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.
Accelerating value creation 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.
I was speaking to a legal services company recently who have been able to productise a core service, transforming their 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 (earnings before interest, taxes, depreciation, and amortisation).
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 generating a rapid return on investment and building cultural momentum to becoming a more data-driven organisation.
Democratisation of investment opportunities
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 the playing field, but also creating a situation of haves versus have nots.
According to Per Edin, board committee chair and AI go-to-market leader at KPMG 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 data foundations in place
So what is the best course of action for PE firms seeking to use AI to enhance their investment strategy?
