AI vs. RPA: What Mortgage Leaders Need to Know Before Making the Switch
March 17, 2025
We get it. AI is the hot new thing, and everyone and their grandma (including us!) is promising that it will revolutionize your business. Meanwhile, you’ve got RPA bots breaking left and right every time an interface changes, and you’re wondering if AI is really the answer—or just another expensive headache.
In our prior article, we indicated that the Rocket-Redfin combination makes AI an imperative for all lenders. What do we mean by that? Well, Rocket Mortgage is already famous for using automation to speed up lending, and now with Redfin in the mix, the industry is seeing AI-driven customer experiences become the new standard. If lenders don’t start integrating AI into their workflows, they’ll be left behind as competitors use AI to streamline approvals, cut processing times, and offer a seamless digital experience.
So what’s it going to take? This article isn’t a vague “AI is the future” pitch. It’s a brutally honest breakdown of what AI-powered automation can actually do, how it stacks up against RPA, and what you need to know before jumping in.
Here’s the TL;DR:
RPA (Robotic Process Automation) is fine—until it breaks every time a UI changes or encounters unstructured data. AI-powered automation is smarter—handling documents, conversations, and complex decision-making—but it requires real engineering, ongoing monitoring, and compliance guardrails.
Bottom line: AI-powered automation isn’t a 1:1 replacement for RPA—it’s an entirely new approach. Make the switch strategically, or risk trading one automation headache for another.
AI vs. RPA: What’s the Difference, and Why Should You Care?
RPA (Robotic Process Automation) is a glorified macro recorder that follows rigid rules. If a task is structured, predictable, and never changes, RPA is fantastic. But the moment your workflow encounters unstructured data, unexpected variations, or any form of decision-making, RPA turns into a fragile, high-maintenance nightmare. Anyone running more than a few RPA bots likely knows this and has to deal with paying humans to watch their bots which diminishes the value proposition.
AI, on the other hand, isn’t just following instructions—it’s interpreting, predicting, and adapting. It can process handwritten forms, understand human language, and make decisions dynamically without breaking every time a minor change occurs.
Sounds amazing, right? Well, here’s the catch: AI isn’t as plug-and-play as RPA. It requires:
- API-driven architectures instead of screen-scraping hacks.
- More technical expertise. (Sorry, your low-code RPA developer might need an upgrade.)
- Ongoing monitoring. (AI can “drift” over time, making increasingly terrible decisions if left unchecked.)
The Business Case for AI-Powered Automation
Let’s say you’re in mortgage lending and using RPA to process loan applications. An RPA bot can extract structured data from a standard form and enter it into your system. For example purposes, let’s consider a use case Brimma automated via RPA: retrieve purchase advice statements from investors and validate them against the LOS data. If everything is valid, write some of the PA data into the LOS. But what happens when:
- There is some sort of mis-match between the PA data and the LOS?
- The investor site gets an upgrade and the user interface moves everything around?
RPA fails in these cases. AI, on the other hand, can:
- Attempt to determine “why” the mis-match exists. More importantly, if the mis-match follows a pattern, AI can understand the pattern without having to write IF-THEN-ELSE code.
- Understand that the intent is to get the PA and not get caught-up on the specific user interface layout.
The big business benefit? AI automates more processes, requires fewer updates, and scales better than RPA. But there’s a risk: AI’s decision-making isn’t always predictable. It generates probabilities, not certainties—which means it might hallucinate an answer or make an incorrect judgment if not designed with very specific guardrails to keep it in-line..
Don’t go into AI with rose-colored glasses. It is fraught with risks and you need partners that you can trust to ensure your success!
Technical Complexities: What AI Requires That RPA Doesn’t
With low-code RPA tools, a business analyst can set up a bot in weeks. AI? Not so much. At least, not yet. For those of us that have been in software long enough, we remember that the grandiose value of the Internet originally required us to hand-code HTML, CSS, and Javsacript. Once tools were built to streamline the creation of those components, the Internet was able to really start reaching its potential. Same thing for AI…there will be a lot of tool building required to help build faster than what is available today.
Today, to deploy AI-powered automation, you need:
- Custom API integrations (not just UI automation).
The GOOD NEWS: Companies like Brimma that have a deep repository of APIs for integrating into POSs/LOSs can leverage those in their AI agents. Why is this good news? First of all, because it means you don’t have to start from scratch to get the value of AI. More importantly, it keeps consistency with our operating premise: AI needs to meet users where they already work! - Prompt engineering (because AI doesn’t read your mind).
