By Suzy Bibko, Content Marketing Manager, EMEA

The use of AI in M&A has become critical in terms of efficiency, timing, and transparency in dealmaking. The amount of data in data rooms has exploded. Technology and automation are working overtime as dealmakers look for solutions to shorten the M&A cycle.

In a recent survey, 64% of the 2,000+ dealmakers polled believe that they would be able to close deals within 30 days by 2025 and that due diligence would also only take a month.

These views were also echoed at our recent webinar, where panelists shared insights into private equity in the M&A segment. As more and more private capital, both on the debt and the equity sides, is searching for good assets, it becomes important to look at the role of technology and automation in powering capital in the dealmaking process. Here are some highlights from the webinar.

Maximizing value creation during the M&A process
From a private equity point of view, value creation through digitalization can be seen in three main categories.

One is the actual identification of the targets and formation of a pipeline strategy to narrow and focus on the right targets for an investment strategy. This is the first step in the origination process where transparent and efficient market information allows focus on the right targets.

The second is to have the key components in place to monitor the process and set procedures for reporting between the investee and private equity. Here, having the right automation tools within the investing company to set up governance is critical.

The third is to have data analytics in place for transparent and efficient growth of the investing company. Also, for PE firms to achieve their exits, these tools are needed to secure an efficient divestment process.

When the amount of information and data is huge and growing, PE firms have to couple strategic advice with demonstrable tools that will effectively allow the investing company to scale up.

Solutions like those offered by Datasite become vital for PE firms in securing the right data, reporting it to LPs, and then using efficient ways for investing companies to grow and also exit smoothly. 

At any point in the M&A process, custom analytics can be run on the data and the data can be extracted for due diligence.

AI for origination and matching
AI tools identify, narrow, and give viable direction in terms of potential targets for private capital. From an origination perspective, funds gain better knowledge and build conviction on assets well ahead of any process. Traction in terms of automated origination is showing encouraging results where such things as taxation, regulation, and legal issues are used in matching.

However, challenges exist in achieving this automation, especially in cross-border situations where markets can be fragmented and the cost of deploying such tools may be higher than employing local intermediaries. 

Tech for strategy
Strategy can also be helped with technology. At any point in the M&A process, custom analytics can be run on the data and the data can be extracted for due diligence. For instance, international issues, ESG factors, and risk management are some core aspects where data and technology aid strategic decision making. AI is able to identify information and the things that need to be looked at quickly. With AI and machine learning, only selected documents that need review reach the data room, which helps the dealmaker form an opinion to advise their client.

Automation for integration
Data has always been an important component from the origination point of view, but AI is important for integration, as technology can make for more efficient and quicker transactions. For instance, getting all the stakeholders in a virtual data room to review documents can not only be done much quicker than if done in person, but a private equity investor can also have a broader view of things.

AI powers specialization
A PE fund manager largely deals with origination, execution, and portfolio management. With more and more automation, dealmakers are focusing and honing their own skills sets and letting the other competencies be delivered via AI, allowing them to further specialize.

Still, automation suffers from constant reiterations. As important as it is now becoming to the M&A process, automation still needs to be agile and offer quicker and easier ways to be adapted by dealmakers.