Managing key risks in deals involving proprietary AI systems

As featured on BusinessMirror:   Managing key risks in deals involving proprietary AI systems

In today's market, there is a growing trend of Artificial Intelligence (AI), which is intelligence demonstrated by machines, helping modern businesses increase revenue and boost operational savings. In particular, the applicability of AI technologies for a variety of use cases has enabled value creation for organizations across multiple industries, including IT services, health care, cybersecurity, financial services, retail, manufacturing, transportation and logistics.

In recognition of AI's benefits, private equity firms and corporations are progressively acquiring companies that leverage AI capabilities. While AI has increasingly embedded itself into organizational processes, we have seen several commercial, technology, organizational and data security risks inhibiting successful value creation in the deal context.

Proactively managing the key risks early in the process can help maximize the value of AI systems and position the buyer for digital leadership. “Business and corporate leaders must see things proactively by implementing security and safety measures even before certain risks arise,” Royeca added.

While driving the digital leadership use cases of AI can result in tangible business benefits, buyers need to consider the following risks in the acquisition of AI companies:

Sellers often showcase the commercial value offered by proprietary AI systems, which typically require several years to develop. However, the systems may be quickly outdated by off-the-shelf AI technologies, especially those developed by nimble software companies that have rapid product development cycles. In these instances, the revenue-generating potential and commercial standing of the acquired technologies would be limited.

Integrating AI systems into the technology environment after an acquisition requires the development of comprehensive data management platforms, the use of modern software development methodologies, and the reinforcement of mechanisms to extract advanced analytics insights. This requires robust technology capabilities, which the buyers may not have fully developed at the time of the transaction.

The rapid pace of innovation in AI requires that the talent pool be equipped with the required technological and operational skill sets, and able to quickly scale the organizational capabilities for evolving business requirements. An inability to identify, hire and retain suitable talent can pose a potential risk to the buyer’s strategy to fully leverage AI.

AI engines are fuelled by data, much of which can be sensitive consumer or proprietary information. Failing to protect this data from cyberattacks can create legal liabilities for the buyer, especially when operating in regions with strict data protection and privacy rules, such as California’s Consumer Privacy Act.

There is an opportunity to mitigate the key risks by applying the following framework in the pre-deal due diligence phase:

In the acquisition of AI companies, it is important to confirm that the seller's technology is unique and that it cannot be rebuilt using AI tools and solutions already available in the market. Also, the product strategy that dictates the degree of customization of the AI modules and their applicability to the buyer's business requirements should be factored into the diligence process. These considerations are important to mitigate commercial risks. 

The buyer should validate that the seller’s technology tools and processes can enable the maintenance and periodic upgrades of the AI technologies. These include AI development kits as well as maintenance modules from reputed vendors. These proactive steps would promote the seamless integration of the AI systems into the buyer’s technology environment, thus mitigating the technology risks.

In our view, while the rise of AI may appear to be eliminating the human element, the opposite is true. Skilled talent is still required to develop, monitor and fully leverage AI platforms. Ensuring that the seller's talent strategy fosters those skills from within and brings the right set of individuals into the team is an important step toward mitigating organizational risks.

An AI platform is enabled primarily by the data that drives the platform’s algorithms. In many cases, however, the data owned and managed by the seller is deemed sensitive and is governed by regulatory constraints. The seller should, therefore, protect the data against breaches through robust cybersecurity controls and policies to mitigate the data security risks.

Buyers need to recognize the commercial, technology, organizational, and data security risks involved in the acquisition of companies that own proprietary AI systems. Early assessment in the pre-deal due diligence phase can help identify and mitigate these potentially material risks and help position the buyer to maximize value by driving revenue generation and boosting operational savings.

^{The excerpt was taken from the KPMG Thought Leadership publication: https://kpmg.com/ca/en/home/insights/2023/01/managing-risks-in-deals-involving-ai.html}