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MortgagePoint » Your Trusted Source for Mortgage Banking and Servicing News 32 February 2024 F E A T U R E S T O R Y Both assessments give you a clear picture of your organization's readiness for generative AI. Now, it's time to think about use cases. Step 2: Scope the Highest-Value AI Use Cases E very generative AI strategy must highlight specific use cases with the highest impact in the shortest time. One promising thing for many lenders is using generative AI as a virtual loan officer assistant. Imagine an AI-powered assistant that loan officers can ask to pull up specific loan documents, identify market trends, or distill complex regulations into simple explainers. Loan officers can use this chatbot while communicating with customers to complete otherwise manual tasks, deliver guidance faster, and serve more customers. Another high-value generative AI use case for lenders is a software devel- opment copilot. Picture conversational AI that can help software developers draft specifi- cations, write and debug code, and au- tomate application testing and deploy- ment—all in seconds. That accelerated workflow is something we call digital efficiency. With an experienced partner, lenders can build a custom AI copilot to drive faster software development, lower development costs, and shorter time- lines to launch new products or features. Those are just two use cases for a rapidly evolving technology. It's also possible to train AI models on internal datasets to develop dozens of other functionalities. One lender has already done that. It trained an AI chatbot on over 70,000 internal messages from an earlier bot iteration. The bot can answer basic mort- gage questions from potential customers. It also uses sentiment analysis to gauge when a human agent should step in (e.g., if a customer seems frustrated or a ques- tion is on the bot's "do not answer" list). As more generative AI use cases emerge, a partner can help you quickly build, test, and tweak proofs of concept to identify the most practical applica- tions for your organization. Step 3: Roadmap Your AI Implementation O nce you've identified a few genera- tive AI use cases, start mapping your implementation. The specifics will vary for every lender, of course. But your road- map should do two things: 1. Prioritize each generative AI use case by cost and impact. 2. Establish a timeline for implemen- tation. First, I recommend prioritizing use cases with the highest impact and lowest development and implementation effort and cost. In your AI readiness check, for in- stance, you might have learned that your customer service agents struggle to keep up with call volumes. Here, generative AI could deliver a significant increase in efficiency. With the help of a partner, you can gauge the cost and complexity of implementation (i.e., whether you need a custom system or an off-the-shelf tool). If you're leaning toward custom-built software, a partner can help you identify ways to lower costs (say, by contracting out the development work so internal teams can focus on their most essential projects). Next, build out your implementa- tion timeline. If you're in a downturn but expect volumes to stabilize in a few months, seize the moment to fast-track work on a new generative AI tool so you are ready to operate more efficiently when activity bounces back. With a robust AI roadmap, you can give your organization a structured way to gain from generative AI. The Best AI Strategy Is the One That Starts Now G enerative AI holds significant prom- ise for mortgage lenders. Market leaders have recognized this promise and are working earnestly to find applica- tions that will translate to meaningful product and service improvements. Such applications could position lead- ers to gain market share and boost profit margins when the market picks up. But that trajectory is anything but guaranteed. Lenders who act now to implement gen- erative AI solutions in their business will be best prepared to compete in this market cycle and beyond. Generative AI holds significant promise for mortgage lenders. Market leaders have recognized this promise and are working earnestly to find applications that will translate to meaningful product and service improvements.