PE Firms Wary of Data Deals Amid AI Fears

The specter of AI disruption has sent shockwaves through the valuations of publicly traded data companies, creating a potential opening for private equity. Financial data providers like FactSet, Morningstar, and Gartner have seen significant drops in their share prices, with FactSet's stock falling 39% in the six months leading up to March 2026. This decline has drawn the interest of major private equity firms such as Thoma Bravo and Hellman & Friedman, who are now evaluating these companies as potential acquisition targets. The core concern for investors is that generative AI could replicate and devalue the very information and analytical tools that these data companies sell. This has led to a compression of valuation multiples; FactSet's enterprise-value-to-EBITDA ratio, for instance, has fallen from around 30 in 2022 to approximately 12. Similar declines have been seen at Morningstar and Gartner, reflecting a broad market reassessment of the long-term defensibility of their business models in an AI-saturated world. Ironically, private equity firms are themselves increasingly leveraging AI to enhance their own operations. AI-powered tools are being used to accelerate due diligence, moving from weeks to days by automating the analysis of vast amounts of data, including financial statements and legal documents. This allows deal teams to focus more on strategic analysis rather than manual data processing. While AI presents a clear threat to traditional data businesses, it also offers a significant value-creation opportunity for private equity. Firms are now hiring dedicated AI specialists to deploy this technology within their portfolio companies to streamline operations and boost efficiency. The current market dislocation allows PE firms to potentially acquire these data companies at a discount and then apply their own AI expertise to modernize the business and drive future growth. Despite the opportunities, significant uncertainty remains a key factor in underwriting these deals. The rapid evolution of AI makes it difficult to predict the long-term impact on business models, creating a complex risk-reward calculation for potential buyers. The successful integration of AI is not guaranteed, with many initiatives in PE-backed companies failing to move beyond the pilot stage. Challenges include fragmented data systems post-merger and a lack of clear ownership for AI projects. This highlights the operational hurdles that must be overcome to realize the transformative potential of AI within acquired data companies.