The GOOD NEWS: Prompt engineering and refinement does not create technical debt! Why is this good news? Because, whether you know it or not, a large part of what you pay for software is the software vendors cost to “fix” the technical debt load they have created. Think of it as interest being paid on a large debt…we should want to avoid that, right? - Model retraining and validation (because AI can drift if left unchecked).
The GOOD NEWS: If the appropriate guardrails are applied from the start, models will not be able to drift. Why is this good news? Because this concept of drift (or AI hallucinations) is completely avoidable. Despite the fear-mongering, good AI design can make this a non-issue!
Unlike RPA bots, which require hardcoded responses, an AI chatbot learns dynamically. That’s a huge advantage—but also a huge risk if you don’t monitor it properly.
Make sure your technology partners already has APIs for integrating into your core systems or else you’re probably going to pay to build their platform for them!
Compliance & Regulatory Risks: AI’s Biggest Challenge
Regulators love clear, explainable, rule-based decisions—which is why they’re not thrilled about AI’s tendency to make predictions without clear justification.
Example: If an AI-powered underwriting model denies a mortgage application, how do you explain why?
- Did it flag a low credit score?
- Was it due to income instability?
- Or did the AI just inherit biases from past approvals?
This is where explainable AI (XAI) comes in. Companies adopting AI must implement explainability tools to justify decisions—because “the AI said so” won’t hold up in court. This will be an interesting space to watch because many will to to simply have AI explain itself as a part of its job (e.g. Underwrite this loan and give me every rule and calculation you applied to reach your conclusions. This will work but is also has some potentially fatal flaws. We have our own secret sauce for providing explainability…we’ve been perfecting it with a self-employment calculator we’ve been working on.
Meanwhile, RPA remains fully auditable. If an RPA bot denies a loan, you can track exactly which rule was triggered. AI? Not so easy—unless you have a solid AI governance framework.
But compliance isn’t just about explainability—it’s also about bias. AI models trained on historical data can unintentionally inherit discriminatory patterns, which can lead to regulatory issues. That’s why lenders adopting AI need bias detection frameworks, regular audits, and diverse training datasets to ensure fair lending decisions. Otherwise, AI could reinforce systemic inequalities rather than solving them.
Make sure your technology partners have an explicit plan to deal with explainability and bias!
DEEP DIVE: Three Sample AI Governance Frameworks
- NIST AI Risk Management Framework (AI RMF)
Developed by the National Institute of Standards and Technology (NIST), this framework aims to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. - Unified Control Framework (UCF)
Proposed by Eisenberg et al., the UCF integrates risk management and regulatory compliance through a unified set of controls, aiming to provide efficient, adaptable governance that scales across regulations while offering concrete implementation guidance. - Nine Principles of an AI Governance Framework
Outlined by Duality Technologies, this framework emphasizes principles such as explainability, accountability, safety, security, transparency, and fairness to guide the ethical development and application of AI technologies.
Future-Proofing AI Automation: Avoiding the Next Big Tech Headache
If you’re thinking ahead and realize that the biggest benefit is to be able to scale AI automation, you must avoid the classic problem that has plagued previously technologies: a complex set of interdependencies between different AI agents that ultimately causes many AIs to fail when one fails. Again, this is where (a) more code rather than less will be required in the near-term, and (b) it helps to work with a software company like Brimma that has the experience to know how to avoid such problems.
- Keep it Modular (so one update doesn’t break the entire system).
- Use AI to Protect Your AI (so that each AI agent knows how to protect itself if another AI agent misbehaves). For example, if your AI scores a borrower’s credit (or assets, or income,…), you should maintain a rolling historical average. Any significant deviation should be flagged for model analysis.
- Integrated with fallback mechanisms (so failures don’t cause full-blown disasters).
It’s OK to start your AI journey without focusing too heavily on these issues, but an ounce of foresight will save you from pounding of your head on a desk later.
Final Thought: Your First Step Toward AI Success
Transitioning from RPA to AI isn’t a simple swap—it’s a fundamental shift in how automation works.
- If you need chatbot technology, you should buy something “off the shelf”. That technology is already relatively mature and it is unlikely you need to spend a lot of money on something expensive.
- Know that it is going to take some time for the software “tools” to catch-up with what AI has already made possible. The early adopters will take advantage of APIs and automations they have already built that can be adapted to AI.
- If you already have RPA, start replacing high-maintenance bots with AI-powered alternatives.
- If you need a partner, find one that truly understands AI and knows how to help you put in the governance that will help you sleep at night (not just an RPA reseller pretending to do AI).
AI is already here. The only question is whether you’re using it correctly—or setting yourself up for another automation disaster. 🚀
Learn more about Brimma’s capabilities and its Vallia family of solutions at our website.
