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How Global Dealmakers are Leveraging AI




Artificial Intelligence (AI), including generative AI (GenAI), is rapidly revolutionizing business processes and challenging traditional operational models across industries. The mergers and acquisitions (M&A) industry is no exception.

Large language models (LLM) and GenAI are particularly well-suited to support industries reliant on processing and analyzing vast amounts of data. Financial services, especially the management of capital transactions like M&A, stand to benefit significantly due to the complex and time-sensitive nature of the work. For example, when it comes to buying or selling a business, one of the most challenging parts of the M&A process is organizing and preparing the files needed for review by potential investors or purchasers. AI can help streamline this process significantly. An AI algorithm that understands M&A, can sift through a deal’s data and suggest categories, as well as appropriate folder locations, for the files, transforming an activity that used to take weeks to one that is complete in just minutes.

Dealmakers have already seen the benefits of AI’s ability to improve processes and efficiencies, particularly in due diligence, where AI-powered document analysis can substantially expedite information processing. In fact, a Datasite survey of 500 global dealmakers in the US, UK, Germany and France found that most dealmakers see productivity as the biggest benefit of using AI in their business.

AI is also making other parts of the dealmaking process more efficient. For instance, AI can assist in identifying potential M&A targets by analyzing vast datasets and market trends, particularly beneficial for those pursuing programmatic M&A strategies. By using anonymized private equity and other transaction activity from within a closed and secure platform, some AI-powered applications are already helping dealmakers get better and faster deal targets.

AI can also aid in the valuation process by providing objective analyses based on historical data and market factors. However, while AI can enhance accuracy and efficiency in valuations, human judgment remains essential, especially in evaluating qualitative factors and forecasting.

Additionally, by automating repetitive and time-consuming tasks, AI enables dealmakers to focus on strategic-level decisions and creative thinking. Achieving a balance between AI and human involvement is, in fact, key to maximizing productivity and outcomes.

Yet, despite this awareness of AI's potential benefits, there is still a gap between familiarity and adoption in the M&A industry. While many dealmakers said they have personally reaped the benefits of the technology, 60% said adoption of AI at their own organizations was low, or that they were still using it only experimentally. Furthermore, over 70% of global dealmakers want the technology regulated before it is incorporated into any of their existing processes, citing concerns around data privacy and security, job displacement, quality control, intellectual property, and bias.

For this, the government is stepping in. The EU has introduced the AI Act and the US has published a blueprint for an AI bill of rights and an executive order that requires companies to perform safety tests and reporting on AI systems. As regulatory measures catch up with technological advancements, financial services institutions are sure to play a crucial role in shaping the responsible and effective use of AI in dealmaking.

Looking ahead, AI is only set to further evolve how deals are managed, driving further efficiencies and innovations in M&A dealmaking processes. While striking a balance between human involvement and AI is key, there is no doubt that we will continue to see AI implementation in the M&A field.

James Lehnhoff is Chief Technology Officer at Datasite, a position he has held since August 2022.  In this role, James is responsible for leading software engineering, application support and artificial intelligence (AI) development.

James has been with Datasite since 2017, serving as Senior Vice President of Engineering and Vice President of Product Technology where he grew the engineering team by more than 50% and helped lead the successful launch of Datasite Diligence. Before joining Datasite, he held engineering leadership roles with Workfront and Digital River.