AI Is Changing M&A as Regulators Target ‘Killer Acquisitions’ and Data Control 

That shifting backdrop is the focus of a new post from Herbert Smith Freehills Kramer on AI’s growing role in mergers and acquisitions. The firm argues that AI now shows up in two places at once. First, it affects the business case for deals. Second, it changes the work of doing deals, especially the diligence and drafting that can bog transactions down.

On the business side, the post says buyers are weighing AI as both opportunity and risk. Some corporate acquirers are looking for targets that help them keep pace with the changing tech landscape. Some private equity sponsors are looking for industries where AI can cut costs and expand margins. At the same time, others are cautious about buying companies that may not be ready for AI-driven disruption. The post also points to “compute” adjacent assets — such as data centers and power providers — that are benefiting from the surge in AI investment.

The post notes that AI is no longer limited to tech deals. It frames AI as “sector-agnostic,” meaning it can matter whether a target is in manufacturing, retail or any other industry. If a business fails to adopt AI, or is exposed to disruption from it, that can affect its long-term outlook.

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The hard part is uncertainty. AI is evolving quickly, and that makes it difficult to predict which models, companies and industries will win. Buyers, the post says, should think through issues like cost uncertainty, sustainability, the risk that an AI model becomes outdated, adoption costs and the quality and ownership of data.

AI is also changing how regulators view deals. The post notes that regulators increasingly are looking beyond today’s market shares and revenue and focusing more attention on what a market could become. Per Herbert Smith, “Regulatory scrutiny is another key factor – around the world, regulators are not just looking at current market size/turnover but also the future potential market size.”

It also flags a growing focus on “killer acquisitions,” where a large firm buys a smaller, innovative company to stop a future rival from growing. It also highlights concerns about data dominance, where the main value of a deal may be access to data rather than the product itself.

AI is also becoming a critical factor in performing diligence, according to Herbert Smith. Has the target complied with laws and contracts in how it uses AI and data? Does it have the right permissions from customers? Are there restrictions on cross-border data flows? What do contracts with AI providers say about liability if something goes wrong? Are there reputational risks, such as bias in training data or algorithms? And are there intellectual property risks tied to how AI systems were built or trained?

Over time, as tools improve and platforms become more integrated, the amount of human review may fall. For now, the expectation is more AI in the workflow, paired with guardrails, oversight and a clearer playbook for safe use.