Artificial Intelligence and Its Financial Impact on Business Valuations in Mergers & Acquisitions

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Although artificial intelligence and machine learning date back to the 1950s, we’re in the industry’s infancy with respect to mainstream adoption, whether consumer or enterprise.  Nevertheless, we’re already seeing how the use of AI applications in a company’s business affects its price and valuation for corporate acquisition or other purposes, whether a family-owned business, a corporate strategic divesting a business line, or a private equity sponsor selling a portfolio company.  The following are certain considerations when implementing an AI strategy that impact a company’s purchase price in merger & acquisition transactions.

Utilization of AI Applications Creating Valuation Premiums

Proprietary data applications utilized by a company for operational efficiencies and predictive analytics have long been coveted by buyers when assessing a target, particularly if such buyers don’t need to implement these systems post-closing.  In conjunction with a company’s internal data, the new use of AI applications by businesses to help analyze and automate its operations in the following ways, with corresponding results, have already become widespread in the corporate arena:

• revenue growth through AI assisted sales prospecting or dynamic pricing

• cost reduction from automation and improved operations

• streamlining systems and software development that scale without additional cost

• customer experience applications creating goodwill and retention

• cybersecurity and risk prevention

• augmented analysis of internal company data, whether in customer purchase patterns, supply chain management, financial forecasting or otherwise.

Such use of artificial intelligence in a company’s operations can translate into financial improvements that increase profit margins, thereby justifying higher valuation multiples, resulting in higher purchase prices.  As a result of such financial gains and the use of AI by enterprises now and in the future, AI utilization has to be taken into account when valuing a company going forward and in related purchase price negotiations.

In addition to improved financial margins, the proprietary AI and data sets utilized by a business can potentially be treated as actual assets in and of themselves whether such data or program is valued separately for sale to a third party or used internally to improve the business.  As a result, many valuation professionals are treating such AI applications not simply as another software program used in a business, but actual intellectual property with designated value akin to patents or customer databases, and as a result, can lead to valuation premiums

Valuation Discounts From AI

While AI applications utilized in business operations can result in financial gains, artificial intelligence can also present challenges that can cause an unexpected valuation discount for a business compared to years past.  Businesses whose primary offering is comprised of once valued repeatable services such as content generation or administrative operations, are at risk of being displaced by automated AI applications.

In certain cases, buyers of businesses may now apply lower valuation multiples in industries where AI applications can perform such services on an inexpensive basis, for example, graphic design services, bookkeeping, certain legal services or financial analysis.  In other words, what in the past may have been somewhat expensive to perform or prepare, resulting in a strong purchase price for such businesses, may no longer command such purchase price compared to five years ago because the value of the service has become or is quickly becoming less expensive to third party users since AI can oftentimes perform such services for a fraction of the cost.

Positioning for Higher Valuations Using AI

In order to potentially increase the valuation of a company upon its sale to a third party, sellers who have integrated a thoughtful and successful AI strategy will typically want to highlight that strategy to third party buyers by potentially adopting the following.

Display Causal Links Related to Financial Impact

When providing a company’s financial performance, try to present evidence of where an AI application or strategy has directly and positively impacted the company financially, as opposed to simply expressing that the business uses AI or doesn’t at all.  For example, implementing an AI application may have directly reduced certain inventory and supply chain costs, thereby increasing profit margins.  This financial impact from the use of a proprietary AI system can and should be directly expressed to a buyer to potentially warrant a higher valuation due to the company’s unique and optimized technology that can produce superior financial results now and in the future.

Structuring Your Financial Performance into Components

Potentially structure and display the company’s valuation or financial performance into components.  For example, valuation or financial results utilizing AI applications in the business, those results without AI, and/or how the value of the enterprise can increase if existing AI applications are further used in the company’s other related business lines or operations, whether existing or non-existing.  Such delineation can help buyers and investors understand where AI provides additional value rather than simply getting lost in the whole as just another business software program.

AI for Strategic Differentiation and Competitive Advantage

If applicable, try to implement and display AI applications that deliver a unique or competitive advantage for the business, as opposed to only adopting generic AI software simply because it seems like the thing to do or is the “sign of the times”.  For example, implementing AI applications for better prediction, optimization and personalization that not only create financial gains, but systems that competitors cannot easily replicate.  The more AI is tied to a company’s proprietary data for optimization as well as market insights, typically the better and more likely to create an increase in value compared to other businesses without in the same space.  Said another way, when a buyer is looking for a target in a particular industry, the company that has integrated financially successful AI and data systems is typically going to be a more preferable target to buyers, thereby oftentimes creating a higher purchase price, as compared to its competitors who haven’t implemented such valued systems.

Looking Ahead

Adoption of AI applications to grow revenue, reduce costs, automate routine tasks, and generate predictive analytics is accelerating rapidly across industries. To increase a company’s valuation at the time of sale, AI integration should produce a clear and measurable financial impact that can be demonstrated to prospective buyers. In addition, AI initiatives should focus on building internal, proprietary systems that generate unique market insights and are difficult for competitors to replicate, allowing them to be treated as separately identifiable intellectual property assets and forming a defensible data moat. When a company can substantiate both financial gains and proprietary advantage from its AI utilization, it becomes more valuable not only on a quantitative basis, but also from an intangible perspective to both strategic and financial buyers alike.