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How AI and advanced analytics have come to transform M&A

Merger and acquisition (M&A) transactions are often used by companies looking to create value or improve their capabilities. According to a recent survey, 47% of chief financial officers (CFOs) across all industries are looking to mergers and acquisitions to drive growth in the current year. However, with accelerated deal activity comes several challenges. M&As are high-risk activities and therefore it is critical to retain value through their execution. During the early part of deal planning, business leaders often sign aggressive synergy goals, when achieving them can be far from easy.

Thus, business leaders recognize the need to modernize their approach to mergers and acquisitions to enable planning and execution with speed and certainty. To meet this need is a collection of artificial intelligence (AI) and analytics tools that can provide a structured approach. Adoption of such tools is increasing, as shown by a recent survey of 1,300 global executives, which found that as many as 69% use data analytics for pre- and post-deal M&A analysis.

In this article, we look at the entire M&A value chain and focus on four key areas where AI helps organizations streamline M&A execution.

Due Diligence and Regulatory Compliance: The first area focuses on ensuring value is preserved. In an M&A deal, a comprehensive assessment of the quality of reporting on financial parameters, technology, and environmental, social and governance (ESG) compliance of the acquisition target is a prerequisite for potential investors and buyers. The findings of this exercise can typically influence deal progress and execution. AI-powered tools help automate the review process and reduce the human errors inherent in due diligence, enabling greater organizational oversight and regulatory compliance.

For example, a Canadian company was recently able to use an AI application to expose reporting issues that were hidden even from regulators. This may not have been possible with conventional methods, given the limited time and intensity of the work. Thus, AI is able to mitigate the potential risks that could affect a deal that is about to close.

Bridging the gap between potential and realized synergies: The second area revolves around creating value through synergies. Research shows that approximately 45% of companies reduce their synergy targets during deal execution due to complexity in implementation. Again, companies are turning to AI to streamline disaggregated entity data, increase the accuracy of synergy estimates, and uncover opportunities that would otherwise have been overlooked. For CFOs, these insights can enable better forecasting and help their teams realize synergies, which are an important measure of deal success.

A US-based technology company created synergies in procurement by deploying powerful data analytics tools. It was able to realize $300 million in synergies in less than 30 days by using analytics to visualize spend across suppliers, business units and cost centers, and gain actionable insights to remove inefficient suppliers from the consolidated supplier pool.

Shortening Execution Time: The third area relates to risks of stretched deal timelines that can affect the likelihood of deal completion. Research indicates that about a third of deals fail due to extended execution time, which can be the result of a wide variety of reasons, from internal misalignment, lack of visibility and ownership, low contract count and limited communication to external factors such as business condition. These can have significant financial implications for the companies involved and have a broad impact on other stakeholders such as employees, suppliers and shareholders.

While external factors may be difficult to control, AI tools can help address critical parts of a deal timeline that are affected by internal factors. One such scenario involves the evaluation of a large number of contracts, which can typically run into the hundreds or thousands, and take many weeks or months to process. Cognitive analytics can reduce review time by up to 90%, freeing up time to perform other critical pre-closing actions and increasing overall speed.

Talent management and retention: The last area focuses on employee risks and organizational culture changes. Organizations undergoing a merger and acquisition transaction are prone to increased employee turnover as a result of various changes that are commonly made. In such cases, they can leverage AI to identify the employees most at risk of leaving based on modeling data collected from various business systems.

An example of this is a leading IT company in India that implemented an AI tool to assign risk scores to employees based on more than 80 factors such as demographics, projects, compensation, leave, career development, learning and development and assessment results. These, in turn, were used to flag employees at “risk,” allowing leaders to plan targeted interventions for their retention, helping to better control corporate morale and associated direct and indirect costs.

AI and analytics help organizations overcome some of the key M&A challenges by tightening due diligence processes, highlighting synergies, and shortening contract review timelines, while also supporting acquirers in mitigating human resources impacts.

Business leaders undertaking mergers and acquisitions can test the water by selectively deploying artificial intelligence tools in a few elements of the value chain and evaluating the results across one or two deals, before exploring more extensive AI adoption. By doing so, they can enjoy reduced uncertainties, greater accuracy, and superior decision-making speed, all at a relatively low cost.

Sumeet Salwan & Mayank Jaswal are partners at Deloitte India

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