While the first few years of the mortgage industry's relationship with artificial intelligence have been characterized by new gadgets and fear of missing out, 2026's themes might center on how companies manage to catch up with its game-changing potential.

If or when they do, the industry should start to see standardization and scalability that help it achieve long-talked about, but difficult-to-achieve, ambitions, including greater customer satisfaction, simplified underwriting and faster closings.

The businesses that can standardize certain processes with the assistance of AI will set themselves off from the pack, according to technology executives. The first hurdle that needs to be cleared for breakthroughs to happen, though, is to achieve widespread use within an organization. 

"What we're seeing is that it's the people that are setting the pace. It's not the tech that's setting the pace, because there's this huge gap between what we can actually do with generative AI and how the people are using it, and this gap is only going to grow as models get smarter," said Tela Mathias, managing partner and chief technology officer at mortgage consultancy firm Phoenixteam. 

"Finding the way that human beings who are ultimately sitting in the chair —  engaging with the homeowners, solving problems, uncovering and understanding — and optimizing that relationship with their everyday AI is going to be critical in 2026 because what we're seeing is that we can eat everything on the buffet," she added.

The cost of getting started with AI isn't as burdensome as it was with some past technologies, according to Prasad Kodibagkar, chief technology officer at mortgage industry law firm Polunsky Beitel Green and a former executive at Mr. Cooper and Wells Fargo Home Lending. While costs shouldn't be an impediment, a shortage of qualified users is also holding companies back, he concurred.

"It's not a skill set that's easily available," Kodibagkar said. "The price point, the entry point is very low, but that doesn't mean that you can just plug it in and start using it."

Although some of the barriers still present challenges, they are not keeping ambitious companies from capitalizing on the advances AI has already brought with it and setting lofty goals for themselves and their peers. 

The road to faster mortgage underwriting

Generative and agentic artificial intelligence are creating noticeable progress toward longtime goals in originations that have been discussed for years, according to Lower President Adam Wiener.

"Everyone is kind of poking around the edges of some really big breakthroughs," he said. "We all share this kind of dream of a digital mortgage that can be originated on demand, at a low cost with a delightful experience for both borrowers and originators."  

Even as progress has been made to shorten origination times over the past decade, the mortgage industry has served as a prime example of the adage: one step forward, two steps back. Over that same time period, the cost of loan production has actually increased. 

"I think now we're finally at a moment where technology can reverse that," Wiener said.

What is key to pushing the industry toward greater cost savings is the ability for AI to accurately "read" the full variety of documents needed during underwriting that past tools could not. Often saved in image or PDF forms, the documents related to credit, income and assets are continuing to be digitized, and AI has taken over or eliminated some of the mundane tasks once required to verify hard-to-decipher data.    

"The ability to process semistructured data that's stored in photos or documents or handwritten notes has just jumped up to the next level. As a result of that, you will be able to digitize essentially all of the borrower data in a structured way," Wiener said.

With the data at hand, an AI underwriting tool can help evaluate borrower assets with overlaid guidelines "and almost condition out that file like a pre-underwrite," said Jesse Lopez, vice president of process improvement at Mortgage Solutions Financial. 

"For those loan officers that are newer to the industry, I think AI can be a great supplement to structure loans," Lopez noted.

The insights AI can provide are only getting better, Wiener also said. 

"You get faster decision-making, clearer eligibility criteria, and just shorter-term times across the board, I think that's one kind of main trend that we're going to see in 2026," he said.

As development moves forward, borrowers and lenders should plan to start seeing expedited closing times, an aspect of the mortgage experience that has been criticized by customers for years.  

For "easy" loans with highly qualified buyers who can afford a 20% down payment, or refinances with strong underlying fundamentals, "you're going to see speeds that are extremely fast," Wiener predicted. 

"Call it sub 15 days for those kinds of originations on a regular basis."

Creating a document standard

What's also proved challenging for the mortgage industry in the past is the sheer number of changes, even minor tweaks, that could appear in a single document during the entire life of a loan. The changes may raise questions about which version is to be relied on when consulting it in the future. 

"We are supposed to make decisions at every facet of this based on what version of truth?" Kodibagkar asked, while adding that AI has a role to play that will bring about uniformity. 

Artificial intelligence is helping create a standard for particular documents in a single loan file, which is the type of Holy Grail goal mortgage development should aim for. "The tech is there," Kodibagkar added.  

The standardization and analysis AI can already produce, though, also stands to realign staff structure among lenders and brokerages, according to Lopez.

"There's enough technology out there, as it stands today, that you can put things in place using AI that can dramatically either reduce your processing staff or allow you scalability without having to hire more processing staff because you can take those mundane tasks off of their plate."

What's in store for servicing

AI's capabilities to analyze data quickly and create customized opportunities for the borrower during the origination process likewise carries over to servicing clients and their customers. 

Even before the surge of AI, servicing technology had evolved in recent years to move beyond proprietary systems that, in the beginning, ended up creating a high degree of technical debt. On the other hand, newer software easily integrates with AI and other platforms, according to Cornerstone Servicing President Toby Wells.

"It's not the box that you got five years ago — and that's it," he said. "There's constant enhancements, and that customization flexibility has improved per year and continues to improve."

The new approach to servicing system development makes any automation upgrades or enhancements, including AI, straightforward. "It allows you to customize and build your unique requirements on a client-by-client basis. That is a part of the system configuration, so it's more module-built systems as opposed to that loan accounting, mainframe-task oriented linear system," Wells explained. 

With the addition of AI to the process, the amount of customization that can be applied when working with borrowers is ramping up quickly. While tailoring solutions for servicing customers was already possible through existing automations that could implement customized waterfalls and other loss mitigation options, the data granularity available through artificial intelligence is adding new strategies to help borrowers.

AI's benefit to servicers appears first and foremost in call centers, and like in the rest of the mortgage industry, the tools are set to get better in 2026. Fast analysis of conversation transcriptions and summarization creates savings and better outcomes from investors, Wells noted.

With today's AI abilities, the amount of data a call agent has available to them helps provide clarity and possible scripting suggestions almost instantly, regardless of how complicated the borrower scenario. 

AI scripting assistance "moves you in the right direction," according to Wells.

"I think some of those ecosystems and tools will start leveraging AI fairly quickly to be able to identify topics to be discussed with a customer. A customer may call you about 'X,' and you want to answer that question, but there might be a series of things coming up that you want to touch base with that customer," he said. 

"At the end of the day, what a servicer really is, is a data company. We are moving large amounts of information."

As the mortgage industry moves beyond the foundational stage of AI development, companies will find what they might use it for today as just scratching the surface of its potential. 

The potential won't translate into results without an all-in approach from companies, though, Mathias said. Adoption needs to come from the top down to fully understand how game-changing AI is.

"I've never seen a technology before where it is so necessary for every level of the organization to have their fingers on the keyboards," Mathias said,

"What I see in 2026 is a much bigger push towards true workforce enablement and really getting under the covers with what it is that we have to do to enable the workforce to take advantage," she said.