ARTIFICIAL INTELLIGENCE IN MODERN CORPORATE LEGAL DISPUTES: APPLICATIONS, CHALLENGES AND THE PATH FORWARD

AUTHOR – AKSHAT SINGH* & DR. KAVYA CHANDEL**

* STUDENT AT AMITY LAW SCHOOL LUCKNOW, AMITY UNIVERSITY UTTAR PRADESH LUCKNOW CAMPUS

** ASSISTANT PROFESSOR OF LAW AT AMITY LAW SCHOOL LUCKNOW, AMITY UNIVERSITY UTTAR PRADESH LUCKNOW CAMPUS

BEST CITATION – AKSHAT SINGH* & DR. KAVYA CHANDEL, ARTIFICIAL INTELLIGENCE IN MODERN CORPORATE LEGAL DISPUTES: APPLICATIONS, CHALLENGES AND THE PATH FORWARD INDIAN JOURNAL OF LEGAL REVIEW (IJLR), 6 (4) OF 2026, PG. 370-376, APIS – 3920 – 0001 & ISSN – 2583-2344.

Abstract

The rapid integration of artificial intelligence into corporate legal practice constitutes one of the most consequential transformations in the administration of justice in recent decades. This paper offers a systematic examination of how AI driven technologies , including machine learning, natural language processing, predictive analytics, automated contract analysis, and online dispute resolution platforms  are reshaping the full spectrum of corporate legal activity, from transactional due diligence to high stakes commercial litigation and regulatory compliance management.

The study traces the historical trajectory of AI in law, from the rudimentary rule based expert systems of the late twentieth century to the sophisticated deep learning architectures that today predict judicial outcomes with statistically significant accuracy. It then examines five key application domains: AI assisted legal research and case law analysis; automated contract review and risk identification; predictive analytics and psychometric profiling in litigation strategy; AI powered mediation, arbitration, and online dispute resolution; and regulatory compliance monitoring. For each domain, the paper draws on empirical evidence and institutional case studies  including JPMorgan Chase’s Contract Intelligence (COiN) platform, Baker McKenzie’s deployment of Lex Machina, and eBay’s Modria powered dispute resolution system to assess where AI delivers genuine value and where deployment remains premature or ethically problematic.

A substantial portion of the paper interrogates the principal ethical and regulatory challenges attending AI integration: algorithmic bias arising from historically inequitable training data; the opacity of deep learning models and its incompatibility with professional transparency obligations; data confidentiality risks when privileged communications are processed at scale through third party infrastructure; and the unresolved questions of professional liability when AI influenced decisions produce harmful outcomes. The paper then conducts a comparative survey across the United States, the European Union, China, Australia, Sub-Saharan Africa, and India, revealing a wide spectrum of regulatory approaches from China’s ambitious judicial AI programme to the EU’s structured risk based AI Act governance framework.