is a tool, not the strategy — business outcomes are the strategy.
Focus on friction, not flash
One of the biggest traps companies fall into is trying to “AI everything.” The result is often a lot of activity but very little impact. Cliff Taylor stressed a practical way forward: find the places where work is repetitive, manual and slowing you down, and start there.
In real estate operations and valuation work, those friction points tend to be familiar:
- Endless document review and data extraction
- Work order updates that get done in the field, then repeated in desktop systems
- Invoices that require multiple approvals for repetitive charges
- Meeting notes that never get turned into clear action items
- Data scattered across systems with no reliable foundation
Identifying these kinds of tasks and removing unnecessary manual effort makes people more productive, and that’s where real value starts.
Put the data foundation first
AI depends on good data to be useful. If information needed for valuation or appraisal lives in a mix of databases, spreadsheets, shared drives and unpublished lists, nothing will be predictably accurate. In CRE valuation workflows, accuracy matters more than speed. Poor data means poor outputs, every time.
Before any AI tool earns a place in your operations, make sure your data is accessible and organized, well understood across teams and governed so that rights and permissions are clear. Only with that foundation can tools help instead of hurt.
Build guardrails that protect quality
One concern that comes up a lot with generative models is the risk of “hallucinations” which are outputs that look plausible but aren’t correct. This can’t be ignored in appraisal and valuation work. Taylor described a practical approach to navigate the issue using multiple checks instead of a single pass. That involves:
- Running the same dataset through multiple analysis paths
- Comparing outcomes to see where they agree or diverge
- Flagging low-confidence results for human review
- Not moving forward until a person vets the answers
Tools can speed up workflows, but they don’t replace human expertise in judgment, interpretation or valuation decisions.
Roles will shift, but people still matter
The panel made a clear distinction: repetitive tasks are more likely to be automated, providing an opportunity to shift valuable human effort into work that requires judgment and understanding. In appraisal and broker work, relationships, negotiation and deep market knowledge are not going away. AI can take the busy work off people’s plates so they can spend more time applying their expertise where it matters most.
Decide on success before you start
AI projects that don’t define success upfront tend to drift. Taylor encouraged teams to set clear outcomes before anything is built. Measuring time saved or reduction in bottlenecks is useful, but the goal should be more specific in terms of how much faster information needs to flow, what percentage of manual effort should be reduced and what kind of exception handling should happen earlier. Defining these things before deployment gives everyone a clear target. Without it, there isn’t a reliable way to tell whether the new approach is better.
Get teams comfortable with the tools
One piece of the adoption puzzle is less technical and more cultural. Demystifying AI by explaining what it can and cannot do helps teams engage with it rather than avoid it. When people see a tool saving time on tasks they dread, they become advocates rather than critics.
Gain real value from practical application
The conversation repeatedly returned to a simple idea: Don’t chase technology for its own sake. Improve a process that hurts today and you’ll understand what AI can do for your organization. Work order processing, invoice approvals and document review are the kinds of tasks where teams can see measurable impact quickly.
Looking ahead
AI can help, but only when it’s applied with clear purpose, good data, defined outcomes and a sensible partnership between people and technology. Improving how people work by removing repetitive effort, improving accuracy and freeing time for decisions is where the real change happens. That’s true for appraisal teams, analysts, operations groups and brokers alike.
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