Elli Peterson's TechCrunch

Implementing artificial intelligence across the private equity investment lifecycle represents one of the most significant operational transformations firms can undertake. Yet many implementations fail or underdeliver—not because the technology lacks potential, but because firms approach deployment without systematic planning and execution frameworks. After working with multiple PE firms on AI integration initiatives and observing both successful transformations and expensive false starts, clear patterns have emerged regarding what separates effective implementations from superficial adoptions that generate minimal value. This comprehensive checklist provides a structured approach to AI in Private Equity implementation, organized by investment lifecycle phase. Each item includes not just what to do, but why it matters and what success looks like. Whether your firm is beginning its AI journey or seeking to enhance existing initiatives, this framework provides a roadmap for systematic c...

Three years ago, our fund made a decision that fundamentally altered how we approach deal sourcing, due diligence, and portfolio management. We committed to integrating artificial intelligence across our investment operations—not as a novelty, but as a core capability. What followed was a journey filled with unexpected wins, humbling setbacks, and invaluable lessons that reshaped our understanding of how AI Integration in Private Equity actually works in practice, beyond the conference presentations and vendor promises. The promise of AI Integration in Private Equity was compelling from the start: faster deal screening, deeper analytical insights, and the ability to monitor portfolio companies with unprecedented granularity. Yet the reality of implementation taught us that technology alone solves nothing. The real transformation came from understanding where AI genuinely adds value versus where it creates expensive distractions. Our experience offers a candid look at what actually wor...