Technology And Automation In Corporate Transactions

INTRODUCTION

From very long time, corporate transactions have been designed and structured with human interventions and innovations whether it is due diligence, negotiation, risk assessment or execution of strategies. Within a corporate environment, human judgement and analytical skills have always played a significant role in transactional governance and decision-making process, as well as in ensuring the smooth functioning of the operations within the entity. However, with the rapid integration and growth of technology into transactional practices, the traditional human centric approach is taking a transformative shift towards an automation-centric approach. Corporate deal-making is facing a profound transformation in the mediating role of lawyers, to the machine-mediated execution. Artificial Intelligence driven contracts, predictive valuation models, and rule based transactional mechanisms are becoming more integrated into the corporate areas of mergers and acquisitions, transfer of shares, and complex financing structures. On the one hand, these systems are expected and turning out to be efficient, quick, and cost effective, but, at the same time, they interfere with the legal principles and accountability frameworks within which corporate transactions are established and operated.

The introduction of AI and automation tools in corporate deals raises a significant concern between automation and the legal framework. There is a growing use of automated systems at decisive transactional areas. Any failure of such systems has not only technical but judicial consequences. The specification of roles, responsibilities and liabilities are opaque within the automative systems. Indian corporate and contract law, nevertheless, is still based on the concept of intention, consent and fault which presuppose human agency. This brings about a regulatory discrepancy with transactions being carried out in machine logic and legal responsibility still based on anthropocentric assumptions. The current literature review on AI in corporate law has mostly dealt with technology related capabilities, ethical risks, or compliance automation, and not enough has done so in regard to how the transactional risk and responsibility is re-allocated with AI systems involved in deal execution

In this article we have tried to address this gap by examining the following questions:-

1. Whether AI can be given legally binding transactional roles?
2. How liability should be applicable in AI-driven corporate transactions?
3. Whether Indian legal framework is institutionally prepared to regulate such developments?

APPLICATION OF AI IN DEAL PIPELINES

Artificial Intelligence is rapidly transforming the mode of conducting corporate transactions especially in due diligence and post transaction compliance. Earlier, deal team used to take weeks to sift through thousands of documents, financial statements and disclosures manually. The emergence of AI-based tools has facilitated this process through the analysis of massive volumes of data within a short period of time and identifying the potential risks that would otherwise go undetected. One notable application is the "red-flag analytics", where AI software is trained to analyse contracts, disclosure schedules, litigation history, and financial statements in identifying complex or high-risk clauses in minutes. These tools enable lawyers and transaction advisors to devote more time to strategic analysis, as opposed to routine documents review. This significantly accelerates due diligence in mergers, acquisitions, and investment transaction while enhancing the level of risk assessment.

AI is also beneficial in "contract summarisation and monitoring of post transaction continuous compliance". The large scale of transaction bundles can be processed using Natural Language Processing (NLP) models and structured summaries of essential contractual terms can be generated. AI can identify clauses that deal with liabilities, termination rights, exclusivity provisions, and the payment structure of a large deal containing share purchase agreements, shareholders agreement and vendor contracts. This allows deal teams to gain a fast comprehension of complicated documentation, and can sustain consistency among various agreements, minimizing the duration of review and the potential risk of being overlooked. Furthermore, AI systems can continuously monitor financial covenants, regulatory filings, and ESG commitments, and can also provide timely alerts to the management and legal departments to potential violations and deviations. However, regardless of these efficiencies, there have been legal issues raised about the reliability, possible biasness, and excessive dependency on automated systems specially where such outputs hold high-value transactional decisions.

SMART CONTRACTING ECOSYSTEMS

AI-driven smart contracting ecosystems represents the future of corporate transactions where AI and blockchain together works in a way which creates secure, intelligent and flexible end to end automated workflows within the computerized systems. Automated closings allow transaction platforms to perform transactions like release of payments, transfer of shares and execution of a document when specified conditions have been met. This minimizes administrative sluggishness and reliance on intermediaries and enables the transactions to be closed more effectively.¹ These systems play an essential role especially in managing the complex transactions that require huge coordination with regards to numerous approvals and documents.

Smart contracts may also be conditional in nature, i.e. certain contractual functions are executed upon occurrence of certain predefined events, e.g. regulatory approvals, milestone achievements or escrow release conditions. Such automation will ensure transparency in obligations and will significantly reduce chances of delays or human error. However, they should be governed in terms of "Information Technology Act, 2000" and "Indian Contract Act, 1872" in India. ² The practical legal solution would be to consider the coded system not as a contract itself but as a technological mechanism for performing a legally binding agreement between the parties. Provided the essential requirements of valid contract such as consent, lawful consideration, and intention to create legal relations are satisfied, the underlining contract may be recognized by the Indian courts despite where parties may be liable towards errors in coding or implementation of the agreement. This approach would maintain the contractual liability while simultaneously fostering innovation in the transactional practices. Therefore, it is imperative to understand that AI alone cannot be granted an autonomous legally binding position in corporate transactions, rather it acts as a technological agent under the control and directions of human or corporate entities. The legally binding force is derived from the prior human consent and the aforesaid predefined contractual conditions.

AI DRIVEN CORPORATE GOVERNANCE FRAMEWORK

AI Driven Corporate Governance Framework reflects the structural integration of artificial intelligence into corporate data driven decision-making, compliance mechanisms and oversight systems.

