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Mortgage AI does not become useful just because leadership approves it. Copperlane co-founders Athan Zhang and Brianna Lin argue that lenders have to win loan officers’ day-to-day adoption by fitting technology into established workflows—and then measure use and work reduced separately from business results.
Why leadership approval is only the starting point
In a HousingWire interview published October 2, 2026, Copperlane COO and co-founder Brianna Lin identified “driving adoption with LOs” as a major challenge for lenders. The founders’ point is practical: a tool that is approved centrally but ignored by loan officers will not change daily loan work.
Loan officers already have routines, preferred processors, and tools they rely on. Lin recommends bringing technology in so it “doesn’t disrupt how they currently run their own workflows.” That is Copperlane’s advice from its experience with its own product, not independent evidence that a particular implementation method guarantees return on investment.
Make adoption part of implementation
Introducing a tool takes more than granting access. Zhang said Copperlane is “very hands-on in the beginning” of many processes to help adoption happen. For a lender assessing an implementation, the useful questions are who will help staff use the tool, how it fits into existing responsibilities, and what support continues after launch.
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- Map the task the tool is meant to change and the people who currently perform it.
- Show loan officers where the tool fits into their established workflow, including what it hands back to them.
- Identify who supports initial use and how staff can escalate an incorrect or incomplete result.
- Check whether completed work moves into the loan origination system (LOS), or whether staff must re-enter it.
Measure use and work reduction without confusing them with ROI
Lin said Copperlane looks at “how many human touches we can reduce per loan.” Zhang added that if loan officers are using the product, “that tells us something is going well.” These are adoption indicators: they can help a lender see whether a tool is being used and whether it appears to reduce manual steps. They do not, on their own, establish better loan quality, compliance, borrower outcomes, lower costs, or a positive return on investment.
Lin also said, “You need to be 95% automated or more in order for the automation to be actually useful.” That is her stated view, not a validated threshold for mortgage AI generally. A lender should define useful performance for the particular task and assess business outcomes separately from usage and workflow activity.
Ask what the AI actually does
Copperlane’s vendor-authored guide distinguishes three labels: a chatbot answers questions, a copilot drafts or assists and hands work back to a person, and an agent can persist across sessions, take actions, and route tasks. Definitions vary across vendors, so evaluate demonstrated capabilities rather than relying on the label.
| What to evaluate | What to ask or observe |
|---|---|
| Workflow fit | Can loan officers use it alongside their established routines? |
| Action versus assistance | Does it answer, draft for a person, or execute and follow through on tasks? |
| Persistence | Can it retain context across sessions and follow up when a borrower’s task stalls? |
| LOS integration | Can validated work be routed to the appropriate condition, or must staff re-enter it? |
| Human authority | Which decisions remain with staff, particularly final credit decisions? |
Copperlane’s guide proposes asking vendors to demonstrate a multi-step task from beginning to end. In a demo, ask what happens after a borrower goes quiet, how documents are validated and routed, where the system connects to the LOS, and when it hands judgment to staff. This is Copperlane’s suggested evaluation framework, not a neutral industry standard.
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Why scaling remains difficult for mortgage lenders
HousingWire’s September 28, 2026 report on a joint AARMR, MBA, and BCG survey found a gap between AI use in production and fully scaled deployment. The survey included 31 residential mortgage lenders and servicers, representing about 40% of the U.S. mortgage market, and was conducted from April through July 2026. HousingWire reported that respondents had about 10 of 38 assessed use cases in production on average, while about 25% had fully scaled at least one use case.
Among respondents, the most common production uses were employee writing and summarization (87%), code generation and developer support (65%), document data extraction (61%), and agent assistance and knowledge search (54%). These are survey results as reported by HousingWire, not independently checked here against the underlying report.
The same report said respondents cited regulatory and compliance uncertainty (59%), unclear ROI (45%), and AI reliability or hallucinations (21%) as barriers to scaling. It also reported that 87% had written AI policies and standards, 84% privacy controls, 81% human review, and 58% ongoing monitoring. Those figures suggest that having governance measures in place does not mean every organization has moved from limited production use to broad deployment.
Quick Recap
A practical way to assess a proposed rollout
- Choose a specific task. Define the loan-work step the tool is expected to assist or automate, rather than evaluating “AI” as a general capability.
- Walk through the real workflow. Have the vendor demonstrate the task end to end, including borrower follow-up, document handling, routing, and handoffs to staff.
- Check the system connection and decision boundary. Confirm what enters the LOS and what work or judgment remains with a person.
- Plan for adoption. Decide how loan officers will learn the workflow, where support comes from, and how feedback or errors will be handled.
- Track distinct measures. Monitor actual user activity and human touches per loan as adoption indicators, then evaluate the lender’s defined business outcomes separately.
- Account for governance. Assess privacy, human review, vendor controls, reliability, and ongoing monitoring alongside workflow fit.
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