Key Takeaways
- • Data fragmentation is the foundational problem: Private equity firms across all sizes store critical information—deal memos, due diligence files, board decks, and LP correspondence—across disconnected systems, creating operational inefficiency and compounding costs that firms are attempting to manage with PDFs and Excel documents.
- • Deal teams lose significant productivity to information retrieval: According to Rishi Chohan, U.S. CEO of GFT, deal teams spend approximately half a day searching for information they know exists somewhere in their systems, representing wasted time that accelerates costs given PE's operational velocity.
- • Data infrastructure must precede AI implementation: Chohan's thesis centers on the industry "skipping a step"—firms are pursuing AI solutions without first addressing underlying data architecture problems, indicating a maturity gap between AI ambitions and operational readiness.
- • Multiple market pressures are intensifying the problem: PE firms face converging headwinds including extended company holding periods that compress return windows, LP demands for greater transparency and performance, and legacy technology stacks inadequate for current operational demands.
- • GFT brings three decades of PE technology experience: GFT, Chohan's employer, operates as a global technology company with 30+ years serving investment firms and is currently engaged with some of the largest PE firms to resolve data integration challenges at source.
Summary
Private equity's rush toward artificial intelligence adoption masks a more fundamental operational problem: data infrastructure fragmentation. According to Rishi Chohan, U.S. CEO of GFT, the industry's most pressing challenge isn't AI capability but rather the foundational data architecture that must precede any meaningful AI implementation.
Critical information across PE firms—including deal memos, due diligence files, board decks, and limited partner correspondence—remains scattered across disconnected systems never designed for interoperability. This fragmentation imposes measurable costs. Deal teams spend approximately half a day locating information they already know exists within their organization. At the operational pace characteristic of private equity, this repeated inefficiency compounds significantly. Many firms continue managing complex portfolios and transactions using PDFs and spreadsheets, a technological posture inconsistent with their stated AI ambitions.
The data problem has become increasingly acute as market conditions tighten around PE operations. Companies remain private longer, compressing the window available for returns. Limited partners simultaneously demand greater transparency and stronger performance metrics. Regulatory and competitive pressure intensifies from multiple directions. Within this constrained environment, legacy data systems and manual information retrieval create drag that larger and middle-market firms can no longer absorb.
Chohan's perspective, grounded in GFT's 30+ years serving investment firms and current work with some of the largest PE operators, suggests the industry is pursuing AI solutions prematurely. Before firms can extract value from artificial intelligence applications, they must first establish coherent, integrated data foundations. The sequence matters: data infrastructure investment must precede AI implementation, not follow it. Without this prerequisite work, AI deployment risks amplifying existing inefficiencies rather than resolving them. For institutional investors and limited partners evaluating PE firm capabilities, the maturity of underlying data and technology infrastructure may prove more predictive of operational success than publicized AI initiatives.
Critical information across PE firms—including deal memos, due diligence files, board decks, and limited partner correspondence—remains scattered across disconnected systems never designed for interoperability. This fragmentation imposes measurable costs. Deal teams spend approximately half a day locating information they already know exists within their organization. At the operational pace characteristic of private equity, this repeated inefficiency compounds significantly. Many firms continue managing complex portfolios and transactions using PDFs and spreadsheets, a technological posture inconsistent with their stated AI ambitions.
The data problem has become increasingly acute as market conditions tighten around PE operations. Companies remain private longer, compressing the window available for returns. Limited partners simultaneously demand greater transparency and stronger performance metrics. Regulatory and competitive pressure intensifies from multiple directions. Within this constrained environment, legacy data systems and manual information retrieval create drag that larger and middle-market firms can no longer absorb.
Chohan's perspective, grounded in GFT's 30+ years serving investment firms and current work with some of the largest PE operators, suggests the industry is pursuing AI solutions prematurely. Before firms can extract value from artificial intelligence applications, they must first establish coherent, integrated data foundations. The sequence matters: data infrastructure investment must precede AI implementation, not follow it. Without this prerequisite work, AI deployment risks amplifying existing inefficiencies rather than resolving them. For institutional investors and limited partners evaluating PE firm capabilities, the maturity of underlying data and technology infrastructure may prove more predictive of operational success than publicized AI initiatives.
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