A. Algorithmic Oversight

Corporate boards are increasingly relying on AI-driven dashboards to monitor operational risks, compliance failures, and transactional anomalies in real time. These systems aggregate large datasets across departments and generate predictive insights, enabling directors to identify governance red flags before escalation. This shift transforms board oversight from periodic review to continuous monitoring and consequently, strengthening enterprise's risk management frameworks. ³ However, it raises important concerns regarding directors' fiduciary duties, particularly with regards to the extent to which dependence on automated systems satisfies the duty of care and independent – informed decisions under corporate governance standards.

B. Fraud Detection

AI-based pattern recognition tools are widely deployed in procurement processes, financial transfers, and monitoring insider activities. By analysing behavioural and transactional patterns, such systems can detect irregularities that may escape human scrutiny and traditional audit processes. The outcome is real-time alerts and early risk detection, reducing financial losses and reputational damage. Nevertheless, automated fraud detection mechanisms must be balanced with procedural safeguards to address concerns regarding algorithmic bias and due process within internal investigation procedures.

C. Internal Controls

The integration of AI within enterprise governance frameworks enhances audit trails, improves managerial accountability, and enables automated compliance monitoring. This strengthens internal control mechanisms and accuracy of regulatory disclosures. From a legal and regulatory perspective, oversight by the Securities and Exchange Board of India (SEBI) and the Ministry of Corporate Affairs (MCA) becomes critical in defining standards for technological reliance, disclosure norms, and board responsibility when automated systems influence corporate decisions.

TOKENISED EQUITY AND DIGITAL ASSET FINANCING

Tokenised equity refers to the representation of shares or underlying assets as digital tokens recorded on distributed ledger technology (DLT) systems. These tokens can encode ownership rights, transfer restrictions, and compliance conditions directly into their programmed architecture through smart contracts.⁴ Applications include startup financing, fractional ownership models, enhanced private market liquidity, and asset-backed instruments.⁵ By reducing intermediaries and enabling faster settlement processes, tokenisation promises increased transparency and operational efficiency. It can democratise access to investment opportunities while improving capital formation mechanisms.

However, significant legal and regulatory challenges persist in India. A central issue concerns classification of securities specifically, whether tokenised instruments fall under the ambit of definition of "securities" under existing laws i.e. "Securities Contracts (Regulation) Act, 1956" and related frameworks administered by the SEBI.⁶ Additional concerns also persist such as regarding cross-border investment rules, custody mechanisms for digital assets, and investor protection standards. Regulatory clarity from both the RBI and SEBI is essential to prevent regulatory arbitrage and ensure systemic stability. Without a coherent framework, tokenised financing may remain legally uncertain despite its technological promise.

MAPPING AI ACCOUNTABILITY

A central concern in AI-powered corporate governance relates to liability for automated decisions. When AI systems generate incorrect risk assessments, execute flawed transactions, or cause financial harm, the key legal question is who bears this responsibility? Potential liable actors may include the corporate entity deploying the system, the third-party developers designing the algorithm, and the managerial personnel authorising its implementation. Reliance on algorithm outputs does not impede the directors' owed duty of care to act with reasonable diligence and independent judgement. AI systems cannot act as decision making substitutes capable of shielding boards from their owed responsibility and liability. In light of this, the concept of "human-in-the-loop" governance seeks to address this challenge by mandating human supervision in critical decision-making processes.⁷

Emerging regulatory approaches increasingly emphasise algorithmic transparency, auditability, and explainability as foundational safeguards. In India, developments such as the "Digital Personal Data Protection Act, 2023" highlight accountability principles in automated data processing. Globally, AI governance frameworks are shifting toward risk-based regulation, documentation obligations, and impact assessments. For Indian corporate law, the task lies in reconciling traditional doctrines of liability and fiduciary responsibility with technologically mediated decision-making systems.

CONCLUSION

Artificial Intelligence (AI) has the potential to significantly reshape corporate transactions, governance mechanisms, and capital markets. While automation offers efficiency, speed, and predictive capabilities, it simultaneously raises significant legal issues. The effective integration of AI into corporate systems requires legal certainty, clearly defined accountability frameworks, and proactive regulatory oversight. India currently stands at a formative stage in addressing these developments. The evolving approach of regulators and legislative bodies will determine whether innovation can coexist with institutional safeguards.

Following this model, the future of AI-based corporate governance in India will depend on the capacity of legal institutions to remain adaptable and flexible without stifling innovation. A balanced regulatory approach will be necessary to maintain the technological advancement while strengthening fiduciary accountability and market integrity. Subject to principled supervision and the institutional coordination, AI can enhance corporate governance ensuring that improvements in efficiency do not undermine transparency, responsibility, accountability and investors' confidence.

Footnotes

1. IBM, " What are smart contracts on blockchain?", https://www.ibm.com/think/topics/smart-contracts

2. Bhavna, "The Future of Digital Contracts and Smart Contracts in Corporate Law", Vintage Legal, April 24, 2025

3. RTS Labs, "AI in Due Diligence – What It Is & How It's Transforming M&A (2025)", July 09, 2025

4. Ledger, "What Are Tokenized Equities? A Comprehensive Guide", January 29, 2026

5. RWA, "Private Equity Tokenization: Secondary Liquidity", December 09, 2026

6. Utkarsh Mehrotrat, "Cryptocurrency: A Regulatory Conundrum", SCC Online, November 03, 2021

7. Secoda, "What is Human-in-the-Loop Governance", March 27, 2025

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