Merger and acquisition (M&A) transactions are frequently used by companies seeking value creation or enhancement of capabilities. According to a recent survey, 47% of chief financial officers (CFOs) across sectors are looking to M&As to drive growth in the current year. However, with accelerated deal activity come several challenges as well. M&As are high-risk activities, and so preserving value through their execution is critical. During the early part of deal planning, company executives often sign up for aggressive synergy targets, whereas achieving them can be far from straightforward.
Business leaders are thus recognizing a need to modernize their approach to M&As, such that it allows for planning and execution with speed and certainty. Aimed at meeting this need is a collection of artificial intelligence (AI) and analytics tools that can provide a structured approach. The adoption of such tools is increasing, as shown by a recent survey of 1,300 global executives which revealed that as many as 69% are using data analytics for pre- and post-deal M&A analysis.
In this article, we look across 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 focuses on ensuring that value is preserved. In an M&A deal, a comprehensive review of the acquisition target’s quality of reporting across financial parameters, technology, as well as environment, social and governance (ESG) compliance is a prerequisite for potential investors and buyers. The findings of this exercise can typically impact deal progression and execution. AI-powered tools help automate the review process while reducing the human error inherent in due diligence, thereby allowing for greater organizational oversight and regulatory compliance.
As an example, a Canadian firm was recently able to leverage an AI application to uncover reporting issues that remained obfuscated even to regulators. This may not have been possible using conventional methods, given the limited time and intensity of the work involved. AI is thus capable of reducing the potential risks that could threaten to affect a deal on the verge of closing.
Bridging the gap between potential and realized synergies: The second area revolves around the creation of value through synergies. Research suggests that about 45% of companies reduce their synergy targets during deal execution due to complexities in implementation. Here too, companies are turning to AI in streamlining disaggregated entity data, increasing the accuracy of synergy estimation and uncovering opportunities that may have been otherwise overlooked. For CFOs, these insights can facilitate better forecasting and help their teams drive the realization of synergies, which are a key metric of deal success.
A US-based technology company drove synergies in procurement by deploying powerful data analytics tools . It was able to realize synergies worth $300 million in less than 30 days by using analytics for visualization of expenditure across suppliers, business units and cost centres, deriving actionable insights to eliminate inefficient vendors from the consolidated vendor pool.
Reduction in execution time: The third area relates to risks from elongated deal timelines that can impact the probability of deal completion. Research suggests that around one third of deals fail due to prolonged execution time, which could be a result of a wide range of reasons, from internal misalignment, lack of visibility and ownership, low volume of contracts and limited communication to external factors such as business 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.
While external factors may be difficult to control, AI tools can help address critical parts of a deal timeline impacted by internal factors. One such scenario is associated with the evaluation of a large numbers of contracts, which can typically range in the many hundreds or thousands, requiring many weeks or months to process. Cognitive analytics can help reduce review time by up to 90% , freeing up time to undertake other critical pre-closing actions and helping 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 susceptible to heightened employee attrition under the impact of various changes that are usually effected. In such cases, they can deploy AI to identify the employees most at risk of exiting based on modelling of data collected through various corporate systems.
One such instance is of a leading IT company in India which implemented an AI-tool to assign risk scores to employees based on more than 80 factors such as demographics, projects, compensation, leaves, 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, thus helping the business control morale as well as the associated direct and indirect costs.
AI and analytics are helping organizations overcome some of the key M&A challenges by making diligence processes more rigorous, highlighting synergies and reducing contract review timelines, while also supporting acquirers in mitigating human resource related impacts.
Business leaders undertaking M&As can test waters by selectively deploying tools of artificial intelligence in a few elements of the value chain, and evaluating the results over one or two deals, before exploring a more comprehensive adoption of AI. By doing this, they can look forward to reduced uncertainties, increased accuracy and superior speed of decision-making, all at a relatively low cost.
