How modern data engines are changing the rules of private equity Private equity and corporate finance have spent the last decade layering on CRMs, datasets and dashboards, yet many teams still struggle to see a single, joined-up view of their market. In our recent data engines webinar, VP of Growth Alex Bajdechi noted that firms are now juggling “Expert network data, company data, fundraising data, proprietary data sitting across Excel data rooms” without a coherent way to connect it all. 

From experiments to engines: predictions for AI in private equity in 2026 Private equity is entering 2026 with AI shifting from experimentation to expectation. The question is no longer whether to use AI, but how to make it a durable advantage across sourcing, underwriting, and value creation.

The case against pointing an LLM at a data lake for private equity Across private equity, there’s a growing temptation to believe that modern AI can be reduced to a simple formula: load documents into a data lake like Snowflake, connect an LLM, and let the magic happen. But while that may be an appealing idea, it breaks down almost immediately in real-world PE environments - where data is complex, confidential, distributed, and deeply interwoven with firm-specific workflows.