A blunt warning to Realtors: AI may displace conventional roles.

At a major industry gathering, Mike Woodward warned lenders and service providers to avoid consumer-grade, free tools for handling confidential borrower information and to insist on rigorous verification after vendor demonstrations that produced multiple factual errors. His comments highlighted the gap between polished vendor demos and real-world performance: demo environments can obscure data handling weaknesses and failure modes, while free tools often lack enterprise encryption, logging and contractual protections. For mortgage organizations racing to deploy generative AI and automation, the immediate takeaway is increased vigilance—demos should not substitute for independent validation, and teams must recognize how inaccurate outputs can translate into operational mistakes, borrower harm and regulatory exposure.

The practical response Woodward advocates centers on stronger vendor governance and technical controls. Firms should formalize data classification and handling, use secure sandbox environments for testing, require human-in-the-loop validation and maintain robust monitoring to detect output drift. Contracts must include encryption, audit rights and incident response obligations, and organizations should adopt compliance-focused governance frameworks that mandate model validation, documentation and independent testing. While these steps may slow some implementations, they reduce legal, financial and reputational risk and help ensure technology actually improves decision-making for borrowers and institutions.

– Avoid free tools for confidential data: Free consumer tools often lack enterprise-grade encryption, logging and contractual protections needed for sensitive borrower information.
– Verify outputs after demos: Vendor demonstrations can mask factual errors; independent validation and human review are necessary before production use.
– Operational and regulatory risks: Inaccurate outputs can lead to processing errors, borrower harm and increased regulatory scrutiny.
– Technical and testing controls: Use secure sandboxes, red-team exercises, human-in-the-loop checks and ongoing monitoring to catch degradation and hallucinations.
– Vendor/governance requirements: Demand transparency about model provenance and testing data, contractual safeguards and a compliance-focused governance framework with model validation and independent testing.

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