Corporate Contracts And AI: Opportunities And Challenges
- Indian Journal of Law and Legal Research
- Mar 6
- 1 min read
Dilip S John, LLM (Corporate and Commercial Laws), Christ Deemed to be University, Bangalore, India
ABSTRACT
The integration of Artificial Intelligence (AI) in corporate contract management is transforming business transactions by improving efficiency, accuracy, and cost-effectiveness. AI-powered tools, such as contract lifecycle management (CLM) systems, utilize natural language processing (NLP) and machine learning to automate contract drafting, negotiation, and enforcement. By reducing human errors and accelerating processes, AI helps companies optimize legal resources while ensuring regulatory compliance. Additionally, AI’s predictive capabilities enhance risk management by identifying potential disputes based on historical data, allowing businesses to mitigate legal uncertainties and enforce obligations effectively.
AI offers numerous advantages, including contract automation, enhanced risk assessment, and blockchain integration for smart contracts. AI-driven systems expedite contract creation, extract key clauses, and highlight discrepancies to ensure consistency. AI also improves risk management by analyzing past breaches and regulatory changes, predicting compliance risks, and detecting suspicious clauses. Integrating AI with blockchain technology enables self-executing smart contracts, ensuring transparency and security. Moreover, AI-assisted negotiations provide data-driven insights by analyzing industry standards and past negotiations, helping businesses achieve favorable contract terms.
However, AI adoption in contract management presents legal, ethical, and security challenges. Legal uncertainties raise concerns about liability in case of AI-driven misinterpretations or disputes, with unclear accountability between AI developers, corporations, or other parties. AI systems may also inherit biases from training data, leading to unfair contract terms or discriminatory outcomes. To address these issues, organizations must implement fairness-focused AI governance, continuous auditing, and bias mitigation strategies. Transparency and explainability remain critical, as AI models often function as "black boxes," complicating legal interpretation and litigation.
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