Allvue GP Outlook Survey Finds AI Ambition Outpacing Data, Skills and Data Readiness
Allvue Systems, LLC a leading technology provider for the private capital markets, today released findings from its 2026 General Partners (GPs) Outlook Survey, showing that while AI investment intent is accelerating across private markets, data readiness remains a structural constraint on execution. Although AI is now a top technology priority for 2026, less than a quarter of firms rate themselves above the industry average in AI adoption, with the majority citing fragmented data, lack of expertise, limited integration, compliance worries and manual processes as ongoing barriers to impact.
Conducted in partnership with Crisil Coalition Greenwich, the survey draws on interviews with 102 senior investment and operations leaders at private equity, private credit and venture capital firms across North America and Europe. The findings show that only 8% of firms rate their data maturity as high, meaning it is well-organized, integrated and reliable. This contrasts with the acknowledgement from almost two-thirds of respondents that it would be extremely or very valuable to be able to query data across systems and functions. Together, the findings highlight that without strong systems and integrated data foundations, AI investments struggle to translate into operational efficiency and scalable performance.
The findings come as GPs face mounting pressure to operate with greater speed and transparency. In an environment where competition for capital is intensifying and expectations around value creation are rising, 65% of respondents say advanced technology will have the greatest impact on their operations over the next 12 months. More than three in five (62%) firms expect investment in AI to be prominent in their technology decisions and decision-making in the next 12 months.
Respondents noted that AI adoption is hindered by limited internal expertise and persistent concerns around accuracy, reliability, and compliance. These challenges are compounded by constrained technical resources and the cost and complexity of implementation.
The respondents also revealed the top three factors that currently limit or block their organization’s adoption of AI:
- Limited internal expertise among users (64%)
- Accuracy or reliability concerns (59%)
- Compliance concerns (38%)
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.
Impact of Poor Low Data Maturity and AI Adoption
Firms continue to struggle with consistency, visibility, and measurement across their portfolios, limiting both near-term efficiency gains and longer-term value creation.
Almost two-thirds of respondents (65%) report inconsistent reporting from portfolio companies as a core challenge, while half (51%) say they have a limited ability to track value creation in a standardized way across the portfolio.
The survey shows a clear shift in what firms want from their data and technology investments. Respondents identify the need for more robust datasets and advanced analytical capabilities as priorities, including:
- The ability to leverage high-quality benchmark data to understand portfolios
- Flexible and customizable dashboards
- Predictive analytics to support forward-looking decisions
- Valuation tools to improve consistency and confidence in portfolio assessments.
However, there is some good news to highlight. The survey results also indicate that when it comes to investment performance over the past 12 months, firms with high or very high data maturity were twice as likely to say their returns were well above average as GPs with average data maturity. This underscores the role of strong data foundations as firms look to scale AI and operate with greater speed and precision.
“This data makes clear that AI outcomes are being shaped long before models are deployed,” said 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.”
Key Challenges To Data Maturity and AI Adoption Remain
Findings indicate that there is significant work required to connect data, ensure consistency, and enable it to flow across systems, setting the stage for the key challenges firms face in achieving advanced data maturity.
Reliance on Excel remains a structural constraint across the industry. The majority of respondents (56%) report ongoing dependence on spreadsheets despite investment in purpose-built systems, even among firms with above average AI adoption. This reliance continues to absorb financial operations capacity, with 70% of firms reporting a challenging workload driven by manual processes and Excel-based workflows.
The survey also found that organizations face challenges integrating data across internal systems, along with staffing constraints and the operational complexity of managing increasingly complex fund structures.
In aggregate, the findings underscore that without reducing Excel dependence and improving system integration, firms will continue to struggle to translate AI investment into operational impact.
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.
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