Institutional commercial real estate investors have broadly incorporated artificial intelligence into their investment workflows, but many are not yet seeing meaningful gains in decision-making efficiency. That is the picture emerging from Dealpath’s 2026 State of AI in CRE Investing survey, which highlights a disconnect between high adoption rates and limited realized value.
According to the survey, 97% of professionals polled said AI tools are now integrated into their firm’s investment process. Despite this near-universal take-up, only about half of respondents report tangible time savings once they account for the need to verify AI outputs. Specifically, 51% of those surveyed said AI ultimately saves them time after verification is included in the workflow.
For a significant share of investors, AI is actually adding friction. Dealpath found that 41% of respondents believe tasks involving AI take longer than if they were performed manually, largely because every AI-generated output must be checked before it can be relied upon in underwriting or investment committee decisions. This verification burden is limiting AI’s ability to streamline core investment activities.
Survey participants pointed to data, rather than model performance, as the most common source of disappointment. When AI fails to deliver expected benefits, 43% of professionals cited fragmented data as the primary issue. This concern outranked problems such as hallucinated outputs or other limitations of the underlying AI models, indicating that data foundations are a more immediate constraint than algorithmic sophistication.
Dealpath CEO and co-founder Mike Sroka said the results underscore the persistence of what he termed a “data problem” in institutional real estate, even as AI becomes commonplace. He argued that the most powerful step institutional CRE firms can take is to centralize and structure their strategic deal data and establish robust governance around it. In his view, firms that invest in building this foundation will be best positioned to use AI in a trustworthy way.
Sroka suggested that, over the coming decade, competitive differentiation among institutional investors is less likely to hinge on whether they adopt AI, and more on whether they have organized their data to make AI outputs reliable for investment decisions. The survey’s findings imply that AI tools alone are not enough to transform investment processes without a concerted effort to address data fragmentation and quality within firms’ existing information systems.
