Weak data infrastructure slows AI adoption

By Vriti Gothi

AI ambitions across private markets are accelerating, but weak data infrastructure and operational constraints are limiting adoption and measurable impact, according to a new survey by Allvue Systems conducted in partnership with Coalition Greenwich, part of CRISIL.

The 2026 General Partners Outlook Survey, based on interviews with 102 senior investment and operations leaders at private equity, private credit, and venture capital firms across North America and Europe, found that while artificial intelligence has emerged as a top technology priority for the coming year, most firms lack the data maturity required to deploy it at scale. Less than a quarter of respondents consider themselves above the industry average in AI adoption, and only 8% rate their data environment as highly mature, meaning it is well-organised, integrated, and reliable.

The findings come at a time when general partners are under increasing pressure to operate with greater speed, transparency, and consistency as competition for investor capital intensifies. Sixty-five percent of respondents expect advanced technology to have the greatest impact on their operations over the next 12 months, while 62% anticipate AI will play a prominent role in technology investment decisions during that period.

Despite this momentum, structural barriers continue to slow implementation. Many firms reported fragmented data across systems, manual processes and limited integration, making it difficult to generate cross-functional insights. Nearly two-thirds said the ability to query data across systems and functions would be highly valuable, underscoring the gap between strategic intent and operational capability.

“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.”

Organisational and risk concerns are also holding back adoption. The survey found that limited internal expertise remains the biggest constraint, cited by 64% of respondents, followed by concerns around accuracy and reliability (59%) and compliance risks (38%). These challenges are compounded by constrained technical resources and the cost and complexity of implementation.

Operational inefficiencies remain widespread. Around 65% of firms reported inconsistent reporting from portfolio companies, while 51% said they lack a standardised way to track value creation across their portfolios. Reliance on spreadsheets continues to be a structural issue, with 56% of respondents still dependent on Excel despite investments in specialised systems. As a result, 70% said manual processes and spreadsheet-based workflows are contributing to heavy operational workloads.

The survey also highlights a growing demand for stronger data capabilities, including high-quality benchmark datasets, flexible dashboards, predictive analytics and improved valuation tools to support more consistent and forward-looking decision-making.

Performance data suggests that investment in data foundations is beginning to translate into competitive advantage. Firms with high or very high data maturity were twice as likely to report returns well above average over the past year compared with those operating at average maturity levels.

“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. These foundations enable speed, consistency and better investment decisions at scale. Without them, AI remains an experiment rather than a performance driver.”

According to Kevin McPartland, Head of Market Structure and Technology Research at Coalition Greenwich, the industry’s current trajectory may not be sustainable. With most firms rating their data maturity as average or low, many are attempting to deploy AI into environments that are not yet equipped to support scale, consistency or reliable returns on investment.

For the FinTech sector, the findings reinforce a broader shift in private markets from experimentation with standalone AI tools toward investment in foundational capabilities such as data integration, governance, workflow automation, and analytics infrastructure. As firms seek to translate AI ambition into operational efficiency and performance gains, technology providers focused on data architecture and embedded intelligence are likely to play an increasingly central role.