Mergers and acquisitions (M&A) typically involve many complex activities across various phases that, due to the short time frame and competitive environment, require coherent cooperation of the disciplines involved. Technologies like artificial intelligence (AI) and data analytics are an increasingly decisive lever to successfully execute M&A transactions, significantly accelerating all process phases from preparation to post-merger integration (PMI).
This article focuses on four typical phases of the M&A process: (i) target screening, (ii) due diligence, (iii) business valuation, and (iv) post-merger integration, where AI can create competitive advantages. However, AI can be applied more broadly to provide deal advantages in every phase of the M&A lifecycle.
The most important target-screening criterion is the probability of success, i.e., identifying acquisition opportunities that promise the highest return on investment (ROI). When selecting acquisition candidates with potentially value-added synergies based on their overall company objectives, AI is a game-changer for traditional opportunity sourcing. Through machine learning, AI allows stakeholders to explore potential acquisition targets and the impact of transactions on strategy and financial performance.
Multiple sources of correlated and uncorrelated data sets can be combined and compared, using complex algorithms to identify patterns imperceptible to humans. By analysing these patterns, market insights and trends can be gained, and predictions can be made much faster than any human, making the target screening process scalable. These algorithm-based evaluations can significantly bolster decision-making, with dynamic real-time visualisations allowing decision-makers to grasp complex relationships more efficiently.
In recent years, cloud-based virtual data rooms (VDRs) have almost entirely replaced physical ones, transforming M&A due diligence processes. This development has improved the efficiency of testing and examining key assumptions regarding the planned transaction’s projected growth. Automation, AI-based analysis methods, and dynamic visualisation techniques can mine ever greater value from the data collected and consolidated in VDRs, allowing the buy- and the sell-side to make faster and more informed decisions through clearer guidance and better insights.
When valuing a business, at least one of three different approaches are generally used: (i) cost approach, (ii) market approach, and (iii) income approach. The cost approach derives value from the cost of rebuilding or replacing a business or asset. While the market approach is a relative valuation method where various multiples (e.g., EBITDA multiple) are extracted through comparable analysis (i.e., peer group analysis) and/or precedent transactions, which are applied to the target company’s financials. Since both methods extract and analyse data, AI can be used to develop real-time databases serving as the valuation basis or to create valuation adjustment formulas tailored to the target and specific criteria to improve calculation quality.
In contrast, income-based valuation approaches are generally intrinsic valuation methods. For example, in the discounted cash flow (DCF) method, the enterprise value is calculated from the company’s expected future earnings, discounted to the present value using an appropriate discount rate.
Consequently, AI can be leveraged to collect and extract discount factors and cash-flow forecasts from market-based benchmarks based on the company’s risk indication. Its powerful data processing capabilities enable on-demand scenario analysis and multi-variable sensitivities. Improved insights can be gleaned from the analytics, transforming the customer experience with faster responsiveness, click-through functions, and more. All this helps clarify the valuation process and provides insights for informed strategic business decisions.
In the M&A environment, mistakes can have serious consequences. One misstep can leave thousands of bills unpaid for months or delivery delays that drive sceptical customers to explore other options, allowing competitors to capitalise on the uncertainty. To avoid these surprises and effectively manage the transaction while maintaining business continuity, a set of preparations, known as day one readiness, is vital.
When a deal is closed, the PMI phase begins, where both companies’ assets, personnel, and related business activities are merged. PMI can also leverage AI, especially in labour-intensive manual processes, e.g., contract management. Data and AI-driven solutions can optimise business activities and identify further value-creation opportunities by analysing the merged companies’ synergy potentials and risks to uncover the most efficient integration method.
To ensure a successful day one readiness and integration, an innovative blueprint should be implemented that allows the organisational structure and business processes to be aligned and the internal complexity reduced.
By boosting the value creation, effectiveness, insights, and decision-making of M&A transactions across all industries, the use of data analytics and AI-based activities in the M&A process is likely to increase in the future.
In the Master of Mergers & Acquisitions, all relevant areas of the M&A process are taught by leading academics and industry specialists. The entire curriculum is continuously adapted to the latest findings from M&A practice and research, for example, new regulatory requirements as well as procedural optimisations through technological developments, such as the use of AI. In particular, guest lecturers give the students exciting and valuable insights into these areas. In addition, Frankfurt School offers students the opportunity to acquire interdisciplinary knowledge through the AI Lab, the Blockchain Center, and other initiatives. This excellent combination of courses equips students with the necessary skills to deal with changes such as those driven by the use of AI described in the article above.