AI Tools Have Become Standard in Title Insurance. So, Why Are So Many Companies Still Getting It Wrong?

The automation conversation in the title insurance industry has never been louder. But louder does not mean clearer. As artificial intelligence tools become standard talking points at every conference and in every vendor pitch, a troubling gap has emerged between what title company executives expect from AI and what it can realistically deliver today.

Jimmy Lewis, co-founder of TrueFocus Automation, has spent the better part of a decade automating title and mortgage workflows. With more than 800 bots developed and more than 16 million automated tasks completed for clients across the industry, he has a front-row seat to where expectations and reality diverge.

The expectation driving most of the confusion, Lewis says, is that automation should handle everything from start to finish without any human involvement. People in title feel like if they are deploying automation it should be able to do everything end-to-end, he says. And it doesnt do that. For many processes, you still need a human in the loop.

That single misconception, he argues, is causing companies to either over-invest in solutions that cannot deliver, or walk away from automation entirely after one disappointing experience.

The 100 Percent Completion Myth

Many title executives picture a fully hands-off process: work goes in, completed files come out, and staff are freed up or reassigned entirely. The reality is considerably more nuanced.

Most mature automation implementations in the title industry reach somewhere between 85 and 95 percent completion rates on automated workflows. The remaining fraction – exception items, files where data does not match expected formats, or documents presenting an unusual variation – still requires a human to review and resolve.

The mistake is treating that gap as a failure. A process that previously consumed a full-time employee and now requires 10 percent of their attention has still delivered substantial value. But if the expectation was zero human involvement, the entire implementation gets written off as underperforming.

Lewis draws a direct parallel to skilled trade apprenticeships. Automation handles the groundwork – the repetitive retrieval, the data entry, the document bundling. The experienced professional comes in at the end to review, apply judgment, and sign off. That division of labor is not a design flaw. It is the intended model.

Cost Assumptions Are Blocking Adoption

Title companies have heard enough about enterprise software rollouts that consumed hundreds of thousands of dollars and delivered little to associate automation with that same level of financial exposure. For targeted, process-specific automation, the numbers look very different.

TrueFocus Automations average bot build runs approximately $9,500, with annual maintenance costs in the $8,000 to $10,000 range. For any process that consumes at least half a full-time employees working hours, those figures typically produce a return on investment within the first year, and often within a few months. As soon as they hear tech, AI, and automation, theyre thinking this could get into the hundreds of thousands, Lewis says. We want to get people to see the benefit, but also the return on investment, so theyre not scared off from trying.

The vendors quoting those larger figures are typically automation generalists – firms that approach each engagement as a complete operational overhaul rather than a focused solution to a specific workflow problem. Domain expertise changes the economics entirely. When a team already understands title production systems, the document types, the exception patterns, and the production environment, they do not need months of discovery before writing a single line of code.

When AI Makes Sense and When RPA Is Enough

Beyond the cost conversation, a more technical misconception is spreading through the industry: that AI and RPA (robotic process automation) are interchangeable. They are not.

Lewis maintains an internal framework that distinguishes which processes belong in each category. RPA handles highly structured, rules-based work: navigating a production system, retrieving a document, and populating fields from a known source. The inputs and outputs are predictable, and the logic does not need to interpret anything.

AI enters the picture when interpretation is required. Document understanding, for example, means extracting relevant data from contracts that arrive in 21 different formats rather than one, or identifying which version of a purchase agreement a file contains before routing it correctly. These tasks involve variations that RPA alone cannot handle cleanly.

A Florida-based client illustrates the distinction well. When the client first automated new order creation from incoming purchase contracts, the workflow handled seven or eight contract variants. Over time, as different real estate boards and brokerages standardized their own forms, the number grew to more than 21. RPA had been the right tool at the start. Layering in AI-powered document understanding extended the automations reach across the full range of contracts the client was actually receiving.

The Staffing Conversation Nobody Wants to Have

The version of the automation pitch that leads with job elimination tends to go badly. Frontline staff grow defensive, managers become reluctant sponsors, and projects stall in internal review. The more productive framing focuses on capacity rather than headcount.

A title processor completing 5 to 8 files per day, with automation handling data retrieval, document preparation, and order entry, can often double that output. The person is still essential, but their role just looks different.

Lewis notes that the fear around AI and jobs has not disappeared, but it has shifted. Three years ago, the question was whether automation would eliminate positions outright. Now the more common question is how to ensure staff adopt the tools rather than resist them. When teams realize their jobs are not at risk, they tend to become the strongest advocates for expanding automation to other parts of the workflow.

Starting Small, Scaling Smart

For title companies considering their first automation project, Lewis recommends identifying the process already causing the most friction: the one that slows down the first step of every new order, produces the most errors, or relies on a single person whose absence leaves the whole team scrambling.

Starting with a focused, high-frequency process produces visible results quickly and builds internal confidence for the next project. The companies that have expanded to 30 or 40 bots across their operations did not get there by automating everything at once. They started with one problem, saw a clear return, and repeated the process.

That pattern, not any specific technology, separates organizations that realize genuine value from automation from those that remain stuck debating whether to try it at all.

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Jimmy Lewis is the co-founder of TrueFocus Automation, a specialist in RPA and AI-driven workflow automation for the title insurance, mortgage, and real estate industries. TrueFocus has developed 840+ automation bots supporting more than 2,500 workflows and has returned over 1.3 million production hours to clients.

This article is based on information provided by the expert source cited above. It is intended for general informational purposes only and does not constitute legal, financial, or real estate advice. Readers should conduct their own research and consult qualified professionals before making any real estate or financial decisions.

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