Private Markets' AI Dream Faces a Harsh Data Reality

NEW YORK, NY – February 10, 2026 – The private capital industry is aggressively chasing an AI-powered future, but a new report reveals a stark and widening gap between ambition and reality. While general partners (GPs) have identified artificial intelligence as a top priority, the vast majority of firms are being held back by fragmented data, a critical shortage of internal expertise, and a persistent reliance on manual processes, creating a foundational crisis that threatens to derail the industry's technological leap forward.

Findings from the Allvue Systems 2026 GP Outlook Survey, conducted with Crisil Coalition Greenwich, paint a picture of an industry eager for transformation but ill-equipped to execute it. The survey, which polled 102 senior leaders across private equity, private credit, and venture capital, found that while 62% of firms expect AI to be a prominent factor in their technology decisions this year, less than a quarter rate their own AI adoption as above average. This disconnect highlights a growing 'readiness gap' that is quickly becoming a key competitive differentiator.

The AI Paradox: Ambition Outpaces Reality

The core of the problem is not a lack of intent, but a deficit in preparation. The survey reveals that the top three barriers blocking AI adoption are limited internal expertise (cited by 64% of respondents), concerns over the accuracy and reliability of AI outputs (59%), and significant compliance worries (38%). These challenges underscore that implementing AI is far more complex than simply acquiring new software; it requires a deep-seated organizational and data-centric overhaul that most firms have yet to undertake.

"Our 2026 GP survey shows that firms want to do far more with their data and use AI to streamline workflows, but many are being held back by limited data maturity,” said Ivan Latanision, Chief Product Officer at Allvue. “That gap is now a competitive issue. To close it, GPs and LPs must invest strategically in data platforms and integrations that embed AI-driven intelligence into day-to-day workflows and convert data investment into measurable operating and performance gains.”

This sentiment is echoed by the data, which shows that while nearly two-thirds of firms believe it would be extremely valuable to query data across all their systems, a mere 8% rate their data maturity as high—meaning it is well-organized, integrated, and reliable. This chasm between desire and capability lies at the heart of the private markets' AI paradox.

Unpacking the Foundational Cracks

Beneath the surface of AI ambition, the industry's operational foundations show significant signs of strain. The survey highlights a continued and problematic dependence on spreadsheets, with 56% of firms—even those with above-average AI adoption—reporting an ongoing reliance on Excel. This perpetuates manual, error-prone workflows that absorb significant resources, with 70% of firms stating they have a challenging workload driven by these outdated processes.

This operational drag is compounded by poor data consistency. Nearly two-thirds (65%) of respondents report inconsistent reporting from their own portfolio companies as a core challenge, while half (51%) admit to a limited ability to track value creation in a standardized way. Without a 'golden source of truth' for their data, firms are attempting to build advanced AI models on a foundation of sand.

“Insights from GPs show that AI ambition in the private markets industry is widespread, but data readiness has not kept pace,” said Kevin McPartland, Head of Market Structure and Technology Research at Crisil Coalition Greenwich. “That imbalance is becoming unsustainable. With six in ten firms rating their data maturity as only average and almost a third rating it low, many GPs and LPs are deploying AI into environments that are not yet built to support scale, consistency, or reliable returns on investment.”

Data Maturity: The True Driver of Performance

Perhaps the most compelling finding from the survey is the direct, quantifiable link between data maturity and investment returns. Firms with high or very high data maturity were twice as likely to report that their returns were well above average over the past 12 months compared to their peers with only average data maturity. This statistic reframes data management from a back-office IT issue into a frontline driver of alpha.

“This data makes clear that AI outcomes are being shaped long before models are deployed,” explained Dmitri Sedov, Chief Data and Analytics Officer at Allvue Systems. “Firms with strong data maturity are better positioned to apply analytics with confidence, and deliver more useful insights to internal and external audiences. These foundations enable speed, consistency, and better investment decisions at scale. Without them, AI remains an experiment rather than a performance driver.”

In response, technology providers are racing to offer solutions. Platforms like Allvue's own Nexius and AI agent 'Andi' are designed specifically to address these foundational flaws by unifying disparate data sources, automating document extraction, and creating the structured data environment necessary for AI to thrive. The market is increasingly shifting towards integrated platforms that can serve as a central nervous system for a firm's data, moving away from a patchwork of disconnected point solutions.

Navigating a Shifting Regulatory Landscape

Adding another layer of complexity to AI adoption are the watchful eyes of global regulators. The survey's finding that 38% of firms harbor compliance concerns is well-founded. In 2026, financial authorities including the U.S. Securities and Exchange Commission (SEC), the UK's Financial Conduct Authority (FCA), and the European Securities and Markets Authority (ESMA) are intensifying their scrutiny of AI in financial services.

Regulators are largely applying existing, principles-based rules to the new technology, demanding governance, transparency, and accountability. The concept of 'explainable AI' is paramount, as firms are being required to demonstrate how their models make decisions and to prove they do not result in biased or unfair outcomes for investors. The FCA, for instance, has emphasized that firms must have "rock solid data foundations" to use AI responsibly and has placed accountability squarely on senior managers to oversee its use.

This regulatory pressure further raises the stakes for data maturity. Firms cannot ensure compliance or defend their models' outputs if they cannot trust the underlying data. As GPs move forward, they must navigate a dual challenge: building the internal capability to leverage AI effectively while simultaneously constructing a robust governance framework that can withstand regulatory examination.

The journey toward an AI-native private capital market is proving to be less of a sprint and more of a meticulous, foundational marathon. The firms that prioritize building robust data infrastructure, cultivating internal talent, and establishing strong governance will not only unlock the transformative power of AI but will also likely define the next era of leadership in the industry.