Alchemist Solutions’ Alok Datta on Why the Industry Has Become More Skeptical About AI

Alok Datta is the founder and CEO of Alchemist Solutions, Frisco, Texas, an AI-driven technology and consulting firm that automates manual document, data and decisioning work by converting loan packages into structured, validated and underwriter-ready files.

MBA NewsLink: Why do you think mortgage companies have become more skeptical about AI?

Alok Datta: Because they’ve lived through the first wave and have had sub-par experiences.

Most lenders and servicers didn’t sit on the sidelines. They invested and piloted. In many cases, the technology looked impressive. But once those tools moved into real production environments, the results didn’t always match expectations. Instead of simpler operations, some tools created more exceptions and more oversight. That experience naturally led to skepticism.

I’d argue that’s not necessarily a negative; it indicates the industry is getting smarter about AI and what will actually work. Mortgage leaders now know what questions to ask. They’re not buying into the hype. They’re demanding proof.

MBA NewsLink: What created the gap between what AI promised and what it delivered?

Datta: It came down to misalignment and poor management of expectations.

A lot of early AI was general-purpose technology dropped into a highly regulated, exception-heavy environment. Mortgage operations don’t tolerate ambiguity. Accuracy, consistency and traceability matter every day. “Most of the time” isn’t good enough.

Tools that performed well in demos struggled with real loan volumes, investor overlays and compliance demands. When that happens, teams add manual checks and ROI disappears quickly.

There was also confusion around how success was measured. Vendors highlighted extraction rates or model accuracy. Mortgage leaders measure success differently, focusing on cycle times, defect rates, audit readiness and downstream data integrity.

And not all AI is equal. True AI in mortgage is not defined by how advanced it sounds. It is defined by what changes operationally. The solutions that succeed are designed specifically for mortgage workflows, with an understanding of how defects are introduced, how they surface later in QC or servicing, and where automation can reduce risk without compromising control.

Without that context, even technically sophisticated tools struggle in production.

The lesson is simple: technical sophistication isn’t enough. In mortgage, it has to be paired with deep domain understanding.

MBA NewsLink: What did the industry learn from that first wave?

Datta: That AI success isn’t about experimentation. It’s about operational integration.

Early on, many organizations treated AI like a proof of concept. If the model worked in isolation, the assumption was value would follow. But production is different. AI has to fit cleanly into workflows and eliminate friction. If it creates parallel processes or requires constant monitoring, adoption stalls.

Mortgage companies also learned that AI doesn’t fix broken processes. It magnifies them. Strong governance and clean data matter just as much as model performance.

ROI is now defined much more concretely. Leaders are asking: Does this shorten cycle times? Reduce defects? Improve audit readiness? Strengthen downstream data? That shift toward measurable impact has changed how decisions get made.

MBA NewsLink: Given those lessons, what should companies look for when evaluating AI solutions today?

Datta: Start with the operational impact. The first question shouldn’t be, “How advanced is your model?” It should be, “What operational issues are you solving, and how will we measure success?”

Look for solutions that truly reduce unnecessary manual touchpoints instead of shifting work elsewhere. In a regulated environment, transparency and auditability also matter. Teams need to understand how outputs are generated and validated.

Mortgage work is nuanced, and experience goes a long way. The more complex the process, whether in origination, QC, or servicing, the more valuable real-world domain knowledge becomes.

When AI is designed with those day-to-day lending and servicing realities in mind, you see the difference. QC cycles move faster because fewer files come back for rework. Defects decline because issues are identified earlier. Servicing data becomes cleaner, reducing friction during audits, transfers and investor reviews. That is what lenders and servicers care about: measurable operational improvement.

MBA NewsLink: What specific questions should lenders, servicers and investors ask potential AI vendors?

Datta: I’d focus on five areas.

First, measurable results: What operational impact have you delivered in environments like ours? Not theory, specifics.

Second, production performance: How does the system handle unusual or complex files? What level of oversight is required at scale?

Third, domain expertise: How well do you understand underwriting standards, QC frameworks, servicing transfers and investor overlays?

Fourth, transparency: How are decisions made? Can outputs be audited and traced?

And finally, integration and governance: How easily does this connect with existing systems, and how is performance monitored over time?

The strongest AI vendors combine technical capability with real mortgage fluency.

MBA NewsLink: You’ve said skepticism is healthy for the industry. Why?

Datta: Because it raises standards.

Volume will return. Regulatory expectations will remain high. In that environment, technology has to perform under pressure.

Skepticism forces leaders to define success and demand proof: faster cycles, lower defects and cleaner data. It pushes the market toward solutions that hold up in production, not just in presentations.

AI disillusionment may be real, but it does not signal retreat. It signals discernment. And discernment is what ensures innovation earns its place in core lending and servicing operations.

(Views expressed in this article do not necessarily reflect policies of the Mortgage Bankers Association, nor do they connote an MBA endorsement of a specific company, product or service. MBA NewsLink welcomes submissions from member firms. Inquiries can be sent to Editor Michael Tucker or Editorial Manager Anneliese Mahoney.)