Private Markets Stay Bullish on Retail but Cautious on AI
Private markets managers are leaning into retail distribution and advanced analytics even as they wrestle with tougher value-creation conditions and uneven AI adoption. That’s the picture from Allvue Systems’ 2026 GP Outlook Survey, conducted with Crisil Coalition Greenwich, which polled 102 senior leaders across private equity, private credit, and venture firms in North America, the U.K., and Benelux.
Performance remains a bright spot: 73% of respondents said their firms are performing above or well above the industry average, citing multiple successful exits and exposure to a small number of dominant AI-driven winners. At the same time, managers acknowledged the need to stay adaptable as markets shift, while pushing deeper into retail via new partnerships and products.
On technology, larger firms with more than $2 billion in assets are leading the way in AI experimentation and are demanding richer data and analytics from vendors, including benchmark data for portfolio analysis, customizable dashboards, predictive analytics, and valuation tools.
Yet most managers see their AI efforts as modest. “Our findings indicate that progress is constrained by both technical foundations and people-related challenges,” Allvue wrote. Seventy‑eight percent of respondents described their AI investment as average at best, and Allvue said most organizations have not reached the level of AI readiness needed to make 2026 a breakout year.
Data frictions are a key drag as 65% of respondents reported inconsistent reporting from portfolio companies, and 51% said they cannot track value creation in a standardized way. AI priorities are clustering around fundraising and technology development, with current use cases concentrated in lower‑risk areas such as research, deal sourcing, and document review.
Fewer firms reported success deploying AI in higher‑risk functions such as back‑office operations and investor reporting, where only 28% cited progress. Allvue attributed the gap not only to concerns about accuracy in deterministic tasks like reporting and investor communications, but also to broader issues around organizational readiness, skills, and trust in AI systems.
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