How AI and Advanced Analytics Started Transforming M&As


Mergers and acquisitions (M&A) transactions are frequently used by companies looking to create value or improve 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, the acceleration of business activity also comes with several challenges. Mergers and acquisitions are high-risk activities, so it is essential to preserve value throughout their execution. Early in deal planning, company executives often sign up for aggressive synergy goals, when achieving them can be far from straightforward.

Business leaders are thus recognizing the need to modernize their approach to mergers and acquisitions, so as to enable rapid and certain planning and execution. To address this need, a collection of artificial intelligence (AI) and analytics tools can provide a structured approach. Adoption of these tools is on the rise, as shown by a recent survey of 1,300 global executives which found that up to 69% use data analytics for M&A analysis before and after the transaction.

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

Due diligence and regulatory compliance: the first area aims to ensure the preservation of value. In an M&A transaction, a comprehensive review of the quality of the acquisition target’s reporting on financial metrics, technology, as well as environmental, social and governance (ESG) compliance is a prerequisite for investors and potential buyers. The results of this exercise can generally impact the progress and execution of trades. AI-powered tools help automate the review process while reducing human error inherent in due diligence, enabling greater organizational oversight and regulatory compliance.

For example, a Canadian company was recently able to leverage an artificial intelligence application to uncover reporting issues that remained obscured even to regulators. This might not have been possible with conventional methods, given the limited time and labor intensity involved. The AI ​​is thus able to reduce the potential risks that could threaten to affect a transaction about to be concluded.

Bridging the gap between potential and realized synergies: The second area revolves around creating value through synergies. Research suggests that around 45% of companies reduce their synergy targets when executing the deal due to the complexity of implementation. Here too, companies are turning to AI to streamline disaggregated entity data, increase the accuracy of synergy estimation, and uncover opportunities that might have otherwise been overlooked. For CFOs, this insight can facilitate better forecasting and help their teams drive the realization of synergies, which are a key indicator of deal success.

A US-based technology company has generated synergies in procurement by deploying powerful data analytics tools. He was able to realize synergies worth $300 million in less than 30 days by using analytics to visualize spend across vendors, business units, and cost centers, deriving actionable insights to weed out vendors inefficiencies from the consolidated vendor pool.

Reduced Execution Time: The third area relates to the risks associated with extended transaction times which can impact the likelihood of the transaction occurring. Research suggests that around a third of trades fail due to prolonged execution time, which could be the result of a wide range of reasons, ranging from internal misalignment, lack of visibility and ownership, a low volume of contracts and limited communication with external factors such as companies. conditions. These can have significant financial implications for the companies involved as well as a broad impact on other stakeholders such as employees, suppliers and shareholders.

Although external factors can be difficult to control, AI tools can help address critical parts of a deal schedule impacted by internal factors. Such a scenario is associated with the evaluation of a large number of contracts, which can usually number in the hundreds or thousands, requiring several weeks or months to process. Cognitive analytics can help reduce review time by up to 90%, which frees up time to take other critical actions before closing and helps increase overall speed.

Talent management and retention: The final area focuses on risks related to employees and changes in organizational culture. Organizations going through an M&A transaction are likely to increase employee attrition under the impact of the various changes that are usually made. In such cases, they can deploy AI to identify employees most at risk of leaving based on modeling data collected through various enterprise systems.

One such example is that of a leading IT company in India that implemented an artificial intelligence tool to assign risk scores to employees based on over 80 factors such as demographics, projects, compensation, time off, career progression, learning and development, and appraisal results. These were, in turn, used to flag “at risk” employees, allowing leaders to plan targeted interventions for their retention, helping the company control morale as well as the associated direct and indirect costs.

Artificial intelligence and analytics are helping organizations overcome some of the key M&A challenges by making due diligence processes more rigorous, highlighting synergies and reducing contract review times, while helping acquirers mitigate human resources impacts.

Business leaders undertaking mergers and acquisitions can test the waters by selectively deploying AI tools in a few parts of the value chain and evaluating the results on one or two deals, before exploring a fuller adoption of AI. By doing so, they can expect reduced uncertainties, increased accuracy, and faster decision-making speed, all at a relatively low cost.

Sumeet Salwan and Mayank Jaswal are partners at Deloitte India

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