The global pandemic has changed the way we work, including how and where we work. For those involved in the mergers and acquisitions (M&A) industry, a notoriously relationship-driven business, this has meant in-person boardroom handshakes have been replaced by video conference calls, remote collaboration and potentially less travel in the future.
Research shows that AI will transform the M&A process by decreasing the time it takes to perform due diligence to less than a month in 2025 from three to six months in 2020.
The pandemic has also accelerated digital transformation, and deal-makers have embraced digital tools, sometimes even drones, to help them execute effectively. Even legal M&A professionals, often among the major holdouts to embrace remote work and technologies, are increasingly using technology to automate common time-consuming tasks, such as redaction and contract analysis. And with a vast majority of them reporting permanent remote or hybrid work arrangements, further technology adoption is expected.
The quickening pace of digital transformation is no longer about ensuring a competitive edge. Today, it’s also about business resilience. But what’s on the horizon, and how else will technology evolve to meet the needs of companies and deal-makers?
There are still many inefficiencies in managing M&A, but technologies such as artificial intelligence, especially machine learning, are helping to make the process faster and easier.
AI helping sell-side prepare deals and conduct diligence
Typical deals require the analysis of huge amounts of data in a relatively short period of time. So, when time is money, tools that speed up the M&A process are critical. That’s why AI-powered tools that help deal-makers automate tasks, reduce human error and ensure greater regulatory compliance are gaining interest.
For example, when it comes to selling a business or asset, 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. Investment banking analysts often spend weeks reviewing thousands of files to figure out how to organize them and prepare them for a transaction.
Using statistical methods that allow a system to learn from data, and then make decisions, AI and machine learning leverage an algorithm to sift through those large volumes of data and content. Such a tool can then enable the upload of hundreds or thousands of files and their review by an AI engine, which reads the files and suggests categories, as well as appropriate folder locations, for the files. AI and machine learning streamline the process in a matter of minutes, not weeks, freeing up deal-makers to focus on higher-value activities.