AI USE CASE
Intelligent Contract Analysis and Risk Scoring
Automatically extract clauses, flag non-standard terms, and score risk across contract portfolios.
What it is
NLP and generative AI scan incoming and existing contracts to identify key clauses, deviation from standard templates, and potential legal or commercial risks. Risk scoring surfaces high-priority issues before they escalate, reducing exposure on large contract portfolios.
Data you need
A corpus of existing contracts in digital (text-extractable PDF or Word) format, ideally tagged with contract type and counterparty.
Required systems
- erp
- project management
- none
Why it works
- Involve senior legal counsel early to validate clause taxonomy and risk-scoring thresholds before rollout.
- Start with a single contract type (e.g. NDAs or supplier agreements) to prove value quickly and build team trust.
- Maintain a human-in-the-loop review step for contracts flagged as high-risk, preserving legal accountability.
- Establish a feedback loop where reviewers correct AI errors to continuously retrain and improve the model.
How this goes wrong
- Model misclassifies jurisdiction-specific legal language, producing false-safe risk scores that lull reviewers into complacency.
- Contracts stored as scanned images or non-searchable PDFs prevent text extraction, stalling the pipeline before any AI runs.
- Legal teams distrust AI output and revert to manual review, nullifying efficiency gains without a change-management programme.
- Scope creep into multi-language portfolios without adequate multilingual model coverage degrades accuracy significantly.
When NOT to do this
Do not deploy contract AI when your contract repository is fragmented across email inboxes and local drives with no consistent naming or versioning, the data preparation cost will exceed the AI benefit.
Example vendors
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