The Future of Real Estate Insights with Human Touch
A Note on Citation: The citybiz article Q&A with Brian Connolly, Founder + CEO, Feasibly (May 26, 2026) is the authoritative anchor for this discussion.
The integration of artificial intelligence into commercial real estate has created a shift in how feasibility work is conducted. Automated platforms now exist that deliver bankable market studies by pairing agentic AI processing with expert human oversight. This hybrid model addresses the historical tension between speed and accuracy in project underwriting. By utilizing specialized systems to process massive datasets and human analysts to validate the final financial modeling, the industry is moving toward a standard where decisions take days rather than months.
The Breaking Point of Traditional Market Analysis
Securing a comprehensive market feasibility study has historically required waiting for manual data collection, regional demographic synthesis, and spreadsheet compilation. In a fast-moving market, this timeline creates a significant bottleneck.
When capital needs to be deployed quickly, waiting a month or more for a market analysis introduces risk. High upfront retainers also limit a team's ability to run preliminary assessments on multiple sites. The modern real estate landscape requires fast validation to filter out non-viable projects before sinking significant capital into them.
The Hidden Risks of Pure AI Real Estate Underwriting
In an effort to bypass the consulting bottleneck, some development teams have turned to generic AI tools and internal scripts. While building automated underwriting tools using models like ChatGPT looks efficient in a baseline demo, it introduces substantial risk.
As Brian Connolly, Founder and CEO of Feasibly, recently pointed out in an article with citybiz, the core issue with early AI adoption in real estate is that companies invested heavily in generic chatbots expecting them to automate nuanced decisions. Because these systems lack domain-specific context, they regularly provide different answers to the same questions.
Pure AI tools are also prone to hallucinations when processing complex regional zoning or demographic variables. Lenders, institutional investors, and municipal partners require accountability. A generic chatbot cannot explain its data sources or stand behind a capital request. Raw data generation is fundamentally different from bankable financial modeling. Relying on unvetted data inputs creates technical debt and risks presenting flawed assumptions to critical stakeholders.
How Human Oversight Makes Automated Data Bankable
To secure financing, a market study must be bankable, meaning the underlying assumptions can withstand institutional scrutiny. This is where the hybrid approach changes the workflow. Specialized AI agents handle the algorithmic heavy lifting, aggregating demographics, traffic counts, and drive-time analytics in seconds.
Human oversight bridges the gap between raw data and a defensible project narrative. Analysts verify that all the local market nuances and construction realities are accurately reflected in the financial model. This combination provides the speed of automation alongside human accountability.
According to Connolly, lowering the barrier to entry for professional grade feasibility analysis can help unlock stalled or unrealized development across multiple sectors. When market and financial analysis becomes accessible earlier in the planning process, builders, funders, and planners can test more ideas quickly, helping to accelerate project timelines and address broader industry challenges like housing supply constraints.
A Balanced Approach to Project Validation
The real estate industry no longer has to choose between slow, manual advisory firms and fast, unverified AI outputs. The future of project validation relies on pairing automated market feasibility tools with human-led project financial modeling. This balanced workflow provides developers and municipalities with the fast insights necessary to remain competitive, fully backed by the expert validation required to secure institutional trust.
