Open-source models have become remarkably capable. Cloud OCR is inexpensive. Building an AI proof of concept has never been easier. Which has led many mortgage lenders to ask: Should we build our own AI system?
After researching the real costs of enterprise AI, organizations are going to need to be able to not only build it, but operate, maintain, govern, and continuously improve it on a continuous basis. That’s a completely different investment.
We put together some key questions mortgage lenders need to ask themselves before making the decision to work with a vendor or build in house.
- Is building mortgage AI really cheaper than buying an enterprise platform?
- Not necessarily. The visible costs of a DIY approach are only part of the investment. Production AI also requires engineering, integration, testing, monitoring, security, governance, exception management and ongoing maintenance. If you need more explanation, our ebook looks at the total cost of ownership, including the costs that often don’t appear in the initial build estimate.
- If today’s AI models are so capable, why not just build internally?
- Modern LLMs have dramatically lowered the barrier to creating a proof of concept. But a tech demo is not the same as a working system. The harder work is building the infrastructure around the model like document processing, validation, workflow orchestration, auditability, exception handling, and integrations, all while maintaining all of it as models, documents and requirements change. The model may be easier to build around. The enterprise system is not.
- How should lenders think about build vs. buy if they already have a strong engineering team?
- Is building and continuously operating mortgage AI is the best use of those resources? Our eBook provides a decision framework for evaluating where proprietary development creates real competitive differentiation and where an enterprise platform may provide a faster, lower-risk path to production.
- What costs are most often overlooked when lenders decide to build?
- Ongoing engineering is one of the biggest. Production systems require continuous prompt and model evaluation, document taxonomy management, exception workflows, integrations, monitoring, security, compliance and user support. The system is never finished. The initial development budget tells you what it costs to launch. It doesn’t necessarily tell you what it costs to own.
- Is this really a build or buy decision?
- Increasingly, no. Most sophisticated lenders will likely do both. The more useful question is what should you build, what should you buy, and how should the two work together? Capabilities that create proprietary competitive advantage may justify internal development. Mature infrastructure that every lender needs may make more sense to consume through an enterprise platform. Our ebook explores the tradeoffs of DIY, enterprise and hybrid approaches.
- How should we measure whether our AI investment is actually working?
- Accuracy alone won’t tell you whether AI is removing work. A system can achieve high accuracy and still require employees to verify nearly every output. That’s why our ebook looks at Manual Review Rate (MRR) alongside accuracy. It looks at how much human work can safely be removed from the process. That ultimately has a greater impact on cost per loan, throughput and ROI.
- What changes when AI moves from a pilot into production?
- Almost everything around the model becomes more important. Production introduces uptime expectations, security, monitoring, auditability, governance, change management, integration dependencies and real users making real decisions from the output. That’s the gap our ebook touches on: getting AI to work is one challenge; getting an enterprise to depend on it is another.
- What’s the biggest question leadership should answer before deciding to build?
- Are you evaluating the cost of building the technology or the responsibility of owning it? Building means taking responsibility for the system beyond launch: its performance, infrastructure, security, governance, integrations, maintenance and continuous improvement. Our ebook gives technology, operations and executive teams a framework for deciding when taking on that responsibility creates strategic value, and when it doesn’t.
Our latest research explores how much time and monetary investments are required for enterprise AI projects, from workflow orchestration and exception management to compliance, monitoring, adoption, and ongoing engineering.
If your company is evaluating its AI strategy, this guide provides a practical framework for making that decision.
