The impact of generative AI on private equity firms

Adoption of generative AI may give some private equity firms an advantage but could also attract more operational risk. Find out more.

GenAI may give private equity firms an advantage but could attract more operational risk.

The rise of Generative Artificial Intelligence (genAI) has propelled AI from a quantitative tool to board priority in 12 months. How will this impact private equity (PE) firms? How can they thrive in this wave of innovation? What can they do now to gain an edge? KPMG interviews Per Edin, Board Committee Chair and AI Go-to-Market leader at KPMG in the US and Chris Coulthrust, Senior Solution Architect at Microsoft to find out more.

What does genAI disruption mean for PE firms?

AI is potentially a game-changer for PE firms seeking advantage in an ocean of data. If widely and responsibly deployed. It can help unlock incredible value previously unobtainable.

“While most companies will be affected by AI, PE firms are likely to be facing the greatest opportunities and risks,” according to Per, “and will also have to be the best and fastest at navigating this disruption.” On one hand, genAI creates a bounty of opportunities for private equity (PE) firms and their assets. It holds the potential to make their deal-making knowledge workers more effective, it can create an additional driver of upside potential for every bid, it can make the integration process more effective, and can offer new levers to improve the performance of their assets before exit.

At the same time, Chris points out, “Harnessing the power of genAI requires technical capabilities that many funds and their portfolio companies lack. It also brings a slew of new operational risks to be managed. Ranging from legal exposure to cyber risks. In addition, genAI raise strategic uncertainties for deal-makers – for example, how to value current assets, how long they should be held, and how much to bid for new.”

This creates an interesting market dynamic. Where some firms are aggressively seeking advantage by being early adopters, others are taking a fast-follower stance to avoid being disrupted – and all are trying to influence their portfolio assets to do the same.

How does generative AI add value to PE firms?

Used appropriately, AI can make humans more productive, by freeing up time spent on tasks that AI tools can do better. It can also accelerate human creativity by re-investing this time to discover new sources of value and competitive advantage. As Chris sets out, “AI won’t replace portfolio managers, but one using AI may eventually replace one who isn’t.”

Per sees at least three major opportunities for PE firms to create value with genAI. The first is to apply it within the fund itself to improve speed and quality of deal process. As Per explains, “when you see that work that previously took days, can now be done in seconds with a simple prompt, you realise how magical this technology can be”. According to KPMG's 2023 global tech report, 57 percent of companies believe that AI and machine learning, including generative AI, will be important in helping them achieve their business objectives over the next 3 years.

The second opportunity is to apply genAI post-close, both during integration and across all portfolio companies during the hold period. As Per says, “If most portfolio companies can use genAI to free up say a third of all knowledge worker hours, this could unlock incremental exit value in the order of several billions of (US) dollars for a medium-sized fund.”

The KPMG global tech report, however, reveals that only 24 percent of PE firms are using AI this widely and effectively. “We’re looking at untapped productivity enhancements across the board,” continues Per, “not just by reducing cost, also by driving more volume with the same cost, selling more effectively and making products more attractive”. We expect this to be the focus for many PE firms in 2024.

Finally, AI may affect what type of assets PE firms prioritise for investment in the future and how much they are willing to bid. Per explains, “A previously attractive target could rapidly drop in price if now deemed exposed to disruption by competitors leveraging AI. Similarly, an asset with untapped AI potential could attract a bigger bid premium if the buyer is confident both in the diligence and its ability to capture the incremental up-side.”

Risk factors of genAI for private equity firms

While AI brings many opportunities, there are costs, risks and barriers to overcome before its potential can be realised. A major obstacle that Chris emphasises is the foundational role of data to enable high-value use cases, “Moving your data to the cloud is just the entry ticket. Feeding AI models with high-quality, contextual, indexed, and searchable data is key to unlock full value. This requires a robust data cloud modernisation program not yet in place for many PE Firms.”

