Artificial intelligence and M&A: Are you getting the value you paid for?

Extracting value when acquiring AI companies

The sophistication of artificial intelligence will continue to accelerate in considerable ways—making deals involving this emerging technology both a challenge and an opportunity. Dealmakers will need to focus their attention around two key stages of a transaction: pre-deal and post-deal.

The rise of artificial intelligence and M&A

For an emerging technology, artificial intelligence actually has a long past, existing in some form for more than 60 years. But only recently have companies harnessed the power of AI to grow their business. From 2013 to 2018, the number of deals involving companies in the AI market increased exponentially as technological developments made the emerging technology accessible to many more people. Meanwhile, venture capital investments in AI startups have risen steadily, signaling that the market for AI acquisitions will likely be strong as companies mature and make their exits.

Increase revenue and profits

According to PwC’s 2019 AI Predictions survey of US business executives, most of today’s biggest tech companies are betting AI will improve productivity. More than that, what they expect most from their investments is revenue growth and increased profits, followed by improved experiences for customers and more innovative products.

Intent of investment

Whether companies intend to acquire talent, data or analytics, recent investments in AI have generally fallen into one of the following five categories:

Long-term bet

The buyer and target join forces to conduct long-term research into new AI applications aimed to accelerate growth of the overall business. Such deals include: Google-DeepMind, Intel-Nervana, Intel-Movidius; Microsoft-Lobe, Oracle-DataFox and Workday-Stories.bi.

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Platform play

Buyer acquires virtual reality or other platforms to integrate and enhance existing AI capabilities, as demonstrated in transactions such as Facebook-Oculus, Apple-FlyBy Media and Amazon-Body Labs.

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Core offering

Buyer aims to enhance its product offering with specific technology that could be a market mover for them. Examples include Apple-Realface, Juniper Networks-Mist, Avalara-Indix and Amazon-Kiva.

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Talent acquisition

Buyer acquires target for its AI talent or intellectual property developed through AI, as evident in such deals as Apple-Init.ai and Facebook-Ozlo.

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Analytics

Buyer acquires target for its analytics capabilities, such as in these deals: Apple-Turi, Apple-Laserlike, Google-API.ai, Google-Terraform Labs, SAP-Qualtrics, Salesforce-Datorama, Facebook-GrocStyle and Intel-Image processing technologies.

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Dealmakers are indeed bullish about AI. But before jumping into a deal, acquirers need to evaluate these transactions through a different lens, or else their investments won’t generate the value they expect. Deals involving AI aren’t always what they appear to be. According to a 2019 MMC Ventures study of more than 2,000 European AI startups, 40% were found to have no AI at all.

Because AI deals have several nuances that make these transactions unique, dealmakers should rethink the deals process by evaluating the details of the technology, as well as data of the target. Moreover, integration plans should consider responsible AI principles and practices to extract a deal’s value well after it closes.

Buyer beware: What exactly are you acquiring?

Buying versus building AI capabilities in-house has many advantages if companies can navigate the challenges associated with the transaction. An acquisition can help put AI technologies to use in the market faster, but companies also may have less flexibility to design a system tailored for their products or services.

Evaluate M&A from different angles

Should you buy an AI company?

Dealmakers need to think strategically and ask: Why is an AI company even worth buying? What business problem will the technology solve? Depending on a company’s business strategy, the reasons for acquiring AI capabilities commonly include:

  • to acquire innovative technology, such as breakthrough algorithms
  • to access proprietary data that’s unavailable or inaccessible through licensing
  • to leverage talent that is difficult to grow or attract organically

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Making a successful deal

For a deal to be successful, developing a rigorous method of evaluating the transaction will be key. The process should pull on a diverse set of experts, including financial experts, product managers, data scientists and technologists who can assess whether the target’s AI capabilities will be able to perform as promised. Together, they can evaluate deals from different angles and address questions such as:

  • Technology: Do buyers have adequate technical infrastructure to support an acquisition?
  • Talent: Do employees, separate from data scientists, have the necessary skills to operate the new technology?
  • Product strategy: Will the acquired AI system or machine learning algorithm perform the same in its target environment if, for instance, the customer segment, geography or other variables were to change?
  • Data: Will the target’s data, which powers the AI technology, be able to evolve and improve over time?

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Assessing AI companies: Adopt an integrated due diligence approach

AI doesn’t work without the right data, but it’s not uncommon for targets to over-promise the accessibility and uniqueness of these assets. Product managers, data scientists and technologists can effectively contribute to commercial diligence efforts to assess how the data compares with competitors in the market.

