AI as a value creation tool for PE portfolio managers
As private equity fund managers are keenly aware, the data amassed by an acquired company represents a valuable asset that should generate significant returns. However, it is a common scenario that these data assets, acquired at considerable operational and infrastructure expenses, often sit dormant once their initial business transactional purpose is fulfilled.
By systematically integrating data and artificial intelligence (AI) value creation into their portfolio management strategies, private equity firms can harness these digital assets for productive use. This leads to a discernable impact on enterprise valuations, uplifting both EBITDA – through enhanced productivity and top line improvements – as well as valuation multiples – by catalyzing growth and broadening the spectrum of potential buyers.
AI for enhanced profitability
The implementation of a comprehensive data & AI transformation roadmap throughout a portfolio company’s value chain yields three primary benefits to boost profitability:
- Driving down operational costs and elevating margins,
- Unlocking revenue upside,
- Opening up new adjacent business models.
1. AI-enabled operational optimization
AI's potential for cost reduction
While the potential of AI to automate repetitive tasks is widely recognized, there are many other ways to increase efficiency with analytics insights and machine learning algorithms. This includes supply chains optimization through data-augmented supplier decision making, enhancing suppliers interactions, and optimizing sales efficiency through lead prioritization. Integrated data assets serve as the foundation for these productivity improvements across various operational facets.
Dynamic pricing for margin optimization
Harnessing artificial intelligence, particularly unsupervised learning algorithms such as k-means clustering, enables portfolio companies to finely segment their customer base. Recognizing that different segments exhibit varying willingness to pay for the same product, pricing engines leverage these patterns to dynamically generate prices or provide real-time price recommendations. This process takes into account factors such as supply, demand, and discrete supply chain events, culminating in a dynamic pricing strategy that maximizes profits through margin optimization.
2. AI helps companies sell more by making better products
The introduction of usage analytics and predictive models enables product managers to pinpoint innovations with the most significant impact on user consumption. This enhanced precision accelerates the product development cycle and the experimentation process, resulting in a faster pace of innovation. The outcome is an improved ability to attract customers and gain market share through the delivery of products that better align with customer preferences and needs.
3. New revenues from data-enabled adjacent business models
As PE-owned companies elevate the quality and quantity of data incorporated into an expanding array of algorithms—initiating a virtuous circle of AI—they concurrently gain a comprehensive understanding of their data ecosystem. This heightened awareness enables:
- a dual role for customers as providers and consumers of data,
- data exchange with partner companies,
- the integration of public data sources with internal assets to produce new market insight.
This in turn unlocks the potential for innovative business models, such as Data-as-a-Service and subscription models. This diversification introduces new revenue streams from previously untapped customer segments. AI transforms data into a strategic asset, paving the way for accelerated growth and profitability for PE-owned companies.
AI-enhanced multiples moving towards tech valuations
AI impacts the long-term trend of key financials
Machine Learning’s long-term effect of ‘bending the curves’ of costs downward and of sales upward helps explain the stratospheric valuations of AI-centric tech corporations. This underscores why businesses boasting a higher degree of AI maturity can command superior valuation multiples compared to their direct competitors that treat data as a passive asset. For PE funds, there is a compelling rationale to advocate for portfolio companies to invest in becoming data- and AI-driven. This strategic shift will start moving the valuation needle away from traditional sector benchmarks and towards tech and VC multiples.
Deepening the pool of buyers by harnessing data ecosystems
A further benefit of portfolio companies accumulating extensive data assets to fuel AI innovation is to raise visibility among larger players operating within the same data ecosystem. In their own M&A approach, these larger players may prioritize strategic investments to gain a competitive data advantage over conventional considerations of underlying profitability of an acquisition target. This emphasis on data leverage opens avenues for achieving valuations beyond the norm in PE-to-PE secondary market transactions that predominantly focus on financial metrics.
Also, PE fund managers can actively use their portfolio companies’ data ecosystem maps to identify future strategic buyers, potentially lowering brokerage fees at exit point.
Conclusion and path forward for PE investors
The transformative journey for PE-owned portfolio companies towards becoming data- and AI-driven businesses yields multifaceted benefits. They experience enhanced profitability through cost reduction, optimized pricing strategies, sales growth, and the unlocking of adjacent markets and new business models.
The augmentation of valuation multiples is the second notable effect. AI-enabling a traditional brick-and-mortar company not only accelerates its growth trajectory but also makes it more visible and appealing to strategic buyers. This heightened attractiveness often translates into a higher valuation multiple at the exit, maximizing returns for PE investors.
Impact of AI on valuation multiples
PE investors uniquely geared to drive valuations with AI
Some companies are born digital, but for the others the opportunity is in making them data-driven. Transforming a traditional company into an AI-enabled one is a clear case where the active leadership of private equity can do a better job than the average company left to its own devices.
An AI transformation roadmap does not need to be complicated, but it must be strategic, with the clear objective of producing a data-driven company at the time of exit. Given a typical holding period of 4-5 years, the execution should be pragmatic and focus on quickly validating and demonstrating the AI investment thesis.
Organic AI Transformation at scale across PE portfolios
Crucially, the AI transformation should occur organically at the core of the company. While hiring AI consultants may bring rapid results, strategic buyers are interested in acquiring an AI-driven business with a deeply ingrained data culture. The aim is not to have a traditional company digitally sustained by external experts, but to foster a self-sufficient, AI-driven business.
At LastingImpact.AI, we specialize in igniting and nurturing AI-centric innovation within the leadership and operational teams of portfolio companies. We show them how to ‘do AI’, help them draft their own AI strategy, and coach them in their first implementation steps until they achieve full self-sufficiency.
We operate at scale across PE fund portfolios, leveraging synergies between portfolio companies to accelerate learning and innovation. Our consistent and parallelized approach brings substantial cost efficiency benefits, ensuring that the AI-driven transformation not only enhances valuations but does so in a sustainable and efficient manner.