We no longer think about AI as something to pilot or selectively deploy. We treat it as a core capability, one our team is expected to understand, use, and improve with as part of everyday work, much like financial judgment or operational experience.
Our team uses AI daily across analysis, preparation, and internal workflows. In addition, we have operated AI-enabled workflows inside portfolio companies, not as pilots or proofs of concept, but as production infrastructure handling real data, real volume, and real operational edge cases.
That experience shapes how we advise clients.
When we work with CFOs and management teams on AI adoption, we are not extrapolating from vendor demonstrations or industry commentary. We have already navigated the gap between theoretical capability and practical execution, where invoice formats vary, covenant definitions differ by agreement, and data lives across multiple systems.
What this looks like in practice
We have embedded AI-driven capabilities across several core operational areas:
Back-office automation.
AI-assisted invoice intake, exception handling, and fraud flagging designed to accommodate variability that often breaks traditional automation. Finance teams are able to process higher volumes without adding headcount.
Cash and covenant visibility.
Workflows that pull actuals, maintain forecast logic, and surface variance explanations automatically. This allows finance leaders to focus on decisions rather than rebuilding spreadsheets.
Document intelligence.
Multi-stage processing of compliance, operational, and financial documents across asset portfolios. In one engagement, this reduced approximately $250,000 of annual manual effort.
These are not products we sell. They are examples of what becomes possible when advisors have already crossed the implementation threshold internally.
Why this matters for portfolio companies
Many AI initiatives fail during execution because the advisors involved have not operated within the underlying constraints. Tools are recommended without accounting for integration friction, data quality issues, or the exceptions that still require human judgment.
Our advice is informed by having worked inside those constraints ourselves.
As a result, implementations are designed to embed into existing workflows rather than requiring new habits to succeed. Systems are built to scale without proportional increases in cost or headcount, and guidance is grounded in production experience rather than theory.
Conclusion
We are not the most vocal participants in discussions about AI in private equity. However, when portfolio companies ask whether an approach will work in practice, we are able to answer based on experience rather than optimism.
If this approach is relevant to your portfolio, we are happy to share what we are seeing in practice.