When AI moves from adoption to performance​

Across Private Equity firms, the real inflection point in AI adoption does not occur at deployment, but at adoption at scale and measurement of impact.​

Market research from leading strategy and advisory firms consistently shows that while a large majority of Private Equity firms have initiated AI programs, less than one-third achieve sustained, measurable financial impact. The primary barrier is not technology maturity, but adoption and execution discipline.​

In practice, investment teams initially approach AI as a support tool, a way to accelerate access to information or reduce manual effort. However, without clear integration into core workflows, usage remains sporadic. AI is perceived as useful but optional, limiting its economic contribution.​

The shift occurs when firms explicitly anchor AI to core performance metrics: time-to-decision, transaction costs, quality and consistency of Investment Committee materials, and early risk detection. At that point, AI stops being a side initiative and becomes part of the operating model.​

“The value of AI became tangible when we moved from deployment to discipline. Once we started measuring adoption and impact in economic terms, usage increased and results followed. Within months, AI was no longer discussed as an innovation topic, but as part of how we run our investment process.” Partner, International Private Equity Firm​

Common adoption challenges observed​

Across funds, four recurring challenges explain why many AI initiatives underperform:​

• Fragmented tooling and workflows, forcing users to leave their core investment environment to access AI insights.​
• Lack of senior ownership, with initiatives driven by IT or innovation teams rather than investment leadership.​
• Insufficient change management, resulting in low and inconsistent usage across teams.​
• Absence of clear success metrics, making it difficult to demonstrate value and sustain momentum.​

Best practices among advanced adopters​

Firms that successfully translate AI into performance consistently apply a small number of best practices:​

• Prioritize a limited number of high-impact use cases, typically due diligence, IC preparation, investor relations and portfolio monitoring.​
• Embed AI directly into daily workflows, minimizing behavioral change and maximizing adoption.​
• Assign clear accountability at partner or operating-partner level, treating AI as an operating model topic.​
• Track adoption and impact with the same rigor as other value-creation levers, using simple KPIs on time, cost and risk.​

In this context, AI adoption becomes a matter of operational governance and execution discipline, not experimentation. The outcome is not incremental efficiency, but a structural improvement in decision quality, execution speed and operating leverage.​