AI is impacting the work of dealmakers and financial professionals. Here’s how.

Artificial intelligence (AI) is actively challenging the traditional operational models of enterprises. Across all industries, business leaders are considering two questions. First, how are AI technologies leading the next iteration of innovation, and second, which of these technologies could be adopted to improve efficiency outcomes to help enhance productivity.

Using large language models (LLM), AI is naturally geared to support industries that rely on the processing and analysis of quantitative and qualitative data. At the same time, access to data and information is greater than ever before. While this empowers professional workers and opens new opportunities, it also makes the manual processing of information more challenging.

This is the reason why the financial services sector is ideally positioned to benefit from AI.

Bankers, and in particular, mergers and acquisitions (M&A) dealmakers, are required to manage information and data of multiple stakeholders in high pressure, time sensitive environments. Yet there are some gaps that need to be bridged between AI knowledge and its application.

Bridging the gap between AI knowledge and application

For example, a 2023 Datasite survey of 500 dealmakers based in the U.S., U.K., France and Germany, shows that dealmakers are increasingly aware of how AI could be practically applied in financial services, with 90% of respondents claiming moderate to extensive AI familiarity. Most (35%) also identified process improvements and efficiencies as the biggest improvements that could be made through the application of Generative AI (GenAI). This was followed by improved deal intelligence (19%) and better deal decisions (16%).

Despite this awareness, over 60% of dealmakers acknowledged that their organization has low GenAI adoption or are only using it experimentally. The gaps that exist between the benefits, the familiarity and the adoption collectively reflect the complexities around the use of AI in financial services.

When considering the barriers of AI adoption, most dealmakers (34%) identified data privacy and security concerns as the greatest risk to using GenAI in their business, followed by job displacement, quality control, and intellectual property rights and bias and fairness.

These results provide a timely snapshot of how dealmakers perceive AI integration. While there is an appreciation of its benefits, questions persist about its use in the dealmaking process. It explains why governments are actively researching and preparing regulatory measures to guide AI’s appropriate use. Importantly, financial services institutions will need to be part of these conversations.

Efficient due diligence processing

Of all the dealmaking processes to be impacted by AI, due diligence is a key area that is already being transformed by AI. This critical stage of the dealmaking process gives all parties involved in the transaction a comprehensive assessment of the risks, opportunities and challenges that need to be taken into consideration. Poor due diligence can directly undermine the success of a proposed transaction.

Due diligence is resource-intensive and traditionally relies upon the manual processing of information and documents. When faced with tight deadlines and time constraints, the standard of work delivered can be compromised. AI can overcome this challenge by rapidly generating summaries of information through deep document analysis. By extracting core clauses and notable obligations relevant to attorneys and those involved in the deal, it rapidly reduces the time involved in the processing of documents during the due diligence stage.

However, AI is not a silver-bullet solution. While it can provide the critical information required, the parameters required for the algorithm to work will require human input. The information extracted by the AI will also still need to be analyzed by a professional, demonstrating the need for high-end legal and business decision making. AI can offer significant time and resource savings but considering the current capabilities of existing technologies, those applying AI algorithms for due diligence need to do so with a critical eye.

AI-powered acquisition targeting

Alongside the practical uses of AI by dealmaking, there are ways in which AI can be deployed outside of the dealmaking channel. The interconnected nature of the global market demands an understanding of major and minor trends shaping the performance of leading economies. For those companies and professionals considering M&A activity, this knowledge is critical though complex. Understanding the future performance of industries requires input from multiple datasets. Even then, any projection is speculative due to the number of factors at play.

There is a clear opening here for AI to be used. By reviewing reams of data and analysis and being informed by a defined criteria of desirable company profiles, AI can effectively identify potential M&A targets. This identification process ensures that M&A searches and targets can be actively generated, with the latest data samples and unfolding market trends.

Those pursuing a programmatic M&A strategy of acquisitions of multiple smaller companies over larger, one-off deals, stand to greatly benefit from this approach. This approach can also mean that companies are in a better position to integrate new capabilities when the deal is completed to deliver the consistent growth that was intended by the tie-up.

Determining true value

Since the Covid-19 pandemic, the topic of valuation has dominated discussions on IPOs and funding rounds. Volatile valuation swings have demonstrated the difficulties in determining the true value of a company. On the point of objective analysis, AI can be deployed to remove the subjective elements surrounding company valuations.

The accuracy of a valuation is determined by the information and data that AI can access. Revenue-generation forecasts, debt management and cash flow data can collectively lead to a fair deal price agreeable by all parties involved in the transaction. Data which also provides a historical perspective on deals completed in similar industries and by companies of equitable size and turnover is also useful, covering the market factors that influence the total value of a company.

Using AI to help assess valuations can provide some advantages. It can provide an accurate company valuation free from any bias from the stakeholders involved. It can also build greater deal trust and potentially reduces the amount of time for the deal to close. However, an over-reliance on AI can lead to a neglect of the qualitative factors and the importance of human judgement. This is important when considering valuations from a forecasting position, considering geopolitical and economic trends and risks that might not be easily identifiable from the analysis of historical data sets.

Empowering decision makers

While the above analysis may focus on M&A, it doesn’t limit the general observations which are relevant across the private and public sectors. The core theme is empowerment. While its full potential will take years to be realized, AI in its current form can take on manual and resource intensive tasks that rely on human input. The result is a workforce able to focus on strategic-level decisions and creative thinking to improve productivity and outcome.

The advantages that come from human involvement in financial services should not be overshadowed by the exciting innovations on display from AI. Striking the right balance of M&A and financial services professionals working effectively and efficiently through AI is the goal, and positively, we are already seeing important progress being made.

Mark Williams is Chief Revenue Officer, Americas at Datasite.