AI fitness and value creation in PE-backed companies

The growth instinct remains strong among the leaders of private equity portfolio companies. PwC’s 29th Global CEO Survey, shows that 46% are very or extremely confident about revenue growth over the next 12 months, compared with 30% of CEOs globally. Across the survey, they are also more acquisitive, more willing to enter new sectors, and more focused on the near term than their peers.

The catch is that it is becoming harder to realise value from such efforts. Exits have been delayed, and sponsors have had to hold assets for longer than planned. Portfolio company CEOs have been left to do the work of sustaining performance and creating value deeper into the hold: finding efficiencies, defending margins, and producing a credible growth story for an exit that keeps moving further out.

In a longer hold, technologies such as data and analytics platforms, automation, cloud-based applications, and, increasingly, AI become more immediate tools of value creation. They help management teams plan faster, see performance more clearly, and run the business with greater precision. The challenge for many portfolio companies is to become faster, fitter operators, and, in particular, to create the right foundations to support their AI fitness.

But many PE-backed companies have substantial work to do. Four in ten CEOs say technology-related functions are performing below expectations, and almost as many say the same of demand generation, according to PwC’s Global CEO Survey. Taken together, the findings indicate strain in both the operating engine and the commercial engine of value creation. And when those engines are underpowered, management can work harder without necessarily getting further.

AI has immense promise to turn the power of technology into gains in efficiency, operations, and value. And while there’s a great deal of talk surrounding transformation and revolution, the results thus far are more modest. In PwC’s Global CEO Survey, only 14% of PE-backed CEOs say AI has contributed to both higher revenues and lower costs. More than half report no upside at all.

The small minority who are getting results share a trait that is more prosaic than visionary: strong foundations. The companies that report stronger technology operations are more likely to report revenue gains or cost reductions from AI. These businesses tend to have a set of core AI capabilities in place, which include cleaner data engineering, better analytics, stronger applications, and more reliable IT.

AI applications that matter in PE-backed companies are often the ones that improve the operations of the business during the hold period and could, in principle, be replicated elsewhere in the portfolio. At one PE-backed electric vehicle manufacturer, AI-enabled tools were introduced in finance before spreading into procurement and operations, and cut the time required for forecasting and reporting from weeks to less than a minute.

Elsewhere, gains include significant financial upside. At a PE-backed automotive battery manufacturer, AI is improving inventory allocation across customers, cutting penalty exposure, and delivering roughly US$15 million in annual savings.

How to improve AI fitness 

For years, private equity firms have relied on portfolio strategies. They take practices—for example, on energy use, finance, inventory management, or operational discipline—that work in one portfolio company and apply them across the broader portfolio. The same imperative now applies to AI. Owners increasingly want to identify tools and use cases that work in one business and roll them out across several.

PwC’s AI performance study found that the most AI-fit companies are achieving an AI-driven performance boost, combining AI-led revenue gains and cost reductions, that’s 7.2 times that earned by less-fit companies.

But a portfolio strategy is only achievable if the portfolio companies themselves are AI fit. In PwC’s terms, AI fitness means having the right discipline across nine key factors: strategy, investment, workforce, data and technology, governance and risk, innovation, breadth and depth of AI use, sophistication of use, and the ability to capture value from industry convergence. 

The lesson from PwC’s AI performance study is that use cases and foundations move together. A promising pilot in planning, inventory, or customer service will only travel if the underlying business has the data, governance, technology, and workforce capabilities to support it. That is why the companies seeing stronger AI outcomes are experimenting more and building the conditions that make those experiments repeatable. 

For PE-backed CEOs, that points to a practical sequence. Start testing now, in high-value areas such as forecasting, procurement, inventory, maintenance, customer service, or demand generation. Measure the results properly. Identify the use cases that work, stop the ones that do not, and then invest in the data, governance, technology, and workforce capabilities needed to scale the winners across functions and business units. 

AI fitness, like physical fitness, is built through disciplined repetition. What most portfolio companies need is to become faster and fitter operators. 

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