Title examination has always been a discipline grounded in experience, judgment, and attention to detail. While the volume and complexity of public records continue to increase, the fundamental requirement remains unchanged: accurate interpretation by qualified professionals.

Artificial intelligence is not replacing title examiners—it is enhancing how they work.

The Challenge of Modern Title Review

Traditional title examination relies on manual review of extensive document sets, often spanning decades. Examiners must locate relevant instruments, identify ownership chains, evaluate encumbrances, and flag potential defects—all under increasing time and cost pressures.

Key challenges include:

• Growing record volume across digital and legacy systems

• Inconsistent formatting and indexing of public records

• Time-intensive preliminary document review

• Risk of human fatigue during repetitive analysis

These challenges make preliminary review an ideal candidate for intelligent automation.

How AI Supports Preliminary Title Examination

Machine learning tools can rapidly analyze large collections of recorded documents to assist examiners during the early stages of review. When properly implemented, AI functions as a force multiplier, not a decision-maker.

AI-enabled systems can:

• Identify and classify document types (deeds, liens, easements, mortgages)

• Extract key metadata such as grantor/grantee, legal descriptions, and recording dates

• Flag potential gaps, overlaps, or anomalies in chains of title

• Surface documents most likely to require senior-level analysis

This automation significantly reduces the time spent on document sorting and initial scanning.

Preserving Senior Analytical Judgment

While AI excels at pattern recognition and data extraction, title examination ultimately depends on contextual understanding and legal interpretation. Automated tools cannot assess intent, jurisdiction-specific nuances, or complex legal implications.

Senior examiners remain responsible for:

• Interpreting ambiguous conveyances

• Evaluating curative requirements

• Assessing risk tolerance for acquisition or underwriting decisions

• Applying jurisdictional and transactional context

In this model, AI delivers efficiency—humans deliver accountability.