AI AND IT’S APPLICATIONS IN MODERN-DAY MERGERS AND ACQUISTIONS

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By Mamoon Rashid, a 1^st year student at National Law University Odisha

INTRODUCTION

Disney’s acquisition of 21st-Century Fox was a landmark business deal. Finalized on March 20, 2019, this acquisition reshaped the media landscape. The question here is what made a company like Disney make this acquisition a part of their growth outlook. For this, we have to understand about Mergers and Acquisitions. M&A is a powerful tool to transform your business overnight. Every company has a burning desire to grow beyond its normal pace while managing to skip the major portion of the work required to do so. This can be achieved effectively by mergers and acquisitions. Due to their stategic and economic benefits mergers and acquisitions have become a growing trend among modern-day companies. The increasing popularity can be credited to the ability to enable rapid market share growth, geographic expansion, and mass diversification of a variety of products and services. Additionally, M&A can provide a competitive edge by reducing competition and increasing market dominance.

Mergers and Acquisitions, being highly advantageous, also necessitate the analysis of extensive data sets, the identification of concealed risks, and well-informed decision-making. The primary intent of the buyer is to either generate more revenue or reduce processing price by acquiring the target company. Such concepts are popularly known as synergies. Synergy is founded on the principle that the combination of both entities produces more valueable than if each operated independently. Bearing this in mind, the buyer conducts a meticulous analysis of all aspects of the company, ensuring a comprehensive understanding of the business. Such thorough knowledge and analysis are referred to as Due Diligence. Due Diligence can be classified into three categories based on the aspect it examines: Legal, Financial, and Commercial due diligence. To have all this information readily accessible, an automated thinking system is essential, offering human-like program execution and minimizing errors. This is where artificial intelligence enters the picture.

AI’s ROLE IN ANALYTICAL OPERATIONS

Artificial Intelligence, popularly known by its abbreviation ‘AI’ is the closest resemblance of a mechanized form of human thinking. AI works by analysing huge amounts of datasetsdrawn from Big Data and drawing conclusions from it. In its maximum efficiency, AI demonstrates eight capabilities, broadly categorized into two groups. The first category encompasses those already present in ready-to-use programs or those that depend on supervised or unsupervised machine learning. By analysing vast amounts of financial, operational, and market data through the use of machine learning algorithms, this system can prominently enhance the accuracy and pace of due diligence, far surpassing the capabilities of human analysts.

COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE WITH TRADITIONAL DUE DILIGENCE

Traditional due diligence is in a way dependent on undertakings that are time-consuming and prone to human error. It allows for a detailed and thorough examination of legal documents, contracts, compliance issues, intellectual property rights, litigation risks, and regulatory matters. Experienced legal professionals can identify nuanced issues and interpret complex legal language, effectively facilitating a comprehensive analysis of the Legal Due Diligence (LDD) factors of M&A.

This process while being highly customizable and tailored to the client’s needs, accompanies shortcomings. Time factors and human error are the prime hassles surmounting traditional analysis processes. Its labor-intensive and slow nature often gives rise to variance between analysts’ interpretations and judgments, which leads to inconsistencies in the subjectivity factor. Financial Due Diligence also plays an important role in exercising due diligence in the buyer’s conduct. Financial due diligence, conducted using traditional methods, is a comprehensive process that involves meticulously reviewing and analyzing the financial records and statements of a target company. This method relies heavily on manual scrutiny and verification of historical financial performance, assets, liabilities, cash flows, and accounting practices. The process includes evaluating the accuracy of financial statements, assessing compliance with relevant accounting standards, and identifying any potential financial risks or irregularities.

Traditional due diligence involves extensive document reviews, interviews wih key personnel, and often physical inspections of assets. It aims to provide a clear and accurate picture of the target company’s financial health, ensuring that investors or buyers who already have stocks or shares in the company, or are to become buyers in the future are fully informed before making any decisions. This method, while thorough, can be time-consuming and resource-intensive, but it remains a critical step in the merger and acquisition process to ensure informed and sound financial decisions.

AI brings to the table, that it can provide automation to document reviews and analysis, identifying relevant information and potential risks of a huge data set. For instance, in legal and financial due diligence, AI tools can scan thousands of documents to highlight pertinent clauses, detect anomalies, and flag potential compliance issues. Additionally, AI can extract and process data to identify risks associated with specific decisions or analyses, such as evaluating the financial health of a target company during an acquisition. By employing predictive analytics, AI can forecast potential future risks and suggest methods to mitigate them, such as diversifying investments or renegotiating terms.

AI’s capability to assist organizations in effective decision-making regrding Merger and Acquisitions is further enhanced by its ability to integrate and analyze data from diverse sources. To illustrate, AI is can considered useful in this example for enhancing the process of financial records merge, market trends and competitive analysis providing a full picture that helps top management make informed decisions. Besides, AI-driven sentiment analysis can analyze the public opinion of a possible acquisition by looking through social media and news articles, giving various types of feeling of the market.

COST OF PRODUCTION AND MAINTAINANCE

Regarding costs associated with these processes, AI helps manage the processing costs by significantly reducing the time and manpower required. Automation in data processing ensures that expenses are within the company’s budget, preventing cost overruns that could jeopardize the financial stability of the organization. AI systems can operate around the clock without fatigue, providing a continuous and efficient workflow that human employees alone could not sustain.

SHORTCOMINGS ASSOCIATED

However, artificial intelligence is not without its drawbacks, much like a medicine with an unpleasant aftertaste. A significant limitation lies in its constrained understanding of context. While AI can process vast quantities of information, it frequently lacks the nuanced comprehension that human analysts bring to their work. For instance, AI might misinterpret intricate legal language or fail to understand the subtleties of a contractual clause that an experienced counsel would readily grasp.

Concerns about security and confidentiality present further significant risks. AI systems, particularly those connected to the internet, are prone to cyberattacks. Unauthorized access to sensitive data during an acquisition can direct to leaks of confidential information, potentially harming the reputations and financial standings of the involved companies. Furthermore, the integration of AI systems often necessitates access to large datasets, which might include proprietary or personal information, raising concerns about data privacy and adherence to regulations such as General Data Protection and Regulation (GDPR).

Though it can provide insights based on data, it cannot substitute the intuition and experience of effective decision making. The final authority always lies within the buyer who in maximum cases is made out of flesh and blood. For example, in the final stages of negotiation in an acquisition, the ability to read body language, assess trustworthiness, and make instinctual decisions are crucial aspects that AI cannot replicate. This human element is vital while surfing the complexities and emotional nuances of high-stakes business dealings.

CONCLUSION

In summary, while artificial intelligence offers considerable benefits in automating document review, risk analysis, and cost management, it is crucial to recognize and address its limitations. These include its restricted contextual understanding, security vulnerabilities, and the absence of human judgment, which must be considered to fully harness its potential in organizational decision-making.

Referring to it as closest to human thinking is appropriate, as it lacks spontaneity and decision-making through critical reasoning, implying it cannot perform its tasks without analyzing data. However, when prompted efficiently, it can employ its maximum cognitive capabilities to determine the best solution from a given data set. Its ability to process vast amounts of information and identify patterns that enables it to emulate humanized thought processes, making it a precious tool in situations requiring detailed analysis and problem-solving, despite not possessing true spontaneity or independent reasoning.

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