Mergers and acquisitions (M&A) stand at a crossroads as we enter the summer of 2025. Geopolitical tensions, economic headwinds and rapid technological advancements are forcing traders to rethink how they originate, structure and close trades. Trade policy is becoming the main variable. Unpredictable tariffs, transfer alliances, and growing regulatory scrutiny have pushed global trading activity into more cautious areas. However, in the uncertainty, AI began to focus.
AI is no longer a futuristic add-on. This is the core of the company’s approach to mergers and acquisitions. In climates, speed, accuracy and risk management are more important than ever, and AI brings key advantages to traders. It can help surface opportunities, stress test hypotheses and on-site risks faster before the deal derails. AI not only needs to carry out mergers and acquisitions faster. It makes it smarter.
Trade uncertainty is reshaping M&A strategies
What changes U.S. trade policy is to stagnate cross-border transactions and make future revenue streams more difficult to predict. As a result, traders face a challenge from both sides: how to maintain momentum while isolating portfolios from geopolitical shocks.
Some effects are already evident in the data stone, which processes more than 19,000 new transactions per year. In the first four months of this year, new deals in global trading, especially asset sales and mergers, grew 4% worldwide in the first four months of this year. Since these are deals before the announcement, it is a good idea to understand what is coming and some of the momentum that has already happened.
However, be cautious. After the first large-scale tariff announcement in the United States on April 2, the transaction completion rate of Data Stone fell to 44%, down from 49% year-on-year (YOY). This means buyers are slowing down. They want more time to assess the risk. They ask more questions. They are probing beautiful prints and, if necessary, they are walking away.
The key reason is tariffs. When tariffs are imposed on imported goods or raw materials, they can directly affect the cost structure and profit margins of the target company, especially cost companies with global supply chains. This causes volatility in financial forecasts, which complicates the valuation model and prevents trading. Buyers are trying to assess whether target current revenue performance can be maintained under changing trade conditions, and they face increased risks. In many cases, tariffs prompt companies to reconsider expansion or acquisitions within certain countries, shifting M&A activities to regions with more stable trade relations.
Furthermore, ongoing trade tensions, such as those between the United States and China, have led to an increase in regulatory scrutiny, which further delays or derails. These combined factors force traders to spend more time doing due diligence, modeling various tariff plans and adding protective clauses to deal with the structure. This then makes the M&A process more complex and expensive.
Tariffs not only increase operating expenses, but also reshape strategic plans by making the integration of long-term growth, ROI and cross-border transactions more difficult.
Now, risk models often consider tariff exposure. Buyers are not only focusing on the target company’s revenue today, but also looking for how future trade policies affect cash flow. With the changes in investment mathematics, some transactions, especially cross-border transactions.
To remain competitive, traders must adapt. This means better tools, faster workflows, and stricter diligence. This also means building flexibility in the trading process to explain economic volatility.
AI simplifies diligence and strengthens risk control
This is where AI intervenes. It helps the trading team process more information with less time and greater accuracy. Due diligence is a critical but resource-intensive process that traditionally involves manually viewing large amounts of documents and information. This approach can be time-consuming and labor-intensive and often puts significant pressure on professionals, especially when working under tight deadlines. As a result, the quality and thoroughness of the review may be compromised. AI solves this challenge by enabling faster and more efficient analytics. AI tools can quickly organize, summarize and highlight key terms and related obligations in the document, allowing traders to focus on the most important information. This not only improves accuracy, but also greatly reduces the time it takes to complete the due diligence process. For example, AI can organize, classify and tag critical data in thousands of documents in a virtual data room in real time and help reduce human error and ensure compliance with regulatory requirements.
Not surprisingly, one in five trading companies now use generative AI during the M&A process, and more say AI adoption is their highest operating priority this year. Why? Because the merger script is changing. The comments are even more intense. Regulators raise more questions. Investors demand more in-depth insights. AI helps answer calls.
Virtual data rooms are also constantly evolving. Now it is common for trading teams to use AI-driven Q&A tools to ask for information before taking action. In fact, the use of Q&A tools on data stone has climbed since the beginning of this year, reflecting the increasing demand for sellers to prepare quickly and thoroughly react to buyers who want to see clean, complete data.
Furthermore, AI is increasingly playing an important role in identifying potential acquisition targets. By analyzing various market signals such as company descriptions, geo-compatibility and size-related standards, AI can help buyers point out the right candidate more effectively. These insights often come from a combination of public, private, and proprietary data sources. As a result, some AI-powered platforms have enabled traders to discover potential targets faster and accurately. This proactive approach can improve strategic consistency, making it easier for companies to integrate new capabilities after acquisition and achieve growth goals for transaction intent.
AI can also contribute to the valuation process by providing data-driven analytics based on historical trends and current market conditions. It can also automate routine and labor-intensive tasks, such as sensitive information in documents. By simplifying these operational steps, AI allows professionals to focus more on advanced strategies and innovative thinking, ultimately improving the quality and effectiveness of decisions throughout the M&A life cycle.
Traders must move from responsiveness to proactive
In today’s environment, the best moment to wait for a deal is not a strategy, but a responsibility. Time is important, but preparation is even more important. Those who succeed in this market will be those who are ready for early investment transactions. This can include cleaning up finances, mapping supply chain dependencies, reviewing IP portfolios, and being consistent by terms of transactions.
Of course, artificial intelligence alone is not the answer. The best strategy combines human insight with machine intelligence. Use AI for surface options. Use your team to make a call. Technology should guide the process, not replace judgment.
The future of mergers and acquisitions is here
Mergers and acquisitions will always bear risks. But how to manage this risk is changing. AI is improving the bar. It provides traders with faster, smarter and more visionary tools.
In a world where tariffs may continue to evolve, regulators can change courses in mid-term review, speed and insight. The future is a data-driven, technologically advanced and strategically agile trader.
