There’s no doubt about it: artificial intelligence is reshaping the landscape of private equity.
Once a tool primarily used for data crunching and portfolio analysis, AI is now becoming a strategic asset, influencing everything from deal sourcing to portfolio management.
The potential of AI in PE is undeniable. The huge growth in assets managed by AI-driven platforms is fuelled by a rising number of PE firms exploring the innovative applications of AI in their operations, such as their transactions and trading strategies.
Deloitte even predicts that 25% of PE firms will be using AI to augment their portfolio valuations.
One particularly promising area is the use of generative AI. With its ability to analyse vast amounts of unstructured data, identify hidden patterns, and extract actionable insights, generative AI can significantly enhance investment decision-making.
Tasks that were once time-consuming and labour-intensive can now be significantly sped up, allowing PE firms to focus on higher-value activities.
A redefined culture of investment
But this transformation is not merely about adopting new technologies, it’s also about reshaping the culture of investment itself and re-defining the art of the possible.
Historically, investors have wanted visibility into core financial trends. Now they have much higher expectations for transactions on a data front row, and are much more interested in understanding the ‘how and why’ certain financial and operational trends are occurring.
This is redefining the investor’s decision-making process. PE firms have typically relied on experience, intuition and their own network intelligence to find and create value, usually supplemented by a basic level of analysis. But rising asset prices and competitive markets can make it risky to rely solely on these methods.
With the speed of change that the latest developments in AI are causing in markets, there is a far greater likelihood that a good investment could turn bad during a hold period. Investment committees are acutely aware of this and are demanding an even greater level of granularity before backing investment theses that might previously have been considered safe bets.
While traditional skills remain valuable, interpreting data quickly and at scale is helping investment managers de-risk investments and optimise value creation and returns.
Importance of equity storytelling (backed up by data)
This shifting buyer behaviour is creating a growing expectation for management teams to provide broader and deeper datasets at exit. It’s now critical for management teams to place greater emphasis on data and analytics to support their ‘equity story’, to give investors comfort on past performance and future returns.
Again, investors are even more concerned with evidencing the ‘how & why’ when it comes to performance and trends; just saying that we have grown profitably by X% year on year is now not enough – it needs to be evidenced by granular data and solid analytics.
This also allows management to showcase opportunities for further future growth, with investors being able to leverage these data ‘assets’ to underpin their investment cases. Management teams are expected to be using these assets to run the operation and the platforms must be able to scale with future growth – especially if M&A is a strategy that an investor would like to pursue.
With higher investor expectations, those who fail to do so risk undermining their valuation potential and will likely have a more painful transition in the first year or two of the hold.
Maximising value during the hold period
There are both seismic and incremental ways in which AI can create value during a hold.
In much the same way as we saw with trends like decarbonisation, perhaps the most exciting impact of AI is the opportunity to either change the narrative of a business or the business model during a typical PE hold period, which can potentially drive higher multiples and a greater valuation.
A services business, integrating an AI product effectively, can enable the productisation of a core service, transforming revenue and margin potential and moving them from being a services company to a services and product company, almost overnight, shifting the multiple they are likely to achieve on exit.
At the same time, using AI to make incremental improvements to traditional value creation levers, enables rapid revenue increases and enhances EBITDA. Value creation is fundamental given the current market, and data and AI is no longer a nice-to-have here. Understanding the core value creation plan and augmenting the key initiatives with AI can add tremendous value at pace.
Simple things like using AI to better understand and target customers, be effective in your pricing methods, accelerate product development and drive operational efficiencies are use cases that can be executed without causing too much disruption. For businesses starting on their data and AI journey, this route can be the most effective to generate a rapid return on investment and build cultural momentum to become a more data-driven organisation.
Broadening access to investment
Another significant cultural shift brought about by AI is the democratisation of investment opportunities.
Traditionally, sophisticated investment tools and insights were adopted primarily by large cap technology businesses with the budget to develop or own the technology to train AI models. The latest generation of AI changes this. For the first time, it means these capabilities are trickling down to mid-sized and smaller businesses, levelling up the playing field, but also creating a situation of ‘haves vs have nots’.
According to Per Edin, board committee chair and AI go-to-market leader at KPMG in the US: “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 dollars for a medium-sized fund.”
Getting the basics right
So what is the best course of action for PE firms seeking to use AI to enhance their investment strategy? Firstly, get your foundations right.
Do you understand the readiness of each of your assets and potential assets? Do you have a solid data foundation to take full advantage of AI’s rapid progression? Are your investments sufficiently educated to do the same?
Far too many businesses invest in experimenting with, or applying, new innovations when really their resources would be much better applied ensuring the infrastructure they use is up to scratch. Even to this day, the data exchange process between PE firms and their portfolio companies – extraction, analysis and reporting – can take months. This is far too slow and can seriously undermine decision-making and value creation.
But establishing a well-defined data strategy isn’t just about numbers. There is a significant opportunity for firms that harness data to craft a compelling equity narrative for portfolio companies that resonates with stakeholders, investors, and potential buyers.
Whether considering an acquisition, implementing a value creation strategy, monitoring performance, or planning an exit, more than ever leveraging data-driven insights will enhance the clarity and attractiveness of the investment proposition.
As investors get more familiar with what is required to drive value from AI, investment theses underpinned by a solid data strategy will become a necessity as opposed to a nice-to-have.
It also necessitates significant cultural and organisational changes — such as upskilling and retraining staff, restructuring teams, and evolving business models.
Looking ahead
While the AI landscape is ripe with potential, the most significant opportunities for investors may not lie in acquiring AI startups, there will be far more losers than winners here. Instead, the real payoff lies in partnering with existing businesses and guiding them through an AI-driven transformation, because they already have the expertise and market to make AI successful. This presents a unique advantage for PE funds, who can leverage their agility to rapidly identify and capitalise on emerging trends.
However, success hinges on a strong data foundation and a proactive assessment of both opportunities and risks. By understanding how AI can reshape industries and anticipating potential disruptions, PE funds can position themselves for substantial returns in the years to come.