For years, mergers and acquisitions (M&A) and deal-making professionals have utilized quantitative finance models to make better decisions on how to allocate their internal or clients’ investments on deals that they believed would generate positive returns in the future. Now, with the recent advances in machine learning, deep learning, and other forms of artificial intelligence, we are now seeing AI being used as an integral part of many aspects of deal-making, inclduing and artificial intelligence in M&A due diligence processes.
AI holds great potential to help B2B companies become more agile, innovative, and streamlined with superior decision-making capabilities. Through data analytics, AI can improve the business planning process by evaluating deal viability across multiple factors such as financial risk, management expertise, or sales growth. These improvements will be significant enough to influence or even change the entire business strategy of (non-)tech companies.
As we look at AI changing deal-making and due diligence processes going forward, in this article we discuss ways we think AI can impact M&As going forward.
The Game Changer: Due Diligence
AI-powered due diligence helps executives make more informed decisions about whether or not they should go ahead with the acquisitions, or sell a business. The due diligence process is accelerated as companies don’t have to wait around for weeks as their internal or external deal team carry out manual research to figure out if an asset is worth buying; largely thanks to AI’s ability to perform high-quality due diligence quickly.
In particular, instead of combing through every individual legal document, an algorithm can be trained to assess whether a given contract is likely to hold up if challenged. A training dataset consisting of previously signed contracts results from arbitration rulings, or plain old common sense could be used to train an algorithm that would then apply its conclusions to new deals and give feedback on their likelihood of success.
This feedback could then help the deal makers with structuring or provide intelligence on areas that might need additional fine-tuning before signing—allowing for better decision making on both sides of negotiations.
Automating some parts of due diligence workflows would free up employees’ time to focus on higher-level tasks like strategic planning and team building – human intelligence, rather than sector data extraction and analysis—and give them more time to think about how AI will fit into their business models and acquisitions strategy as well.
It’s not just about saving time either; it’s about increasing accuracy and reducing risk. Just like with any other AI application, once you have access to accurate information, you can use it to make smarter decisions. And those decisions are what makes all of these AI applications so powerful; they allow companies to do more with less time and money by augmenting human capabilities instead of replacing them entirely.
AI used to make Deal Structures
Using AI in deal-making can be useful for structuring deals, especially complex ones. For example, a study by Northwestern University showed that artificial intelligence used for asset-liability management could potentially reduce customer costs by $1 billion per year at a large bank.
To determine whether it’s worth it to pursue a deal with other companies, organizations typically evaluate three factors: profitability (the financials based on economic or sector data), potential synergies (the structure of an offer), and execution risk (how likely it is for each party to come through on its promises). What AI does is gives organizations an outside view of those factors; there’s no bias or agenda, which helps ensure that decisions are more objective than if they were made by human investors or executives.
AI in Strategic Analysis Phase
AI has already started to be used in strategic analysis, but there are some considerations to keep in mind when using AI for a thorough review. M&A documents can vary from company to company, but there are some common categories that companies typically go through.
These include strategy, structure and assets, technology, current products and services, management team members (usually including background checks), financials, and budget forecasts. AI is especially good at handling large datasets for data mining purposes and AI assisted search queries. It’s possible for AI to handle hundreds of thousands of pieces of information or data points with ease.
For instance, AI has already been used by major corporations like Microsoft in their legal department to help automate processes such as document review and contract negotiation. In fact, AI helped Microsoft save $100 million per year via automation tools targeting manual tasks related to reviewing contracts and performing other legal tasks.
In addition, deal makers also use of AI for aspects such as creating new value propositions based on existing market data on valuation combined with predictive analytics based on historical trends.
The use of AI as an Acquisition Advisor
One of the first law firms to make headlines for its deal-making advice for acquisitions is LawGeex, which uses machine learning algorithms to provide real-time due diligence support on complex legal contracts. The goal of using AI here is to scale up time-intensive human labor across a broad range of clients.
The company targets legal professionals who have expertise with contracts but lack specific knowledge about individual deals, such as investment banks and private equity funds. LawGeex’s website offers demos of how it works for clients looking for support for their due diligence process on one-off deals or interested in scaling up services to process high volumes of data.
Application of AI in Negotiations Phase
Traditionally, negotiations used to follow a phase-wise approach. It included planning, analysis, collecting information, and then forming an opinion on both your as well as your counterpart’s stance. All these required quite some time.
However, with AI technology advancing day by day, it is now possible to perform all these tasks simultaneously for better results which saves a lot of time. Moreover, AI performing market research for you through virtual agents allows you to use your own negotiation skills to optimize the outcomes.
The most important aspect that should be kept in mind while leveraging AI in deal-making is that it cannot make decisions on its own but works best when supported by human decision-makers working hand-in-hand with AI-powered tools. Artificial intelligence has immense potential to make business strategies more efficient than ever before allowing companies to gain the necessary competitive edge and increase market share.
AI in Post-deal Phases
Given AI’s potential to analyze large amounts of data, it’s not surprising that it can also be used to optimize different deal phases including post-deal phases and post merger integration.
At a time when an increasing number of deal failures are attributed to a lack of preparation for integration, AI offers a solution by suggesting best practices for integrating acquired companies into their new parent organizations, based on millions of training scenarios and real-world success and failure outcomes.
Challenges of using Artifical intelligence in M&A
Selecting a suitable AI solution is just one of many challenges companies will face when using artificial intelligence in M&A process. The unique nature of each deal means that AI technologies capable of completing standard due diligence and execution for all types of deals will be difficult to develop.
No two deals are alike, which is why automation must be customized to match a company’s strategic goals—and its competitors’ strategies. Moreover, most large companies already have considerable legacy systems, as well as rules and regulations designed to ensure compliance with standards such as Sarbanes-Oxley.
These systems may need upgrading or replacing before an AI system can be integrated into them—meaning more time and money spent by a business on adapting existing software versus developing new solutions that would require less modification.
On the technology and operational side, when incorporating AI insights into the deal-making process, there are some challenges that have to be addressed by the technology executives. Some of these challenges include ethics in machine learning (ML), ML models not being updated regularly due to a lack of relevant data sources, and lack of integration between technology systems from different vendors.
In addition, AI may also face legal issues if it makes decisions without human oversight; this could potentially lead to discrimination against certain groups of people. In order to address these challenges, we need better regulation on AI usage and clear guidelines on what kind of algorithms should be used for which purposes. Furthermore, we also need better education programs on how AI works so people can understand its limitations and benefits. Lastly, we need improved tools for tracking unfair biases so that AI does not perpetuate them further.
Several organizations have adequately addressed the aforementioned challenges and reaped the massive benefits of artificial intelligence in M&A. In particular, AI could make business realities more apparent via data-driven insights, making leaders more aware of potential problems early enough that they have ample opportunity to fix any issues before they arise—and also give them access to insights they might not otherwise get until much later in a deal cycle.
Conclusion
While there are certainly many ways artificial intelligence will disrupt deal-making via increasingly sophisticated models, it is important to remember that AI is not a panacea. The most successful companies looking to leverage AI technologies will also have strong, traditional practices such as financial analysis, data gathering, due diligence process, marketing, and distribution. The success of any merger or acquisition depends on aligning specific corporate goals with corporate strategy through various phases of planning and implementation.
