AI is moving beyond experimentation in private credit. The focus is shifting towards practical applications that can support day-to-day operations, improve how data is handled, and help teams make better use of their time.
In our recent webinar, AI and private credit: moving from headlines to operations, Helen Wang, our Chief AI and Data Science Officer, sat down with Jerry Smay, Director, Credit Middle Office – US, and Donald Shannon, Private Credit Sales Head, Americas, at Apex Group, alongside Coran Darling, Associate Lawyer at DLA Piper. They discussed where AI is already delivering value, where expectations may be ahead of reality, and where human judgement remains essential.
AI is moving into everyday workflows
“The most valuable AI in private credit right now is also the least glamorous.”
- Helen Wang, Chief AI and Data Science Officer
Private credit generates large volumes of information across credit agreements, borrower financials, compliance certificates, amendments, emails, and spreadsheets. Historically, reviewing and extracting this information could take analysts hours per document.
AI can now extract and structure relevant information in minutes. A human can then review the output rather than manually enter the data. This can reduce the time needed to onboard deals, with some processes moving from weeks to days or even hours.
The same principle applies to reporting and reconciliation. AI can help generate investor reports, reconcile information across systems, identify anomalies, monitor notices, and support the resolution of exceptions.
The distinction lies in whether these applications can operate effectively within complex, live workflows. This is where practical value becomes more important than technical promise.
Practical applications matter more than the hype
Fully autonomous systems that source, underwrite, and close a deal from end to end are not yet widely used in production. Data, governance, regulation, and confidence in the technology all play a role.
More targeted applications are already showing value. These include deal screening, document review, loan documentation management, compliance triage, and identifying changes in mandatory disclosures. They can reduce manual work, support more consistent processes, and allow people to focus on tasks that require their judgement.
Good AI starts with good data
One of the biggest operational challenges in private credit is fragmented data. Information can sit across documents, systems, and third parties, while reporting requirements and the language used in credit agreements can vary between deals.
AI can help bring this information together, but it does not remove the need for a reliable data foundation. The panel highlighted the importance of having a clean, permissioned source of truth, with data that is reconciled, structured, and contextualised.
Without this foundation, adding an AI tool may simply place a new interface over the same underlying problems.
Human judgement remains essential
As AI moves closer to credit decisions, investor communications, regulatory filings, and payments, the consequences of an error become greater.
For lower-risk or reversible tasks, such as data extraction, first-draft reporting, and reconciliation matching, AI can perform the initial work while people review exceptions. For higher-risk actions, a named individual should approve the output before anything happens.
AI can deliver scale, speed, and consistency, but people must remain responsible for context, judgement, and accountability.
