AI as an M&A Target: Why Companies Are Buying Algorithms, Not Assets

The world of mergers and acquisitions is undergoing significant change. While acquisitions were once viewed primarily through a financial lens, today’s smart acquisitions are focused on a new type of asset: artificial intelligence.

In the current landscape, companies are not buying revenue, nor are they buying market share. They are buying algorithms, data, and the specialised expertise that goes along with building them. This transformation marks the beginning of a new phase of M&A where a company’s real value may be locked in its code.

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The Strategic Imperative

For large organisations, the greatest challenge often lies in their ability to respond to new technological change. Organisations go through a hiring and development process for AI units, frequently taking years and spending countless hours of human capital committed to that process and assumed compensation that is, at minimum, on par with a best-in-class CPA salary or a specialised CFA. 

The acquisition of an emerging, early-stage company which has created a viable AI solution is often the least cumbersome way to gain a competitive advantage and engage high-value talent to capitalise on its success. 

Primary Motivators for These Transactions 

1. Acquiring Talent: 

To get high-quality AI engineers and data scientists is as easy as acquiring a company. When acquiring a company, you can simply onboard a complete, experienced team who have already worked together. This will usually be the fastest path to hire talent. 

2. Access to Technology: 

The company may contain a proprietary, patented algorithm or unique AI model that’s very difficult and expensive to rebuild on your own. 

3. Getting Data: 

An AI model is only as good as the data models it is trained on. A reason for acquisition may be to get access to a proprietary or unique dataset, which can be an important competitive differentiator and difficult to replicate at scale. 

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Unique Aspects of AI M&A 

Acquiring an AI company has a different set of risks versus a traditional acquisition or investment. The due diligence performed should extend considerably beyond a review of the company’s financial statements.

1. Valuation Beyond the Balance Sheet

Traditional valuation approaches such as discounted cash flow (DCF) are not typically adequate. Many AI targets are pre-revenue and pre-profit, which means that value lies in potential, not in actual current financials; therefore, we need to think about the value of the talent in a new way.

Instead of comparing the very high CFA and CPA salary of a high-calibre finance experience professional to the ones received in transfer pricing, the acquirer needs to be thinking about the CFA interpreters’ and CPA interpreters’ salary packages relative to the even higher salary packages of the best AI research talent potentials.

2. The Intellectual Property Risk

Identifying ownership of the AI model and the copyright associated with the underlying data is complicated, as there are many risks that need to be tediously vetted, including risks from open-source code, third-party data licenses, or handling threats from ownership claims or disputes from the patent process involved for any intellectual property that may have arisen from the AI acquisition and the data used to develop it.

Importantly, the success of the acquisition lies in the acquirer retaining and securing the talent. Retaining the teams required for the success of the acquisition attempts is difficult because the teams are likely used to the fast pace, non-hierarchical, entrepreneurial culture that usually accompanies agile start-up life, which usually contrasts with the slow-moving, bureaucratic culture that larger corporations may exhibit.

 3. Talent Risk

To retain this talent, acquirers needed to be offering up salaries comparable to what a high CFA and a large CPA salary come with in the market. If the founding team or selected lead engineers leave to pursue new opportunities or simply to maintain a lifestyle of high CPA salary and good CFA salaries, it can be akin to granting that the tech acquired is now an unmanageable stranded asset, even though the CPA-CFA financial team, as previously stated above, approved the high-value acquisition in the first place. 

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Due Diligence in the Future

With AI increasingly seen as an M&A target, the due diligence process is also evolving. We are already seeing acquiring firms employ AI-powered tools to screen targets and expedite document reviews. In this new era, it will be critical for all financial professionals to understand and value AI.

This next generation of due diligence is making it a reality to analyse large sets of data and vast stores of unstructured data, including internal communications, to identify cultural fit and uncover hidden risks more quickly and accurately than ever before.