What value can artificial intelligence bring to M&A transactions?
Mergers and Acquisition transactions are always challenging times for companies. Businesses have to decide whether a target company is worth the deal. Consequently, there’s an ocean of paperwork, online sources, and records, to analyze.
Most of the problems around M&A deals trace back to insufficient due diligence, and that’s where AI and machine learning tools step in to take over the drudge work.
With the value of merger and acquisition deals worldwide standing at approximately 3 trillion dollars, in line with a Statista survey, it is paramount that companies get M&A deals spot on.
In this article, we’ll be discussing how to use AI to turbocharge your merger & acquisitions for a better understanding of the role of technology in the process.
Let’s get started.
1. Accelerate Business Valuation
When companies are hunting down possible M&A opportunities, business valuation is one of the topmost factors in deciding whether to look further into a deal or move on to another option.
What method of business valuation does your company rely on when making M&A assessments?
The most popular technique today is comparable analysis, where a business’s value is measured against its like-for-like competitors in the same line of business. That entails considering ratios like EV/EBITDA and P/E, among other multiples, to create reference values.
Whatever valuation strategy you rely on, the bottom line is that without the aid of AI, traditional business valuation tactics are lengthy, tedious, and time-intensive.
In that regard, there are tons of AI-powered business valuation software on the market, which rely on machine learning tools to retrieve EBITA information in real-time.
By combining share price data and EBITA multiples, among other factors, as the changes occur, this live database enables accurate valuation at various points in time. Analysts can even run what-if scenarios to see how a prospective target or partner would perform in varying market conditions.
Some AI-driven valuation tools of today further simplify the process by providing valuation multiples to use, depending on a company’s trading currency.
2. Comprehensive Market Insight
Companies often want a thorough analysis of the new markets they’d be venturing into when acquiring or merging business operations.
In fact, many businesses often perform a market analysis first before thinking about specific M&A partners in that market.
If the transition or expansion would involve the inclusion or switch from the financial sector to the medical industry, for example, it would be prudent for the company to be aware of the prevailing market condition in that sector, and how it would perform in the years to come.
This market research work may involve carrying out an actual study, or analyzing several market reports or surveys, among other informative material.
AI-powered Data Extraction Software and tools step in to help by automating the extraction of sector or market data, and provide a substantial and accurate source of market information.
Instead of making inaccurate decisions by relying on market biases, companies can make decisions in line with the situation on the ground.
In the long run, businesses can quickly and more accurately analyze a depth of markets across countries, making a selection with a clear view of the best ROI.
3. Convenient Contract Analysis
Contract Analysis is among the key reasons why M&A transactions run for months. That’s because these documents can be available in different formats, across various systems.
With unstructured layouts, traditional data ingestion tools will fail at retrieving all important information as needed.
Therefore, AI-powered contract analysis tools, which rely on OCR, ML, and NLP are desirable. These offer the capability to sift through data stores of whatever kind and locate contracts, and crucial information or clauses within them.
With hundreds, sometimes even thousands, of new contracts needing analysis during an M&A process, AI-based contract management software can speed up the work allowing employees to focus on other aspects of the transaction.
Companies can flag down terms of payment, conditional clauses like renewals, and other vital details.
Via machine learning from past contracts and through keyword specification, contract analysis tools can point out important information in contracts for further review.
4. Seamless Data Integration
Finally, once both companies have agreed that everything is in order and have put ink to paper, now comes the next difficult part: data silos.
Naturally, two initially separate organizations have distinct data management processes. Sometimes, there’s a need to even consolidate data from physical records through various manual data entry methods.
OCR-powered data extraction tools can help create digital file cabinets, incorporating manual records from both sides.
In cases where companies’ records are digitized, uniting legacy systems can still be a problem during the M&A implementation.
To break down digital data silos, content intelligence platforms offer a solution to bridge different data management strategies between merging companies. That way, the limitation of data view across the newly expanded network is lifted.
These AI-driven tools digitize data across the new networks in an organization, enabling a data lake to be created on a shared virtual storage medium, such as the cloud.
Therefore, two new organization partners can access data from various departments across the network, even if they are worlds apart.
By leveraging AI for data integration, your company can be fully aware of the extent of resources available to it. What’s more, the management can better implement a new data governance strategy as a result.
5. ESG Compliance Checks
Does your company prioritize ESG policies when prospecting for M&A options?
The Environment, Social & Corporate Governance (ESG) entails a variety of factors including climate change, working conditions, and tax strategies, to name a few, which establish a company’s ethical standing.
ESG is an important criterion of M&A Due Diligence. That’s because getting into business with non-ESG-compliant partners has a direct impact on stakeholder trust, and the public image of the brand.
With AI-powered data analytics, it becomes easier for your business to carry out ESG research by eliminating a lot of leg-work.
AI-powered data insight tools entail a combination of natural language processing, machine learning, and optical character recognition. These capabilities facilitate the scanning of huge volumes of documents in no time.
Using data extraction software, you can quickly highlight vital ESG details from mountains of information encompassing a wide range of information sources.
Companies’ data analysts can figure out links to corruption cases, tax evasion, or climatic degradation, among others, from online news outlets, or even from documentation shared between M&A parties.
To recap, what can AI do for your business with regard to mergers and acquisitions?
Tap into the power of AI for contract analysis, EGS checks, and most importantly, to find the most profitable M&A targets on the market. In addition, AI tools can reduce the amount of time and resources that companies channel into these deals.
In 2020, Investopedia research showed that the entire M&A cycle took on average between three and six months for a company to conduct, and even then, there were still considerable stones left unturned. By 2025, artificial intelligence is set to reduce that time to less than 30 days.
So if you’re not using AI for M&A processing, your business will be spending a lot more time and resources than necessary.