Core Module
AI & Modern Tools
Modern tools can save real time on review work, if you adopt them deliberately. Know what each one is good for, and keep a human accountable for every decision.
What the tools do well
Large Language Models (LLMs)
Tools like Claude and GPT can summarize long records, draft correspondence, extract structured fields from narrative text, and surface questions worth investigating. They are fast and flexible, but they can be confidently wrong, so every consequential output needs a human check.
Optical Character Recognition (OCR)
Turns scanned documents and photographs of paper files into searchable, machine-readable text. OCR is how a banker's box of paper becomes a dataset you can query, though messy scans still need a careful review.
Natural Language Processing (NLP)
Finds patterns across large volumes of text: recurring names, similar fact patterns, sentiment, or topic clusters. Useful for spotting connections across cases that no one would catch reading files one at a time.
Bias-detection machine learning
Machine learning can flag where outcomes differ by race, geography, or other factors, helping you test whether a process is fair. Treat its output as a prompt for investigation, not a verdict, and watch for bias in the tool itself.
The non-negotiable rule
AI assists, humans decide. Validate outputs, document which tool touched which case, and never feed sensitive data into a tool you have not vetted.
A quick gut check
Before you reach for a tool, run the situation through three questions.
High-volume, repetitive task?
A good fit for automation. Let the tool do the first pass and review the results.
Serious consequences without human review?
Add oversight before you deploy. Keep a person in the loop for anything that affects a case.
Data leaves your control?
Check the privacy terms first. Know where it goes, who can see it, and whether it trains a model.
Vetting a vendor
Before you trust a tool with case-related data, get clear answers on five things.
- Data handling. Where does your data go, who can see it, is it used for training, and can you delete it?
- Accuracy and validation. How accurate is the tool on data like yours, and how was that measured?
- Transparency. Can the vendor explain how it works and where it tends to fail?
- Oversight. Does the workflow keep a human in the loop for consequential decisions?
- Exit rights. Can you export your data and leave without penalty if it does not work out?