THE POWER OF GENERATIVE AI FOR M&A VALUE CREATION

THE POWER OF GENERATIVE AI FOR M&A VALUE CREATION

In an M&A environment in which over 50% of deals fail, generative AI offers the opportunity to significantly streamline processes and identify potential red flags which may impact a deal’s success. How exactly can generative AI improve these processes and what are some of the challenges associated?

Deal-making activities in M&A, such as research, sourcing, due diligence, negotiation, and post-merger integration, are often complex, time consuming and resource-intensive. However, generative AI solutions have the potential to deliver benefits at each stage of the deal lifecycle.

One example is identifying potential targets aligned with strategic goals. Current AI tools are already able to integrate a wide range of market data sources to streamline pipeline monitoring and automate deal sourcing. They can also identify more sources of value from the transaction, while more accurately predicting the value to be attained.

Significantly enhancing due diligence

The due diligence stage of a deal can be significantly enhanced by generative AI, given its ability to collect and analyse data, including a target company’s financial, operational, and legal information.

It can also identify red flags and mitigating factors which could impact not only a deal’s success but also its potential value. It does so primarily through automation: extracting and analysing key provisions from across contracts. For us, the fact that so many M&A deals fail makes this feature of generative AI particularly noteworthy.

Valuation is a common stage at which many deals fail. Generative AI is able to use basic company information to evaluate a range of parameters and provide a fair and quick valuation of a business. In turn, valuations become timelier and more accurate, while transactions are carried out under preferable conditions.

Post-merger integration

Post-merger integration can play a major role in dictating the success or failure of a merger. If it is executed effectively, it ensures the merging companies are aligned and consistent across business-critical areas, namely their operations, processes, and cultures.

Fortunately, merger integration and exit support can also reap the benefits from the integration of generative AI. This is primarily through the automation of more mundane tasks, which boosts operational efficiency, as well as an ability to monitor an integration process in real time and provide stakeholders with the necessary insight and feedback to make ongoing adjustments.

The challenges of generative AI integration

While generative AI models can benefit M&A, they also present some issues and challenges that may prevent successful use and adoption. It is essential for businesses and M&A professionals to understand and address these challenges in order to harness the potential of generative AI responsibly and effectively.

One issue is the technical complexity of generative AI models, which presents challenges particularly in the training process. The computer resources needed for it can also make it expensive and environmentally unfriendly. To mitigate these challenges, businesses often turn to cloud APIs for adopting generative AI. By leveraging cloud APIs, businesses can reap the benefits while minimizing infrastructure costs.

Another challenge is security and legal concerns. Generative AI models have been known to utilise training data without the full consent of its original creators. To deal with this issue, businesses should look to adopt a security-first AI approach, with continuous monitoring throughout the model’s lifecycle.

AI hallucination – a term used to describe a phenomenon where an AI system generates content which is not directly derived from training data, but rather as a result of the model’s capability to generate new information, is also possible.

This can present a clear challenge in terms of making sure that the content produced is reliable and accurate. While generative AI is incredibly useful for automating aforementioned mundane tasks, businesses should always consider human verification for more complex and sensitive tasks, to manage the risk of any AI ‘hallucinations’.

The three pillars: people, data, and infrastructure

The three pillars of generative AI – people, data, and infrastructure – must all be considered when firms are looking to optimise the role that it can deliver. It is essential for organizations to have the right balance of technical skills and domain expertise; as such, attracting and retaining top AI talent is a strategic imperative.

Overall, we believe that generative AI offers significant potential in creating additional value in M&A. As AI becomes more potent and stakeholders find the optimal combination with human oversight, failure rates will decrease significantly over the coming years.

First published in the “Financial Derivative” on September 4, 2023

Nick

Neil-Boss

Partner

Strategy & Operations

Eight Advisory London

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