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Tech investment is trending up —and is expected to keep rising, thanks in part to the ongoing advancement in the machine learning (ML) field. Merger and acquisition (M&A) activity in the technology sector accelerated in 2018 with many companies seeking the competitive advantage of machine learning. Quick recap of machine learning, which is a form of artificial intelligence (AI), leverages statistical techniques to give computer systems the ability to "learn" from data, and to improve over time as they process even more data.

At a 451 Research Tech M&A Summit in New York City, panelists discussed trends in technology M&A, including the role of machine learning in deals, and what this could be telling us to expect looking forward.

According to a report presented by Brenon Daly, research vice president for 451 Research, M&A was in a slight decline for the two years prior to 2018. However, by October 2018 we saw the highest year-to-date spending total in the sector since the dot-com boom of the early 2000s.

The boom was led by strategic acquirers Microsoft, SAP, Salesforce and Adobe, which all announced multibillion dollar acquisitions, a dramatic flip from the previous year when none of the four did. This participation is setting the tone overall and bringing other corporate acquirers into the market, as well as creating a premium in the valuations of the companies involved.

The Competitive Advantage of Machine Learning

According to 451 Research’s 2018 survey, Voice of the Enterprise (VoTE): AI & Machine Learning – Adoption, Drivers and Stakeholders, nearly half of respondents said, “gaining a competitive advantage” was the most significant benefit to implementing machine learning, followed by 44 percent mentioning “improving customer experience."

A look at recent M&A deals highlighted in the report shows they are happening not between machine learning-only companies, but rather via deals that involve machine learning as a component. This approach points to the added value machine learning provides when buying a company.

An increasing number of buyers are entering and creating competition between hyperscale cloud companies, point solutions providers, private equity, and traditional industries that want to own ML and not just implement it. The competition, especially the ready capital flow from private equity, is creating premium valuations.

Consolidation and Verticalization: More Activity Ahead

However, even as the diversity and quantity of buyers and demand both increase, the number of companies on the supply side is growing dramatically. This means great opportunity exists in consolidation, particularly between point-solutions companies and managed-service providers, which are advising and assisting IT managers within the enterprise with data and cloud management. One example discussed of this type of consolidation is Rackspace’s acquisition of Datapipe, which allowed Rackspace to rapidly expand its abilities in managing multiple clouds at scale.

A lack of skilled labor is also driving high ML demand. While some panelists agreed technology would not replace human workers in the near future, machine learning is a resource to lower administrative work and empower human labor to shift focus and effort to more complex and growth-driven work. This value proposition will lead to traditional sectors, such as industrial and manufacturing, increasingly seeking machine learning M&A for competitive reasons. One example mentioned is John Deere’s acquisition of Blue River Technology, which applies machine learning to agriculture.

According to Conan Reidy, senior vice president of Rapid7, security is another sector that is bullish on ML. With ever-increasing amounts of data across internal and external users, platforms, and public and private clouds to manage and keep secure, he expects security firms to be active in the M&A market.

Valuation in Machine Learning M&A

So, what’s creating outsize valuations in technology M&A deals? When you look at the components that we measure when evaluating companies – growth and gross margin – and how they correlate to the valuation multiple, it’s very clear that the higher of the two is growth. Now when you think about how the benefits of machine learning are driving that growth, making companies more competitive and improving customer service, you see why having machine learning as a component of the deal can potentially create premium multiples.

For example, Berkshire Partners acquired Masergy Communications, Inc.in a $900-plus million transaction according to the Dallas Business Journal. Masergy Communications is a global provider of hybrid networking, managed security and cloud communications with the ability to use ML to smartly route traffic on different clouds. This was a phenomenal driver of their competitiveness and appeal to private equity.

Looking Ahead: What Will Happen to Volume and Valuations

Machine learning is still early in the adoption phase. At the same time as this deal-making boom, we’re seeing greater investment in AI education, too, such as the personal donation by Stephen Schwarzman, head of Blackstone Group LP, to Massachusetts Institute of Technology to establish a college of computing focused on AI. As the technology evolves, even more applications and enterprises will need to use it to remain competitive, and deals with machine learning as a component will become more prevalent.

However, the long-term impact of rising interest rates will affect company valuations going forward. If that happens, some investors may move capital away from stocks and into bonds, and valuations would need to take that into account.

Despite these macroeconomic trends, as elements of machine learning get incorporated into different traditional industries, such as healthcare, data centers, finance, and industrial, the sentiment is it will continue to lead to higher multiples.

To learn more, connect with Terry Schallich, Head of KBCM Technology Group at tschallich@key.com.

Key Learnings:

  • After a two-year lull, technology M&A reached record levels in 2018, and heightened activity is expected to continue in 2019.
  • Deals that include machine learning capabilities are driving the increased activity.
  • Companies report a competitive advantage from implementing machine learning.
  • Hyperscale cloud providers, point-solution companies, and private equity are all seeking deals.
  • Machine learning is ripe for consolidation and verticalization as the technology evolves and adoption increases.

Disclosures

This article is for general information purposes only and does not consider the specific investment objectives, financial situation, and particular needs of any individual person or entity.

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