Over the past 10 years, private equity firms have tightened their focus on technology investments, including enterprise resource planning tools, data analysis tools and cloud migration to improve bottom-line growth. With the popularity and business application of generative AI (GenAI) and machine learning, technology conversations have shifted towards artificial intelligence’s (AI) relevance in private equity—and there are plenty of use cases for it given the potential impact on revenue and EBITDA, especially throughout the merger and acquisition lifecycle.

But this heavy focus on artificial intelligence and machine learning (AI/ML) capabilities and data related investments creates a significant challenge: identifying and executing on the right use cases at the appropriate stage of the merger and acquisition (M&A) lifecycle, including diligence and value creation.

Many of Highspring’s private equity clients are developing plans for identifying data and AI/ML use cases across the investment cycle. However, unlike the AI/ML bubble before the pandemic, these firms are now focused on leveraging these capabilities for pre-close deal sourcing and revenue-generating activities during the hold period. This shift has led to a rapid increase in private equity customers’ AI/ML and data readiness needs.

Additionally, future portfolio company buyers are now focusing on a company’s ability to leverage proprietary data and AI/ML capabilities through new products and automated insights to support exit multiples. As a result, private equity firms are executing cross-functional, multi-phase data and AI/ML readiness processes—from pre-deal deal sourcing to exit—across various data capabilities including process automation, data and analytics, AI/ML, and GenAI.