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AI exposure report

Will AI replace Title Examiners, Abstractors, and Searchers?

Many of your tasks are within reach of AI. The role will change, so it pays to act now.

Task exposure to today's AI

58/100

High exposure

This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.

Get your personal score

Will AI replace Title Examiners, Abstractors, and Searchers?

Not entirely. Today's AI tools can take on about 58% of the typical work in this occupation, mostly: examine individual titles to determine if restrictions, such as delinquent taxes, will affect titles and limit property use. The parts that stay human include: Confer with realtors, lending institution personnel, buyers, sellers, contractors, surveyors, and courthouse personnel to exchange title-related information or to resolve problems.; Examine documentation such as mortgages, liens, judgments, easements, plat books, maps, contracts, and agreements to verify factors such as properties' legal descriptions, ownership, or restrictions.; Prepare and issue title commitments and title insurance policies, based on information compiled from title searches..

People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.

Which tasks AI can take on

The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.

TaskAutomatable
Examine individual titles to determine if restrictions, such as delinquent taxes, will affect titles and limit property use. 70%
Copy or summarize recorded documents, such as mortgages, trust deeds, and contracts, that affect property titles. 70%
Enter into record-keeping systems appropriate data needed to create new title records or to update existing ones. 68%
Prepare reports describing any title encumbrances encountered during searching activities and outlining actions needed to clear titles. 62%
Examine documentation such as mortgages, liens, judgments, easements, plat books, maps, contracts, and agreements to verify factors such as properties' legal descriptions, ownership, or restrictions. 57%
Confer with realtors, lending institution personnel, buyers, sellers, contractors, surveyors, and courthouse personnel to exchange title-related information or to resolve problems. 17%

+ 10 more tasks (38% of the week) in the full test.

Your human edge

  • Confer with realtors, lending institution personnel, buyers, sellers, contractors, surveyors, and courthouse personnel to exchange title-related information or to resolve problems.
  • Examine documentation such as mortgages, liens, judgments, easements, plat books, maps, contracts, and agreements to verify factors such as properties' legal descriptions, ownership, or restrictions.
  • Prepare and issue title commitments and title insurance policies, based on information compiled from title searches.

AI tools to try

Legal AI tools (e.g. Harvey, Legora)ChatGPT / Claude for document analysisContract review and comparison tools

Related roles

A 3-phase AI learning plan

Phase 1

AI for documents

  • Contract and document review with AI.
  • Confidentiality: what may go into which tool.
  • Always check citations and sources.

Project

Use AI to review 10 redacted deeds or liens for legal description inconsistencies, then verify every flagged item against the recorder's index.

Phase 2

Research and drafting

  • Legal research with AI, verified in official sources.
  • First drafts of memos, letters and templates.
  • Summaries of long case files.

Project

Draft 5 title encumbrance report summaries with AI from anonymized document excerpts, then verify every claim against official recorded sources.

Phase 3

Legal ops

  • Document automation and templates.
  • Knowledge management for your team or firm.
  • Pricing services when AI changes the hours.

Project

Build an AI-assisted template for standard title search reports and clear-title letters, then measure hours saved across 10 past files.
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