Written by: Nandita Lal , LL.M(Corporate and Commercial Law)- Damodaram Sanjivayya National Law University, Visakhapatnam , B.A.LL.B- Department of Law, University of Calcutta.
Abstract
AI algorithms are transforming M&A by analyzing vast data acting as a dealbreaker and creating opportunities across financials, regulations, culture fit, product pipelines, and more. This data-driven approach significantly enhances decision-making and negotiating power. However, human oversight remains crucial to address AI driven work to ensure data quality, and apply contextual judgment. The future of M&A requires synergizing cutting-edge AI capabilities with expert human experience for optimal deal outcomes.
Introduction
The world of mergers and acquisitions is changing constantly. This change has now reached a point where AI (Artificial Intelligence) is being involved in M & A. As corporations depend more on data-driven insights to guide their M&A strategies, AI is taking the spotlight in identifying potential roadblocks and making life-changing decisions.
In past, for M&A, most focus was on financial aspects, regulatory barriers and cultural fit. However, the new generation of dealbreakers can detect hidden risks buried under mountains of data that surround potential deals.
This transformation is being done by AI systems that can consume and analyse vast amounts of structured or unstructured information including things from financial statements to legal papers, social media dialogues and industry trends. Such systems recognize patterns while bringing to light anomalies leading to insights that would be practically impossible for human analysts to find out without huge investment of time and resources. In a case of Manipur High Court, Zakir Hussain, 36, was “disengaged” from his district’s Village Defence Force (VDF) in January 2021, after an alleged criminal escaped from the police station while Hussain was on duty. He never received a copy of the order dismissing him.
After Hussain approached the Manipur High Court challenging his dismissal, Justice A Guneshwar Sharma, in December 2023, directed the police to submit an affidavit detailing the procedure for “disengagement of VDF personnel”. But the affidavit submitted was found wanting, and did not explain what the VDF was. This “compelled” the court to use ChatGPT for further research.
This revolution of AI in M&A covers all aspects of the process, from due diligence and target evaluation to post-merger integration and synergy realization. For example, currently AI algorithms are being used to assess among other things a target company’s product pipeline, patent portfolio, supply chain vulnerabilities, cyber risk exposure among others that could determine whether or not a deal goes through.
AI is driving the future of M&A beyond only identifying reasons for deals breaking down. These complex systems also have the power to simulate different M&A possibilities by running millions of them so as to predict possible outcomes and risks. This data-driven approach enables dealmakers to have unparalleled insights which in turn puts him/her in better position for negotiations during the whole lifecycle of an M&A.
The Rise of AI in M&A
AI algorithms have the capability to look at vast amounts of data, which can be analyzed to identify potential targets using complex criteria such as financial performance, trends in the market and even social media sentiment.
Due diligence is becoming more efficient and comprehensive with the help of AI. For example, algorithms can meticulously go through stacks of legal documents, contracts and financial statements thereby aiding in identification of any red flags or areas requiring further investigation. For example, a North American–based company in the consumer-packaged-goods industry used McKinsey’s proprietary tool DealScan.AI to search and evaluate potential investments. First, the tool identified approximately 1,600 viable targets according to initial prompts. Then, it applied bespoke quantitative and qualitative prioritization criteria, including whether there was a direct-to-consumer operating model, information about subscription-based product assortments, and details about recent fundings. This led to the prioritization of 40 targets—most of which the company had not considered before—that matched all requirements.
Before AI, valuation in M&A or other valuation required field was largely based on human judgment.
However, AI has the ability to analyze historical M&A data and market trends thereby providing more accurate and objective valuations for a target company. This is because it eliminates human bias thus enabling both sides to begin negotiations from a common understanding of what constitutes fair value. Comparing the process of M&A from human based work to how productive AI is, AI excels in most of the tasks leading to a smoother Integration Planning as Merging two entities can be a complicated process. AI can study information so as to make predictions regarding likely integration hurdles while recommending ways of dealing with them. This tends to result into smooth changes that increase chances for successful post-merger results.
Need of Human Touch
However, it is important to have a human perspective involved in M&A processes as even the most sophisticated AI algorithms are only tools that need appropriate guidance and control.
Sarah Johnson, an experienced lawyer in M&A warns us saying “AI can be a really useful tool but not when it comes to major decisions such as mergers and acquisitions”. She adds that “the algorithms might tell you when not to go ahead with some deal but you still need human beings to fully appreciate the environment under which these deals are made.”
Another danger of over-reliance on AI technology is potential bias and error being perpetuated. It is possible for AI models to effectively institutionalize human prejudices inherent within training data. Similarly, some AI systems operate like black boxes hence their decision-making processes can hardly be understood. Dr Michael Lee who researches on AI ethics says “We really have to watch for algorithmic bias and lack of transparency.”
Till now, AI algorithms are only as good as the data they are trained on. In the case of Gramophone Company of India Ltd. v. Super Cassettes Industries Ltd. (2011)[i], the Delhi High Court determined that AI-generated music produced by a computer program lacks human creativity and, therefore, is ineligible for copyright protection. This case clarifies the copyrightability of AI-generated content in India.
The Future of M&A
This article encompasses Dealbreakers 2.0 as a way of identifying better deals and making more informed decisions, thus increasing the success rate of mergers and acquisitions (M&A). Further, there is an expected advancement in AI that could enhance analysis of financial data as well as qualitative considerations such as employee sentiments and company culture. This enables M&A people to have an in depth understanding about the companies, they are engaging in leading to better post-merger performance and more strategic partnerships.
The future of M&A combines human intuition with cutting-edge AI. In the rapidly changing world of dealmaking, it will be only those businesses who can adapt themselves to this new reality that will succeed.
Conclusion
The era of artificial intelligence-driven “Dealbreakers 2.0” is causing a wave within the appraisal, negotiation and finalization of mergers and acquisitions. AI algorithms have shifted deal breaking factors beyond just financial metrics and regulatory hurdles. These sophisticated systems through which they’ve collected structured and unstructured data can unravel hidden risks as well as lucrative ventures that human analysts cannot afford to miss.
What constitutes a dealbreaker in multibillion-dollar deals such as assessing cultural fit through communications analysis; modeling complex scenario outcomes, scrutinizing product pipelines, cyber vulnerabilities have been redefined by AI. The borders of due diligence have been reshaped by machine learning capabilities that identify anomalies from large datasets to surface insights that were previously inconceivable.
Nevertheless, this revolution propelled by AI does not go without checks and controls. Issues around algorithmic bias, data quality concerns and the black box type models are reminders that human governance is indispensable. Besides their contextual wisdom, strategic vision and ethical judgment seasoned M&A pros must fortify AI outputs with their experiences.
Reference
[i] Gramophone Company of India Ltd. v. Super Cassettes Industries Ltd. (2011).
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