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The Hunt is On for AI Synergies in Private Equity Portfolio Companies

iStock 1315757284 - Global Banking | Finance

The Hunt is On for AI Synergies in Private Equity Portfolio Companies

By Drake Paulson, VP Strategic Partnerships, Anduin

Drake Paulson VP Strategic Partnerships Anduin - Global Banking | Finance

Drake Paulson

It’s not news that private equity firms act as technology strategists and guides to foster synergies between the companies in their portfolio. In 2024, their focus is on AI-enabled synergies within the portfolio. In this article, Drake Paulson, VP of Client Experience and Partnerships at Anduin Transactions, analyzes how the initiatives of synergies and AI-enabled transformation intersect in 2024, and can increase fund returns.

Hunting for AI success

The ceiling for return on investment from AI is unknown—but it’s expected to be astounding. The ROI might far exceed the gains that companies experienced by migrating to cloud technology. It’s no surprise that now private equity managers and venture capitalists are almost insistent their portfolio companies work with AI. They’re striving for AI early-mover advantages and perhaps a winning lottery ticket, especially in their largest investment sectors: software & technology, industrial, and services.

Fund managers may not yet know which AI use cases are game-changers for their portfolio, but they are convinced AI will bring a blockbuster somewhere in the mix. A single AI home run could be the rising tide that lifts all boats, putting proprietary differentiators at the disposal of their other portfolio companies. 

AI synergies: Get creative

As private equity casts a wide net and thinks outside-the-box to identify rich synergies, they also seek investments that leverage AI. Some have formal AI committees. Smaller firms usually rely on their creativity and imagination, and their advisors. They look for precursors and correlations that point to new businesses where AI will be transformative. Many approaches are tenable, but we see private funds emphasizing three: 

  • Technology breakthroughs. These can take two forms: first, when AI allows an existing capability to leapfrog from clumsy to transformative; second, when an AI capability makes a quantum jump from questionable efficacy to real-world viability.
  • Synergistic mergers and acquisitions. Otherwise unexciting combinations might have dramatic ROI potential once the right AI capabilities are added.
  • Data-related products and services. This may sound traditional; it is anything but. The basic formula is combining Company A’s data with services or data from Company B, then adding value with AI. Here, the surface has not been scratched. 

Disclaimer: the following examples are a mix of real and anticipated projects. 

Breakthroughs resulting in viability

AI has proven potential to take some industry-specific technologies from “almost viable” to transformative. Often, this happens when AI improves automated processes or machinery. Agricultural robotics are a good example. A picker-bot choosing ripe apples in an orchard performs far better with AI. WIthout on-the-job learning, current robots pick unripe fruit and damage them too often.

Once application breakthroughs happen, they tend to have cross-industry applications. The new AI-enabled technology could actually become more valuable than the industry it serves. 

Companies as building blocks

Creatively combining multiple companies through mergers and acquisitions can launch new ways to serve a market segment. Private equity is no stranger to techniques like stacking companies vertically, consolidating them for buying and marketing power, and joining service providers to create a wider spectrum of offerings. 

Just imagine this hypothetical scenario: AI-enabled machine vision that monitors tires ($600 each) on a semi rig to detect when they need rotation or replacement, while also watching underneath the chassis for leaks. The result is a just-in-time maintenance business that augments existing fleet management and reduces breakdowns.

Another transportation use case is real-time monitoring of vehicle drivers (does the driver check left before entering an intersection?) paired with driver-specific insurance. Coverage would be accurately priced for the individual driver, as AI processes the data stream to continuously reassess that driver’s risk. It could intervene when a heightened risk is detected, such as signs of sleepiness or distress. 

Capitalizing on next-generation AI-enhanced data 

Every organization and person generates data in a variety of forms, much of it marketable. With AI, even more of it could be. The range of ways AI can add value to data keeps getting larger, and the sheer volume and depth of data to work with continues to grow. If this category has boundaries on potential returns, they are not obvious.

To drill down a bit, AI can easily filter and synthesize information from data that arrives in completely different forms. It can merge, normalize, and interpret data from many internal, purchased, and public sources (including social media) then enrich and repackage it to create value in other markets. Generative AI speeds up data integration, analysis, and decision-making across organizational boundaries.

An exciting global use case stems from the aging populations of Europe, the US, and parts of Asia. Too many elders, and not enough home aides or adequate facilities to care for them, have led to a runaway disaster of elder neglect, loneliness, abuse, and fraud. Already, basic voice-driven AI-based personal assistants draw on different data sources to help seniors solve everyday problems. As their functionality blossoms, they could enable hundreds of millions of seniors to live longer and live independently. This functionality should extend far beyond medicine reminders, to alerting family or authorities when signs of dementia, strokes, abuse, fraud, depression, or an impending fall appear. On the brighter side, they can encourage healthy eating, contact with friends and family, jog their memory, and dispense advice.

The insurance field is also ripe with data opportunities. AI apps can combine drone-captured aerial views of properties with streetside analysis and public records for historic risk and catastrophes to model various levels of coverage and create informed offerings and pricing.

Artificial intelligence, real profit

Efficiency gains for intra-company AI solutions are usually reported as 15% to 25%, but could be exponentially higher for synergistic, inter-company deployments of AI. “Develop once, benefit many” has excellent ROI math. The AI innovator gets a test bed and early references from other portfolio companies, which in turn receive early access to a proprietary AI springboard.

It doesn’t stop there. For private equity and funds, once AI is established in their toolkit, they can continue adding value to future and current portfolio companies. Companies that develop expertise in personalizing AI to their methods and processes can expect a very impressive ROI of 150% to 250% and higher.

Fund managers are rightfully energized by those far greater potential returns. They have many options to fund fruitful AI synergies, and returns from investing in AI might just eclipse everything else that private funds do for their portfolio companies. 

Global Banking & Finance Review


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