AI can also optimize workforce and asset utilization. In one case, a PE-backed education services company used AI to integrate data from dozens of systems and forecast needs at each of its sites. This allowed for dynamic staff scheduling that increased labor productivity by over 50% and boosted EBITDA margins by one-third. 

Similarly, manufacturers and asset-intensive businesses deploy AI for predictive maintenance and process control to avoid downtime. By predicting equipment failures and maintenance needs, an industrial portfolio company cut unplanned downtime by 18%, directly reducing operating costs and improving output. These kinds of efficiency gains translate straight to the bottom line, often yielding 5%–25% EBITDA improvements in industries where AI-driven automation is applied.

Predictive analytics is also a game-changer for maintenance and risk management. We mentioned predictive maintenance, by analyzing sensor data and usage patterns, AI can predict equipment issues before they happen, preventing costly outages. Reducing downtime by double-digit percentages not only saves on repair expenses but also increases production capacity and customer satisfaction due to improved reliability.

Portfolio companies can apply similar predictive techniques to anticipate customer behavior (like churn or credit default risk) or to refine supply chain logistics (predicting delays or demand spikes). The end result is a more resilient, data-driven operation that improves efficiency and reduces risk. For many mid-market firms, these improvements quickly add up to tangible financial impact; companies demonstrating AI-driven operational insights can increase EBITDA and lower risk, gaining a competitive advantage in value creation.

AI can also analyze customer data to predict needs and personalize interactions in real time. For example, e-commerce and SaaS portfolio companies use machine learning to tailor product recommendations and marketing messages to each customer’s behavior. This level of personalization not only boosts conversion rates but also increases retention. customers are more likely to stay loyal when they feel understood. In one analysis, AI-enabled sales and marketing teams were able to engage customers more effectively and close 18% more deals with 23% higher contract values on average, compared to those not using AI.

These metrics underscore that better customer experiences are not just a nice-to-have, they deliver measurable ROI in the form of greater revenue per customer and reduced churn. For a private equity owner, that means stronger organic growth within the portfolio and a more robust revenue base contributing to EBITDA improvement.

In the sales process, AI-driven analytics help teams focus on the best opportunities and tailor their pitches. Lead scoring models can predict which prospects are most likely to convert, while AI coaching tools can recommend the next best action to move a deal forward. Dynamic pricing is another powerful lever, AI algorithms adjust prices or offer discounts in real time based on demand, competition, and customer willingness-to-pay. This was the case for one portfolio company, where an AI-driven dynamic pricing model generated an extra $5 million in margin in a single quarter.

Additionally, AI can analyze sales pipelines and customer interactions (emails, calls, demos) to glean insights on what messaging or timing works best, thereby improving win rates. According to industry research, 83% of AI-enabled sales teams achieved revenue growth (versus 66% of non-AI teams), with efficiency gains allowing those teams to engage in more than twice as many customer touchpoints.

By infusing AI into sales and marketing, portfolio companies can accelerate growth and do more with less, a direct boost to revenue and scalability that increases the company’s market value.

By integrating AI into operations and offerings, companies in asset-heavy industries have seen EBITDA gains on the order of 5–25% or more. Even in less asset-intensive businesses, a combination of cost reductions (through automation) and revenue uplifts (through better marketing and CX) can expand EBITDA margins by double digits. 

Crucially for PE firms, AI initiatives can also enhance exit value. Buyers in today’s M&A market are savvy: they perform rigorous due diligence and pay premiums for companies that demonstrate sustainable, data-driven improvements. If you can show that your portfolio company has implemented AI to increase EBITDA and reduce operational risks, it becomes a more compelling story for buyers.

Verifiable efficiency gains and new AI-driven capabilities serve as proof points of a well-run business and support a higher valuation multiple. In some cases, embedding AI has even expanded the strategic vision of a company; for instance, by using AI to reinvent a business model or unlock a new market, a company can increase its total addressable market (TAM) and command a significantly higher exit price.

Top-quartile PE performers already recognize this: they treat technology and AI as value accelerators, using them to compress timelines, expand margins, and enhance exit multiples. Integrating AI into post-acquisition transformation isn’t just operational tinkering, it’s a strategic move that can pay off in both near-term performance and ultimate exit outcome.