One of the most common themes in the mortgage industry is pointing out the inability to adopt technology. It’s a complaint from almost everyone. But as soon as ChatGPT went mainstream this year, our industry (like everyone else) entered the AI hype cycle. Today is a good day to ask the question: why is it different this time?
Certainly, AI is as transformative for work as the Internet is for communications. As a result, it is easy to assume that our companies and partners will all seize the opportunity to leverage the benefits of on-demand answers to virtually any question through large language models and/or the ability to contextualize information, data and documents through agentic AI. If you saw the second internet coming, you would jump at the chance to be at the forefront too, right?
However, when you look beyond the hype, AI adoption begins to look more and more like the adoption of any other industry advancement.
Fannie Mae’s roadmap for eClosing adoption was first printed on a three-ring binder and distributed on a VHS (I’ve seen it!) in the late 1990s, and we’re still grappling with eNote adoption in 2025. A similar story is playing out in document recording, direct source data collection, and assessment modernization. While Plaid and similar technology may not be an AI-level innovation, it can achieve what OCR never could. Yet we have spent hundreds of millions (billions?) on OCR and cannot yet work effectively with direct bank or asset data.
Now AI appears to be claiming it can provide a solution to challenges spanning the mortgage ecosystem. Some argue that the result will be the same as OCR: money spent without much to show for it. Most claim that AI will revolutionize our business.
If we accept that premise, AI will revolutionize the mortgage industry. How quickly can we expect adoption, followed by results and ROI?
Here are the three things I think will determine whether we see adoption and ROI for AI:
1. Know your customer. Product-market fit requires clarity and honesty about who the customer is and how your solution solves their pain point. As Google puts it, “eliminate the toil.” Most mortgage companies serve the mortgage professional (MLO or mortgage broker) or referral source (online lead generation, in the case of consumer direct). AI confuses the issue by claiming it makes it easier for the customer, but often refers to the consumer.
With most mortgage providers, the consumer is not the customer. When determining how to evaluate AI, be clear about who it serves and how that fits into your specific strategy and roadmap. Buying consumer-facing AI won’t deliver ROI if everything else in your business serves the mortgage professional.
2. Delegate to technology. Leaders understand delegation. I bet everyone in our organizations would love to be able to delegate more of their work to a trusted colleague or team member. Whether you implement AI or not, this should be a question you ask yourself in your daily activities: What can I offload for the highest returns in time and efficiency? With AI capabilities, the answer keeps coming back as more and more of our work can be done; However, technology partners using AI will never have the opportunity to prove this without a delegation plan.
At the same time, it is expensive to maintain both an AI workforce and a human workforce that performs separate tasks simultaneously. We know that people like to delegate to people and resist relying on technology. Don’t add AI without a corresponding reduction and expect ROI. Plan the overlap and scale down accordingly, or just know that the time has come to decide where to save and how much.
One caveat: another way to achieve real ROI is growth. Moving people from simple, repetitive tasks to more complex work is another way to delegate to AI and grow your revenue at the same time. Example: Moving operations staff from the Fannie & Freddie Refi workflow to a new apartment product or renovation product. AI can reduce costs AND enable product expansion, if your business is built for change management.
3. Opt-out versus opt-in (also called standard to digital). One of the most powerful tools for adoption is changing the standard. For example, a company that wants to expand the adoption of single-source data collection (i.e. link bank accounts to a mortgage application) can set the primary task on the digital application portal to log into bank accounts using an income and asset verification tool. Documents will only be accepted if the consumer proactively opts out of an alternative PDF upload.
Thanks to AI, our companies are rethinking the way Ops and CX work together to achieve better results. The largest digital lenders were already doing this five years ago before AI smoothed the roadmap. Consider where you can explore opt-out-first processes in your CX and in team member setup functions.
AI for AI’s sake is expensive and probably no more efficient than what you do today.
To believe that AI can drive adoption, a company must identify the customer who will use the tool, create a delegation plan to get that user to actually try the tool, and remove any obstacle or choice (to the extent possible) to return to the old way.
If you think AI will actually deliver on the adoption and cost-saving promises that technologists have been making for years, you need to answer the adoption question. Will it be different this time?
Jeremy Potter is the founder of Next Belt Strategies.
This column does not necessarily reflect the opinion of HousingWire’s editorial staff and its owners. To contact the editor responsible for this piece: [email protected].
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