Key Takeaways
- • Grant Thornton survey of 950 business leaders found 99 of 100 responding private equity firms are exploring, piloting, scaling, or embedding agentic AI, yet only 9% are very confident they could pass an AI governance audit within 90 days, the lowest of any sector and less than half the cross-industry average of 22%.
- • KPMG found 43% of employees in asset management and private equity resist using AI agents because confidence and quality fall below required limits, ahead of concerns about workload or job security.
- • Allvue's 2026 GP Outlook Survey found only 8% of private markets firms report highly mature data environments, and 70% still rely on manual workflows and spreadsheets as their default operating model.
- • Private Equity International found almost half of LPs monitor how their general partners adopt AI, with LPs increasingly scrutinizing manager data and automated reporting.
- • The article argues auditability is a data problem before it is an AI problem, and that three elements, traceability, consistency, and a defined human checkpoint, separate firms that can pass audits from those that cannot.
Summary
The central test for private market firms adopting agentic AI, according to an opinion piece by Ivan Latanision, chief product officer of Allvue Systems, is not whether they trust the technology but whether they can audit exactly what an agent saw and decided, months after the fact, when a limited partner due diligence questionnaire asks for it. The author argues that the question of trust is too abstract to survive LP scrutiny.
The article cites a Grant Thornton survey of 950 business leaders, in which 99 of 100 responding private equity firms reported exploring, piloting, scaling, or embedding agentic AI in some form. Against that near-universal adoption, only 9% of private equity respondents said they were very confident they could pass an AI governance audit within 90 days, the lowest rate of any sector surveyed and less than half the cross-industry average of 22%. The author frames this as a gap in accountability infrastructure rather than a failure of the technology itself. KPMG, in conversations with asset management and private equity leaders, found that 43% of employees resist using AI agents because confidence and quality fall below acceptable limits.
The article identifies buyer diligence, LP due diligence, and regulatory examination as the three moments when auditability is tested. Common failure modes cited include model drift in revenue projections, unauthorized data access surfaced during due diligence, and workflow errors that cascade as firms scale. The author attributes these to weak governance, specifically the absence of a clear owner for agent output and of processes to contain errors, rather than to the AI itself. Private Equity International reportedly found that almost half of LPs monitor how GPs adopt AI in investment and operational processes, which the author characterizes as LPs asking GPs to show their work.
The article proposes three requirements for auditable agentic AI: traceability, so every output maps to a source; consistency, so identical inputs yield reproducible results; and a defined human checkpoint placed at important moments rather than as blanket oversight. Allvue's 2026 GP Outlook Survey is cited as showing that only 8% of firms have highly mature data environments and 70% still depend on manual workflows and spreadsheets. The author concludes that traceability depends on well-structured underlying data, and that firms leading on governance are those that consolidated their data first. The author predicts that within three to five years the performance divide in private markets will separate firms that can document what their AI did and why from those that can only describe what it probably did.
The article cites a Grant Thornton survey of 950 business leaders, in which 99 of 100 responding private equity firms reported exploring, piloting, scaling, or embedding agentic AI in some form. Against that near-universal adoption, only 9% of private equity respondents said they were very confident they could pass an AI governance audit within 90 days, the lowest rate of any sector surveyed and less than half the cross-industry average of 22%. The author frames this as a gap in accountability infrastructure rather than a failure of the technology itself. KPMG, in conversations with asset management and private equity leaders, found that 43% of employees resist using AI agents because confidence and quality fall below acceptable limits.
The article identifies buyer diligence, LP due diligence, and regulatory examination as the three moments when auditability is tested. Common failure modes cited include model drift in revenue projections, unauthorized data access surfaced during due diligence, and workflow errors that cascade as firms scale. The author attributes these to weak governance, specifically the absence of a clear owner for agent output and of processes to contain errors, rather than to the AI itself. Private Equity International reportedly found that almost half of LPs monitor how GPs adopt AI in investment and operational processes, which the author characterizes as LPs asking GPs to show their work.
The article proposes three requirements for auditable agentic AI: traceability, so every output maps to a source; consistency, so identical inputs yield reproducible results; and a defined human checkpoint placed at important moments rather than as blanket oversight. Allvue's 2026 GP Outlook Survey is cited as showing that only 8% of firms have highly mature data environments and 70% still depend on manual workflows and spreadsheets. The author concludes that traceability depends on well-structured underlying data, and that firms leading on governance are those that consolidated their data first. The author predicts that within three to five years the performance divide in private markets will separate firms that can document what their AI did and why from those that can only describe what it probably did.
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