Technology Blog

After two decades practicing corporate law and leading M&A transactions at major firms, I've witnessed firsthand how technology reshapes deal-making. But nothing prepared me for the velocity of change that artificial intelligence has brought to our practice. When we first piloted AI-driven contract analytics on a mid-market acquisition three years ago, I was skeptical. The technology promised to accelerate due diligence review while reducing billable hours—a proposition that seemed too good to be true. What followed was a journey filled with unexpected obstacles, remarkable breakthroughs, and lessons that fundamentally changed how our practice approaches every deal. The transformation began when our client, a private equity firm, demanded a 40% reduction in due diligence timelines without compromising quality. Traditional approaches couldn't meet this requirement. We turned to AI in M&A tools, specifically machine learning platforms designed for contract lifecycle mana...

In the high-stakes world of mergers and acquisitions advisory, deals move at velocity that often defies traditional manual processes. Walk into any mid-market transaction at Goldman Sachs or Morgan Stanley, and you'll witness deal teams juggling multiple workstreams simultaneously—financial modeling, legal due diligence, regulatory compliance checks, synergy modeling, and integration planning—all under compressed timelines that can make or break billion-dollar transactions. What many outside observers don't see is the intricate machinery operating beneath the surface, where intelligent automation has fundamentally transformed how M&A practitioners execute complex deals from target identification through post-merger integration. The reality of Intelligent Automation in M&A extends far beyond simple task automation or robotic process automation. It represents a complete reimagining of how advisory teams approach deal execution, fundamentally altering workflows that have ...

Corporate law practices face mounting pressure to deliver faster turnarounds, reduce costs, and maintain accuracy across increasingly complex regulatory environments—all while client expectations for transparency and value continue to rise. These challenges cannot be addressed through incremental process improvements alone. Legal departments at firms like Skadden and Clifford Chance are implementing artificial intelligence solutions that fundamentally restructure how work gets done, but successful adoption requires matching the right AI approach to each specific operational pain point. The landscape of AI in Legal Operations offers multiple solution pathways, each suited to different problem profiles. Understanding which AI capabilities address which operational challenges—and when to combine multiple approaches—determines whether implementations deliver transformational value or become expensive distractions. This framework examines the core problems legal operations teams face and m...

Private equity firms managing billions in LP commitments face a fundamental challenge: extracting maximum returns from increasingly competitive markets while managing risk across diverse portfolios. The operational reality behind successful funds involves processing thousands of deal opportunities, monitoring dozens of portfolio companies simultaneously, and executing exits at optimal moments—all while maintaining the rigorous analysis standards that LPs expect. Traditional approaches, relying on analyst teams and spreadsheet-driven workflows, struggle to keep pace with the volume and velocity of modern investment cycles. The integration of AI in Private Equity has fundamentally altered how firms execute their core functions, from initial deal sourcing through final exit execution. Rather than replacing human judgment, AI augments decision-making at each stage of the investment lifecycle, processing information at scales impossible for traditional teams while surfacing insights that d...