Calculating AI ROI in Mortgage: Strategies for Success

By Melanie Lewis

Artificial Intelligence (AI) has the power to transform mortgage operations—but without clear goals and measurable outcomes, it risks becoming an expensive solution. At PhoenixTeam, we’ve learned that innovation without a clear use case is just a hobby. And a use case without measurable value? That’s wasted potential.

The key to unlocking AI’s potential lies in a structured approach to evaluate, prioritize, and measure its impact. ROI isn’t just about dollars saved—it encompasses employee sentiment, efficiency gains, and other less quantifiable benefits. By focusing on outcomes and disciplined evaluation, we’ve developed a repeatable framework to turn AI’s promise into measurable results.

Start with the Big Question: Where’s the Value?

The first step in pursuing an AI project is aligning goals, processes, and measurable outcomes. One guiding framework we use is Gartner’s Defend, Extend, Upend model, which categorizes AI initiatives into three strategic portfolios:

• Defend: Strengthen and optimize existing operations.
• Extend: Build on current capabilities with incremental improvements or new tools.
• Upend: Disrupt the status quo by creating entirely new business models or approaches.

This framework ensures every AI initiative pursued is aligned to the organization’s strategic AI vision and addresses the right problems. For each use case, we ask: Are we protecting what works? Enhancing what’s already there? Or breaking the mold entirely? This approach helps identify high-impact opportunities and prioritize them effectively.

Align on Outcomes: Define Success from the Start

Before analyzing costs or ROI, it’s critical to establish a shared definition of success — both for the AI solution’s outcomes and the value/ROI analysis. Securing alignment and shared understanding on priorities early ensures that the analysis focuses on what matters most to stakeholders.

1. Clarify Goals: Is the objective to reduce costs, improve efficiency, or achieve qualitative outcomes like enhanced employee satisfaction?
2. Set Expectations: Agree on the level of detail required. Is a quick estimate sufficient, or is a deeper, data-driven analysis necessary?

For example, if an initiative reduces manual work and reallocation of those impacted resources is a goal, then there may be no need to present savings tied to a reduction in force. Similarly, if cost savings is the goal, understanding expense drivers becomes critical. Aligning on these priorities prevents missteps and keeps efforts focused where they’ll have the greatest impact.

Map the Current State: Build a Strong Foundation

With outcomes defined, the next step is mapping the current state. Understanding your organization’s existing processes, people, technology, and costs provides a baseline for evaluating AI’s potential impact. Key areas to assess include:

• Processes and People: What workflows are in place? Who’s involved, and what are they doing?
• Technology: What systems are used? Where are the limitations?
• Current Costs: What does it cost to operate today?

Begin by identifying inefficiencies and opportunities for improvement. Document existing systems to evaluate compatibility with potential AI solutions and determine how seamlessly new tools might integrate. Engaging subject matter experts (SMEs) to validate assumptions ensures that the final analysis is both accurate and credible. This process also refines reusable templates for future projects, saving time and effort.

Calculating ROI: A Scalable Template

Once the current state is mapped, the focus shifts to distilling findings into an actionable ROI analysis. Here’s how we approach it:

1. Net Cost Savings: What does it cost to do this process today? What will it cost post-AI implementation?
2. Cost and Benefit Breakdown: Factor in labor spend and savings, model training, infrastructure and maintenance, and potential market fluctuations over time.
3. Realistic Projections: Apply a 20% contingency buffer to account for inefficiencies and adoption hurdles.
4. Record Qualitative Feedback: Document notable comments from subject matter experts and stakeholders about the value of implementing AI solutions.

The result is a clear, actionable view of the potential ROI tailored to what matters most to the client—whether that’s cost savings, efficiency gains, or qualitative improvements.

But it doesn’t stop there. ROI analysis transitions seamlessly into action, starting with a proof of concept (POC) to validate assumptions in a controlled environment. Once tested, we compare POC results to initial projections and refine our approach, creating a repeatable, scalable process.

Practical AI for the Mortgage Industry

For mortgage companies, AI is more than a tool to streamline operations—it’s a catalyst for reimagining workflows. While ROI is a key focus, it’s important to understand that the first step is to define broader strategic goals, align stakeholders, and ensure the AI project is positioned for success. Only once these foundations are in place do we dive into the detailed ROI analysis. PhoenixTeam’s Practical Mortgage AI (PMAI) approach enables organizations to:

• Align on outcomes and define success metrics.
• Map workflows to uncover inefficiencies and identify opportunities for AI-driven improvement.
• Build scalable frameworks that ensure AI’s impact grows with the business.

By prioritizing tangible ROI, intangible benefits, and scalable processes, mortgage leaders can confidently pursue AI initiatives that drive measurable value. Whether defending current operations, extending capabilities, or upending the status quo, PhoenixTeam is here to help you unlock AI’s full potential—one measurable success at a time.

Ready to understand the ROI of your AI strategy? Reach out to learn more or join our AI for Mortgage Professionals course.