Artificial intelligence revolutionizing the M&A horizon

If adopted, AI is set to play a crucial role in this function, as it enhances the M&A process at every step, from pre-deal assessment to post-merger integration.

AI is revolutionizing the future and driving growth as it permeates all industries worldwide. Approximately 25-35% companies are utilizing AI in their operations and have witnessed an average increase of 30% in productivity. Organizations are increazingly seeking mergers with AI companies to gain access to advanced technology, talent, and market expansion. For instance, IBM acquired Apptio; Databrick purchased Mosaic ML; and Cisco bought Splunk.

As per interactions with M&A and C-suite leaders, only a small proportion of organizations currently leverage AI in M&A activities. However, it is expected that over the next two years, four out of every five individuals involved in M&A will integrate AI into their workflow. If adopted, AI is set to play a crucial role in this function, as it enhances the M&A process at every step, from pre-deal assessment to post-merger integration.

M&A strategy formulation and deal sourcing

In M&A, strategy formulation is crucial in setting the growth trajectory of organizations. AI employs evidence-based decisions – its predictive analytics provide valuable insights into business trends, industry dynamics, and emerging but unexploited market niches, making strategy formulation more precise.

Finding suitable acquisition targets can be time-consuming and challenging. AI utilises numerous datasets to filter potential targets and identify those with the highest synergy potential, thus enhancing discoverability by augmenting the limited pool of private company data and increazing the pace of the process.

Due diligence

Due diligence is a tedious and laborious task. On the buy side, it begins with extensive research about the target company uzing available public sources. The sell-side process begins by setting up a Virtual Data Room. Uzing AI, today’s buy-side can delve into huge volumes of public resources, including contracts and financials, to help them understand the identified risks, in some cases even publishing probability scenarios. FMCGs are utilizing AI to evaluate brand influence on social media for potential target companies. On the sell side, the VDR process has been simplified by AI algorithms to auto-arrange uploaded files and propose masking of sensitive data. It assists in finding gaps, summarizing documents, and creating reports based on flagged issues as well as expert insights.

Business valuations

It is crucial for mergers to have an accurate valuation from both the buyer’s and seller’s perspectives. AI algorithms ensure precise valuation uzing financial and non-financial data, asseszing metrics such as brand equity, customer satisfaction and market positions with broader perspectives beyond traditional approaches. Hence, acquirers gain better insights, leading to favourable results. AI also handles basic activities like identifying comparable companies and data aggregation, thereby freeing up human capital to focus on strategic elements.

Post-merger integration

The post-transaction period involves merging and integrating firms with different organizational structures, processes, and technologies. AI assists in automating the integration processes to ensure a seamless transition. Organizations can use AI-powered predictive models to anticipate emerging problems. They evaluate data to determine the weakest points where improvements can be made.

AI also plays a role in talent retention by identifying key personnel important for the integrated entity’s success and propozing plans accordingly. Over time, as AI evolves, it will analyse and define integration blueprints, workflows and infrastructures, finding synergy potentials that lead to propozing optimised tailored integration strategies.

Key challenges

Data privacy: Safeguarding sensitive information requires employee training, strong cybersecurity, GDPR compliance, and user access control. Over 35% of organizations identify it as Generative AI’s top challenge.
Bias and fairness: It is essential to mitigate inherent biases in AI algorithms by uzing tools, and ensure fairness in data, along with uzing strategies like prompts and content filtering to handle toxicity. Over 20-25% organizations worry about Generative AI bias.
Regulatory compliance: Adhering to evolving regulations and standards governing AI deployment and usage is necessary for business leaders, which is a significant cost and often freezes the bandwidth of C-suite leaders.
Skills gap: More than 50% organizations encounter challenges related to skill gaps when uzing AI. Talent shortage is one of the key reasons for the slow deployment of AI technologies in M&A.

Synergy of AI and human expertise

Refined screening, diligence and execution through many transactions over time have taught experienced buyers how to be dealmakers. Generative AI, although powerful, cannot replace skilled M&A practitioners. Technology does not substitute but assists humans, thus speeding up deals with accuracy. While AI is good at pattern recognition, people bring interpretative skills and strategic foresight.

By deploying AI for data-intensive tasks and human analysts for emotionally intelligent activities, we can optimize the synergy between man and machine. This collaboration will improve deal outcomes by leveraging the strengths of both. In the AI-driven M&A era, human advisors remain essential - they complement AI and maximise M&A value.

The incorporation of AI into the M&A realm signifies more than just a paszing trend; it streamlines operations, boosts decision-making and supplements human expertise for efficient transactions. The use of Generative AI in M&A is expected to reach 75-80% in the coming years. Despite challenges, AI's integration into M&A signals significant growth and shaping the future of deal-making.