The Promise and Possibility of AI
Before I provide a synopsis of the panel discussion titled “The Promise and Possibility of AI”, I’d like to share some observations from this year’s MBA Annual 2024 conference.
This year’s conference was well attended, but as with the past two years (I started attending the MBA in 2022 in Nashville), only approximately 35% of attendees were mortgage lenders or banks, the rest were all vendors (Full Disclosure: I was one of them).
And it seemed that every other vendor booth had “AI” or “Automation” in their marketing materials, making it clear that the mortgage automation market is crowded with players, whether they deliver anything of substance or not. Regardless, it is increasingly difficult to stand out.
However, from speaking with some of the larger service providers, lenders did come by and ask about automation and digital transformation, and when asked who else they met with, the answer was that these particular lenders gravitated to either the larger, better-known technology players in the market, or they met with smaller vendors that had a more complete product portfolio, and didn’t focus on providing just a single point solution e.g., a Pre-Qual solution, but nothing downstream in the process. The focus on either an end-to-end provider, or a larger scale provider was to alleviate the concern that lenders would end up having to manage multiple vendors, as opposed to only one when it came to AI and Automation solutions.
Perfect for MOZAIQ, as we offer one of the few end-to-end, next-gen automation platforms specifically built for the backend mortgage fulfillment process that delivers a tangible ROI. But I digress.
One of the many and more interesting AI-focused panels was hosted by Laura Escobar, 2025 MBA Chair and President of Lennar Mortgage. The panel was comprised of Olivia Peterson from AWS and David Tepoorten from NVIDIA (bio links are at the end of the blog post).
How Not To Deploy AI
I almost walked out of the session, though, when an “AI-bot” was used to introduce the panel. . . a case study in how not to leverage AI. A robotic, lifeless, monotone voice reading from a script. But the session quickly recovered and. . . wait, there’s a better way to do this.
I’ll share the edited summary that ChatGPT created from my detailed notes and save myself some time. Here it is:
How To Deploy AI
The panel discussion highlighted the distinctions between traditional AI and generative AI (Gen-AI), emphasizing that AI is not a new concept, with companies like Amazon leveraging machine learning since 2010. As we know, traditional AI focuses on data-driven decision-making, while Gen-AI enhances learning by integrating past recommendations with new training data.
The collaboration between AWS and NVIDIA since 2010 has been pivotal in advancing AI technologies, particularly in weather prediction through innovations like NVIDIA’s digital twin, Earth 2—the video demonstration was fascinating. This simulation model allows scientists to forecast weather patterns with improved accuracy, which has significant implications for industries like insurance that struggle with climate-related risks. Can you imagine the benefits if these models can accurately predict the weather beyond the current ten-day cycle? Sign me up! (I added the last two sentences). Likewise, AWS utilizes data from sources like NOAA to integrate climate risk into financial models, further enhancing decision-making for creditors and insurers.
In the mortgage sector, the discussion focused on improving efficiencies through intelligent document processing (IDP), with companies like PennyMac using AWS to streamline document indexing and data ingestion, extraction, and validation. This use case wasn’t that revelatory, as MOZAIQ and a plethora of other companies already provide this “table stakes” functionality, the foundation required for downstream mortgage process automation e.g., Pre-Underwriting, Appraisal Review, Post Close Audit, CD Prep, and Loan Delivery to name a few. The importance of quality data in training AI models was underscored, alongside the necessity of establishing privacy protections and guardrails prior to model deployment. You can read my blog on Responsible AI here.
The panel discussed the potential of digital agents in call centers to enhance employee training and improve customer service. These “buddy agents” could help new hires navigate customer queries more effectively, reducing handle times and minimizing repeat calls by providing on-the-job training and summarizing interactions. Additionally, digital agents can today summarize agent-customer conversations (saving time and money for the call center operator and enhancing customer service), and in the future could interpret customer sentiment more accurately than novice employees, augmenting overall customer engagement. The conversation highlighted the importance of maintaining a balance between AI assistance and human oversight, emphasizing that users must ultimately act on the recommendations provided by these technologies, and not the AI engine.
Lastly, the panel addressed the trustworthiness of AI technologies from a regulatory perspective, noting that regulators are increasingly adopting these technologies themselves. Janet Yellen, the US Treasury Secretary (and a former Professor of mine at the HaaS School of Business at Cal Berkeley—Go Bears) emphasized the importance of understanding risks and implementing AI responsibly, warning that failure to adopt AI would put an institution at risk.
And that’s how to deploy AI.
The panelists:
- Laura Escobar – 2025 MBA Chair; President, Lennar Mortgage
- Olivia Peterson – Director, Worldwide Public Sector Financial Services, AWS
- David Tepoorten – Director, Global Systems Integrator Partnerships, NVIDIA
Full disclosure: This blog post was created by running my copious notes through ChatGPT and then editing the output. Why not use AI to write about AI? It’s the future. And I’ve embraced it.
