AI is Outperforming Bankers in M&A Landscape and Here’s Why

The intersection of AI, private equity, and mergers and acquisitions (M&A) has quietly unfolded in the Indian landscape. AI is starting to outperform conventional investment bankers in identifying, evaluating and attracting M&A opportunities. 

Traditional M&A practices rely heavily on public data, banker networks, and executive-level social introductions. Yet, most startups remain private and invisible in the market, often until they’re already off the market. 

GenAI models can automatically discover and learn patterns in input data and draw conclusions based on newer, more consistent data. As per reports, all they need to achieve this is numerous past examples of input-output pairs. Using this information, the models learn to identify which inputs are most predictive and which actions lead to desired outcomes.

The Efficiency of AI in Deal-Making

In a conversation with AIM, Amar Shirsat, co-founder and CTO of GrowthPal, also highlighted the inefficiencies in traditional deal-making processes and emphasised the importance of using AI models to identify strategic fits and dynamic market trends. 

Unlike bankers, he added that AI tools can analyse vast datasets, including public and proprietary sources, to predict market sentiments and opportunities.

Along with other publicly and client-sourced data, GrowthPal also uses its own proprietary data accumulated over the last four years of conducting business. “We have spoken with multiple buy-side and sales-side companies, so we understand the market well. All of this combined knowledge is used to train those algorithms, and that’s how the insight is put out,” Shirsat highlighted. 

When examining the usual deal-making process, or the majority of M&A deals as they currently unfold, one can see that they are primarily guided by investment bankers and significantly influenced by relationships.

“The volume of companies is enormous, but today, we have a database of over 3 billion companies across the globe. And if somebody is looking for a cross-border acquisition, it becomes very difficult to identify the right strategic fit for you,” Shirsat said. 

Jayakrishnan Pillai, a partner at Deloitte India, told AIM that AI models are particularly effective for tasks that require processing large datasets, especially when time is limited, as is often the case in M&A transactions.

“Overlaying multiple datasets and drawing inferences from them can be done at a fraction of the time and with higher accuracy than having such tasks carried out manually, [such as] identifying red flags by overlaying sales data with customer sentiment information from social media, predictive analysis of raw material prices and their impact on margins and working capital,” Pillai added. 

The Significance of Accurate Data Sets in M&A

Shirsat believes bankers primarily rely on databases, which provide useful but mostly static information and can limit the discovery of new opportunities. Their business model focuses on selling existing services and finding suitable candidates in the sell-side market.

From a buyer’s viewpoint, the goal is to identify strategic fits in a rapidly changing market, mainly in Denton’s research, which indicates that close to 64% of executives in the business sector intend to use mergers and acquisitions to enhance their AI capabilities in the upcoming year, with this percentage increasing to 70% within the next three years. 

The study mentioned that purchasing companies with established AI capabilities provides a relatively effective method to adopt advanced technology and knowledge, which could result in market growth, improved agility, and reduced costs influenced by AI.  

To navigate this environment, bankers need dynamic tools that assess market sentiments, competitor actions, and sector trends. These tools must identify relevant opportunities quickly, as timely decision-making is crucial in this fast-paced landscape, Shirsat highlighted.

“It’s only a matter of time,” said Alexis Christofides, UK regional head for TCS M&A Services, in a TCS report. “Increasingly, companies are discovering ways to codify their capabilities, even for traditionally unquantifiable things like corporate culture. Sooner or later, all that data on ‘e-mail sentiment analysis’ and ‘product time-to-market’ will be thrown into a machine learning algorithm.”

When asked if AI models could predict exit intent before the market sees it, Shirsat pointed out that companies often leave behind digital footprints, primarily influenced by their executive teams. 

Private equity (PE) firms also use AI tools differently from strategic buyers and bankers. PE firms invest for a fixed time horizon of three to seven years, focusing on exit options and potential buyers. In contrast, strategic buyers emphasise integration, where AI proves valuable in identifying synergies and analysing data patterns related to customers, suppliers, and employee trends across both companies, Pillai explained. 

Navigating the Challenges of Integrating AI in Strategic Partnerships

Nonetheless, AI has limitations, such as algorithm bias and the necessity for human judgment in negotiation and trust-building. 

In any scenario, not all M&A transactions succeed. According to a TCS report, the gap between now and an AI-driven future is the lack of usable training data. When inputs for machine learning are as simple as chessboard positions, providing information is easy. However, corporations don’t fit such straightforward descriptions. 

Before AI can identify which corporate strengths predict M&A success, it must define them. Essentially, it needs to determine the companies’ attributes and advantages. As per the report, while collecting and managing data on corporate capabilities may be tough, it’s not impossible.

Shirsat believes “wherever there is a lack of data, there is some synthetic data generated, or maybe some predictions are being made, which may not be accurate, and there is a lot of feedback loop required, and it will eventually get there”. 

Moreover, AI has limitations in decision-making, especially in situations requiring judgment or when data is scarce.

“For instance, voting on a deal by investment committee members in a PE firm is typically subject to a high degree of judgment, and past data on the correlation between a positive or negative vote and the underlying causal factors is not readily available,” Pillai explained. 

Evaluations aimed at removing biases require significant effort. However, this is a challenging task. Sometimes, human intervention is necessary, which is where expert opinions come into play. Companies tend to rely on these experts rather than solely depend on AI. 

“AI is still being used as a co-pilot, and people very well understand that it is a co-pilot. It’s never going to become a pilot, and I hope it doesn’t become one,” Shirsat concluded.

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