Radian's playbook for AI: Governance, growth, and operational excellence
Briefly

Radian's playbook for AI: Governance, growth, and operational excellence
"Behind the hype of generative AI lies a tougher reality for mortgage and real estate leaders: adoption is anything but easy. Compliance hurdles, hidden costs, and the risk of bias make success dependent on clarity, discipline, and the right expertise. Radian has spent years laying the groundwork with data science and automation, giving the company a head start in scaling AI responsibly. Steve Gaenzler, SVP of Innovation and Strategy, reveals how Radian turns disruption into a competitive advantagebalancing innovation with trust, governance, and long-term value."
"The capability of AI to be transformative is with little comparison in history. Few advances or discoveries have been met with such fanfare and high expectation. However, adoption can be far more complex than it appears, with barriers that grow as these models advance rapidly. The challenges are multifaceted, encompassing legal and compliance constraints, skills-based learning and educational needs, regulatory expectations for public companies, and operational hurdles."
"Bias is another concern, and as humans play a potentially smaller role, it becomes more difficult to ensure compliance, validation and responsible use. Finally, cost is often misunderstood. While AI tools may seem inexpensive, production environments can quickly reveal the opposite. Companies must make deliberate decisions about where and how they want to engage with AI, striking a balance between opportunity, risk, and expense."
Radian invested years in data science and automation to create a foundation for responsibly scaling generative AI across mortgage and real estate operations. Adoption faces legal, compliance, and regulatory constraints, plus operational hurdles and skills gaps that complicate deployment. Bias risk increases as human oversight declines, making validation and governance essential. Cost in production can exceed initial expectations, requiring deliberate choices about where to engage AI. Effective scaling depends on clarity of purpose, disciplined governance, talent and expertise, and balancing innovation with trust, compliance, and long-term business value.
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