Another obstacle that Per calls out is the ‘last mile challenge of AI’. This is often overlooked, as Per says, “Even if AI can help knowledge workers free up a third of their time in studies and pilots, this only translates into real productivity gains if all knowledge workers adopt the tools and re-invest hours saved into something more productive – for example, taking on more volume or higher value-add tasks.”

Per continues, “These are major behavioral changes that cannot be ignored if PE firms are to deliver the productivity gains that AI promises. Success will require a carefully crafted transformation program with a portfolio of actions that hit all behavioral change barriers simultaneously. Solving this, at scale, may be the biggest value creation challenge genAI will face, and not a muscle well trained in prior technology-driven disruptions.”

Lastly, accelerating the use of AI increases risks, like data privacy breaches, more effective cyberattacks and legal challenges arising from intrinsic biases within AI models. This will require a human-in-the-loop for many applications, ‘Trusted AI’ governance frameworks and third-party software. An example from KPMG is the spin-out of Cranium in the US, a software platform that offers technology solutions for organisations to adopt and deploy AI models safely.

How to prioritise AI transformation in private equity firms

Knowing when, where and how much to invest in AI transformation is complex. Per and Chris recommend five key actions that may assist:

Unleash the power of your people
Start with a bottom-up approach, make AI tools available for everyone to find their own ways to cut hours from their work. This could prove very effective in freeing up to 40 percent of people’s time – even without proprietary data and training.
Pilot high-value use-cases
Launch pilots to demonstrate the power of combining AI models with your proprietary data to build tailored high-value applications for both the fund and select portfolio companies. The aim is to generate a ‘flywheel effect’ where humans and machines collaborate and amplify each other’s performance and learning. For example, analyse the tasks most knowledge workers spend time on, then pick a subset of these where AI can have an impact. Then build these applications instead of selecting use cases that aren’t well suited for AI development or offer lower value to fewer people.
Shape your workforce transformation
Most of the near-term value generated by AI will come from augmenting the existing workforce to free up time from a subset of their tasks. To scale broadly will require a carefully designed workforce transformation, with a tailored approach by key roles and focus on behavioral change.

A first crucial step is to carry out a value assessment, based on census data and impact benchmarks by industry, function and role. This aims to size the magnitude of the value at stake, where in the organisation this resides, reinforced by actual data from the first two efforts to inform the business case for the investment.
Accelerate data modernisation
Data is the essential fuel for the highest value AI applications. In a world where speed can make or break a deal, AI is redefining what’s possible, but data access and governance underpin that goal. Not only do you need to have the data but it also needs to be in the right place, making data cloud capabilities vital for all PE firms. In many cases, cloud efforts were started before AI but must now be accelerated and funded to meet shorter timelines and higher expectations.
Launch ‘Trusted AI’ governance
Given the scale of disruption that AI is likely to create, almost every PE firm will need to take action to minimise risks of widespread adoption. This includes adopting a ‘Trusted AI’ governance framework, ensuring compliance with emerging regulations and upgrading cyber-protection.

KPMG firms are excited about AI’s opportunities and equally committed to deploying the technology in a way that is responsible, trustworthy, safe, and free from bias. KPMG Trusted AI (PDF 404 KB), is our strategic approach and framework to designing, building, deploying, and using AI solution in a responsible and ethical manner so we can accelerate value with confidence.

As Per points out, “Simply banning the use of AI to minimise risk is unlikely to be effective and creates risks of falling behind – not having these safeguards in place creates undue risks even in the near-term.”

For those that manage to harness its incremental powers and risks, genAI is potentially a game-changer for private equity firms. To gain an edge, look beyond the hype and start taking a few pragmatic steps now, to help gain advantage in the era of AI-driven disruption.

Authors:

Per Edin, US Board Committee Chair and AI Go-to-Market leader, KPMG in the US
Chris Coulthrust, Senior Solution Architect, Microsoft.

KPMG and Microsoft

KPMG and Microsoft are alliance partners working to enable private equity firms to adapt, transform and innovate. KPMG industry and functional experience, powered by Microsoft technologies, empowers clients to thrive in the era of AI. To find out more about the alliance between KPMG and Microsoft, visit kpmg.com/au/microsoft.

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