You can’t just put AI on top of Excel: Why private markets need better data
AI adoption is accelerating across private markets, but moving from experimentation to reliable, institutional use requires more than adding a new tool to an existing workflow.
At SuperReturn CFO/COO, Jessie Juan, Co-Founder of Quantium, discussed why the industry’s particular sensitivity to accuracy makes strong data foundations, auditability and human oversight essential, and why the next phase of AI adoption will be less about individual tools and more about shared infrastructure.
Key takeaways
- AI adoption across private markets is growing, but firms remain at very different stages of implementation.
- The complexity of fund structures, IRR calculations and allocation policies makes accuracy particularly important.
- AI can already reduce manual work across data ingestion, investor questionnaires and reporting workflows.
- Moving from individual AI tools to organisation-wide collaboration requires a common data foundation.
- Human approval, audit trails and transparency remain essential when AI is used for financial reporting.
- Embedded AI may offer more predictable outputs for continuous reporting than general-purpose tools used in isolation.
Private markets are moving beyond AI experimentation
AI has been a major talking point across private markets for several years, but Juan sees a noticeable change in how firms are approaching it.
At SuperReturn a year earlier, she recalls considerable scepticism. Today, adoption is much more visible, although it remains fragmented.
Some smaller firms are using AI across a wide range of functions. Larger organisations, particularly those sitting on substantial datasets, face a different question: is the underlying information clean and structured enough for AI to use reliably?
That distinction matters because private markets have little tolerance for inaccurate outputs.
A small error can have a big impact
Juan brings an LP perspective to the issue.
A minor numerical difference may appear insignificant in many industries. In private markets, a small change can materially affect an IRR or the information being reported to investors.
Fund structures, allocation policies and the mechanics behind performance calculations add further complexity. That makes simply adding AI to existing spreadsheets a risky proposition.
You can’t simply layer AI on top of Excel spreadsheets and hope that it will produce accurate numbers.
Jessie Juan, Co-Founder of Quantium
The opportunity to automate exists, but Juan argues that firms need to get the foundation right before using AI widely across sensitive reporting processes.
The foundation comes first
AI is becoming more embedded in fund operations, but the firms that benefit most may not be those that move fastest.
The greater advantage could come from doing the groundwork first: improving data quality, creating consistent information flows and deciding where human responsibility sits. Before private markets can automate more, firms need to be able to trust the data underneath.