We started with discovery sessions across various Berkadia teams, such as mortgage bankers and loan servicing, to understand how AI could improve daily tasks. Key findings included:
• Overwhelmed by data: Employees struggled to find meaningful insights.
• Repetitive tasks: Many tasks could be automated to save time.
• Slow AI adoption: While there was interest in predictive analytics, unfamiliarity with AI slowed down adoption.

With these insights, we developed and launched an MVP in just one month. The AI tool included:
• Automated Responses: Instant answers to routine questions.
• Knowledge Retrieval: Quick access to relevant information from various sources.
• Data Standardization: Enhanced accuracy and consistency of responses. • Engagement Tracking: Built-in analytics to monitor usage and gather feedback.

Post-launch, engagement metrics revealed:
• High adoption of the automated Q&A feature for routine tasks.
• A need for refining the accuracy and relevance of knowledge retrieval.
• User requests for deeper integration with existing tools and platforms.

These insights confirmed the MVP’s promise and provided direction for further iterations.

With initial feedback in hand, we moved into the iteration phase, where we made targeted improvements to the platform. We prioritized enhancements based on employee usage patterns and areas of friction:
1. Data Research Assistant for Investment advisors
2. Guide assistant for Mortgage Bankers
3. Loan/Lease document Analysis for Loan Servicing team

Usability improvements:  
• Improved User Interface:
Easy AccessSimplify the interfaceDark/light mode for better readabilityOptimum text input fields
• Accuracy of AI Insights: We refined the lead generation algorithms, making them more sophisticated by incorporating more data points and refining the machine learning models.

Before developing the full MVP, we created lo-fi (low-fidelity) vision designs to explore and test initial ideas with users. This phase was critical for refining our understanding of user needs and aligning on the most valuable features. We gathered feedback through interactive sessions, focusing on validating key functionalities and usability. Insights from these tests helped prioritize features that would have the greatest impact on users’ daily tasks.

ChallengesThrough a user-centered design process and iterative development, Berkadia’s AI platform is transforming the way the company operates, optimizing property valuations, improving lead generation, and integrating data insights.
• Increased Efficiency: Employees spend less time on repetitive tasks like manual data analysis, enabling more focus on strategic decision-making.
• Enhanced Collaboration: AI-driven insights empower teams with better data, improving cross-department collaboration.
• Cultural Shift: AI adoption has fostered a data-driven culture, with employees increasingly relying on AI for daily decision-making.
One of the key challenges is balancing a team with more developers than designers.
To address this, we prioritize high-impact usability improvements, ensuring the design team focuses on features that deliver the most value. Meanwhile, we trust the development team to implement smaller features efficiently.

As we look to the future, ongoing iteration, user engagement, and collaboration will be essential as we explore new opportunities, including generative AI, to keep Berkadia at the forefront of innovation in CRE.