How AI Contract Review Leverages Advanced Technologies to Expedite and Secure Your Deals

AI contract review is revolutionizing the way companies handle complex agreements by providing a fast, accurate, and efficient solution to the traditional manual contract review process. As noted in a blog post by Malbek, traditional contract review is time-consuming, with human lawyers spending an average of 92 minutes per contract, while AI can perform the same task in seconds. 

AI contract review leverages advanced technologies like natural language processing (NLP), machine learning (ML), optical character recognition (OCR), and generative AI (GenAI):

• NLP enables AI systems to understand complex legal language, identifying key elements such as parties involved, clauses, and terms and conditions. 
• Machine learning algorithms improve over time, recognizing patterns, flagging potential risks, and ensuring consistency across contracts. 
• OCR converts scanned documents into machine-readable text, facilitating comprehensive analysis. 
• Generative AI offers actionable insights by identifying balanced terms aligned with policies and legal standards, pinpointing unfavorable clauses for revision to expedite negotiations.

AI contract review software accelerates the review process by:

• Analyzing terms and conditions, ensuring financial clarity, flagging risks, and checking for consistency against reference documents. 
• Automating the setup of new contracts, simplifying clause identification, and supporting continuous learning and customization.
• Increasing efficiency and time savings, enhancing accuracy and consistency, and reducing human error. 
• Improving risk assessment and compliance and identifying potential risks and non-compliance issues. 
• Handling large volumes of contracts with ease using scalable AI tools.

As AI contract review systems require strong data protection measures, organizations should choose the right AI solution tailored to their specific requirements. Best practices for implementation include establishing clear policies, training AI systems with relevant data, promoting user adoption and feedback, maintaining data security, continuous monitoring and improvement, and ensuring human oversight and review.