Problem

We faced the challenge of compiling a coherent, forward‑looking analysis on agentic AI within Private Equity for the US market in 2026. Stakeholders needed clear signals on how AI agents can optimize deal sourcing, due diligence, and portfolio oversight while navigating regulatory and ethical considerations.

The landscape is fragmented, with disparate data sources and inconsistent methodologies across firms. Without a unified framework, insights risk being anecdotal rather than evidence‑based, limiting our ability to forecast impact and guide strategic decisions. For a quick orientation, Watch this to see how industry narratives often diverge from data-driven conclusions.

Solution

We designed a rigorous research framework that combines market sizing, scenario modeling, and practitioner interviews to map the state of agentic AI in private equity. Our approach integrates quantitative benchmarks with qualitative insights to produce a repeatable methodology for future updates.

We also developed a tailored reporting template that highlights risk, opportunity, and governance considerations for US‑focused investment teams. This included clear KPI definitions, data provenance, and an executive summary aimed at both operators and LPs.

Impact

X% improvement in decision speed across case studies
Y% uplift in accuracy of deal outcome forecasts
Z% reduction in forecast error variance for portfolio projections

Quote of how incredible of an impact this had.

Team

Team Member #1 - Research Lead
- Coordinated data collection across sources and validated methodologies
Team Member #2 - Data Analyst
- Built the forecasting models and produced key KPI dashboards

Agentic AI in US Private Equity 2026