Artificial intelligence is gaining traction in title and escrow operations, with industry commentary highlighting its potential to simplify workflows and improve efficiency. Recent analysis from outside studies has noted growing optimism about AI-driven tools among title and escrow professionals, particularly around performance gains in routine processing.
DataTrace Title Chief Data Officer Annette Cotton told Connect CRE that the most immediate impact is coming from rules-based automation built on clearly defined logic and standardized procedures. She said automation is well suited to tasks that involve managing inconsistencies and organizing data at scale.
Cotton pointed to repetitive activities such as manual data gathering, document retrieval, running name variations and pulling indices as areas where automation is already cutting cycle times. She added that standardizing these tasks can reduce errors and variability, producing fewer missed steps, more reliable audit trails and earlier identification of discrepancies across files.
For title agents, Cotton said these improvements show up as greater consistency from file to file. For underwriters, automation can free up capacity to concentrate on evaluating risk rather than assembling data, helping keep expert time focused on higher-value analysis rather than routine processing.
However, Cotton emphasized that deeper AI performance is constrained by the quality and structure of underlying property records. She noted that title data is fragmented across counties, with differing indexing practices, formats and levels of digitization, which complicates efforts to build fully automated decisioning.
She explained that insurable title decisions require data that is standardized across sources, validated for accuracy and linked at the property level, with enough completeness to surface gaps, broken chains and missing relationships. When records are incomplete, unverified or unstructured, AI tools are limited in what they can reliably do.
According to Cotton, current technology cannot interpret legal intent, resolve complex title defects, make underwriting determinations or reliably assess whether discrepancies impact ownership rights or insurability. AI also struggles with conflicting information and potential fraud issues, where expert interpretation remains essential.
For title companies looking to adopt AI, Cotton recommended starting with a thorough understanding of data sources and building strong rules-based processes before layering on more advanced tools. She advised defining business rules, setting escalation triggers and clearly specifying when human review is required.
Cotton also underscored the importance of guardrails that pause or stop automated workflows when data falls short of defined standards, noting that without these controls, automation can amplify errors instead of reducing them. By contrast, firms that invest in robust data infrastructure and automation can see gains in speed and consistency, processing more transactions while reducing error rates and variability.
Looking ahead, Cotton expects more repetitive title tasks, including document retrieval, name matching, address validation and rules-based checks, to be automated over the next five years. She said human professionals will continue to be needed to interpret data, resolve inconsistencies and make final underwriting calls, describing the future as a balanced model in which technology handles structured work and experienced practitioners focus on judgment, risk and accountability.