This level of collaboration has paid off in big ways. In a recent deal involving a tech company acquiring an AI-based cybersecurity company, the latter had the capabilities promised, but a closer look found that the technology wasn’t as differentiated as it would have been a few years ago. This was discovered through an integrated diligence process, in which the product technology diligence team worked closely with the commercial diligence team to interview customers and evaluate the commercial and competitive aspects of the company.

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PwC’s Marc Suidan explains why dealmakers should enter AI acquisitions with eyes wide open.

Responsible AI: Where dealmakers can extract value

To ensure that a transaction reaches its optimal value, companies should also focus on responsible AI. This starts with an integration plan that considers the evolving sophistication of the technology. A plan should not only outline when and how the assets and operations of an acquirer and target would combine, but it should also cover how the new company will respond to regulations and social standards likely to shape the future of AI.

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PwC’s Technology Leader, Scott Likens, explains how automation will create new roles and opportunities for companies and their employees.

How to achieve responsible AI

The rise of AI brings inherent challenges around trust and accountability with customers, governments and other stakeholders. To mitigate these risks, companies acquiring AI capabilities should integrate a governance system to address the following principles and practices of responsible AI:

Ethics and regulations

Dealmakers should coordinate with the acquirer’s and target’s compliance departments and other stakeholders to address ongoing and developing regulations. Take, for instance, privacy laws. As an acquirer and target combine key data that powers the AI, it will be important to assess if the systems comply with Europe’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) and other relevant privacy laws. If the systems aren’t in compliance, the acquirer, target or both should consider mitigating solutions, such as anonymizing data. In addition to existing legislations, acquirers should also examine the Institute of Electrical and Electronics Engineers (IEEE) and the European Commission’s guidance on AI Ethics.

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Robustness and security

AI systems need to be secure and safe. The value of a deal involving AI often hinges on the notion that the data being acquired is untampered and protected. As a result, dealmakers should evaluate how vulnerable a target’s AI systems are to cyberattacks and whether proper controls are in place. This starts with involving the chief information security officers (CISOs) in the deal early on, so that security experts can help a deal team understand potential synergies with the target environment.

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Interpretability and explainability

At some point, any business using AI will need to explain why a particular AI model reached a particular decision. Dealmakers should pull on the target’s management, data scientists and others with knowledge of the models to address this question and tailor the explanation to different stakeholders, including regulators, business sponsors and end consumers. Doing so inspires confidence among employees and consumers in the AI technology acquired and can further protect the acquisition’s value. Acquiring companies should ensure the AI they buy is not a black box and can be recreated and understood by either their own data scientists or external experts.

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Bias and fairness

Bias, such as gender discrimination in hiring, is one of the biggest risks associated with AI. Bias can show up in datasets that train algorithms either because the data is unrepresentative of reality, or it reflects existing prejudices. Say AI powers a company’s internal employee recruiting tool, and the algorithm was trained on historical hiring decisions that favored men over women. Chances are the recruiting tool will likely do the same. To reduce such risks, it’s critical for dealmakers to consider plans to tune AI systems to mitigate bias and ensure that decisions adhere to the company’s corporate code of ethics, as well as anti-discrimination regulations. Further, it will be critical for companies to continually test for bias in data, models and human use of algorithms.

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Bottom line

Before considering a transaction, dealmakers need to evaluate the deal's rationale by asking:

Why buy an AI company?

If dealmakers decide to buy, they will need to assess the risks and make sure they’re buying what they think they’re buying. This means This means rethinking the deals process by taking the following steps:

  • Pull on a diverse set of expertise to evaluate the deal, forming a team of financial experts, product managers, technologists, data scientists and others who can assess the AI target from different angles.
  • Examine the technical infrastructure and necessary skills to operate the new technology.
  • Assess whether the data that powers the AI technology will be able to evolve and improve over time.
  • Evaluate whether the acquired AI system or machine learning algorithms can be applied to the acquirer’s unique business model, customer base or other features.

What problems could an AI acquisition solve?

Once the deal closes, the work must continue in order to preserve the transaction’s optimal value. Start early by developing a comprehensive integration plan that includes the following:

  • Operationalizing AI: Evaluate the human role of AI and decide how employees will help operate and manage the technology.
  • Apply responsible AI: Develop a framework that will address how managers and employees will apply the principles of responsible AI, such as ethics and regulations, security, interpretability and bias.

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Global & US Artificial Intelligence and US Data & Analytics Leader, PwC US

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