What AI Is Starting to Change in Private Capital

Artificial intelligence is no longer a peripheral technology topic for private capital firms. Drawing on a recent series of conversations with private equity investors, operating partners, limited partners, and family office leaders in Asia, one theme continued to permeate discussions: AI adoption remains uneven, but the focus is on how quickly and effectively firms can turn it into operating advantage. Firms are testing AI across investment workflows, portfolio oversight, value creation, and talent strategy. The more important question now is where AI can create durable advantage and what type of leaders can best capture it. 

Here are four areas private capital leaders should be tracking: 

For many private capital firms, AI has entered a more practical phase. It is increasingly being tested in day-to-day investment and portfolio workflows, from research and information synthesis to diligence, talent assessment, portfolio monitoring, and internal knowledge management. What matters is not only the breadth of possible use cases, but the need for a more intentional point of view on where AI can genuinely create leverage. 

This broader momentum is consistent with Egon Zehnder’s recent AI in PE work: Our latest observations across external sources of data indicate that 84% of PE firms have appointed a Chief AI Officer, 86% of organizations have embedded GenAI into M&A workflows, and 65% of PE respondents rank AI as a top value-creation priority. Whether these figures are viewed as evidence of maturity or urgency, they reinforce that AI has become part of the operating agenda, not just the innovation agenda. 

The most consequential AI opportunity for private capital may not be back-office productivity, but portfolio value creation. AI can improve visibility across portfolio companies, strengthen management decision-making, and support more systematic performance oversight. Some firms are also looking to specialist advisers and AI-native providers to identify operational opportunities that might not surface through traditional diagnostics. While many of these approaches are still in early phases, the direction of travel is clear: AI is becoming part of the value creation conversation, not just a back-office tool. 

Egon Zehnder’s market observations also point to a portfolio-first deployment model. Firms are increasingly using fund-level AI leadership, such as AI operating partners, AI transformation leads, or senior AI advisors, to identify value-creation opportunities, build repeatable playbooks, and help portfolio companies translate experimentation into measurable EBITDA, growth, and productivity impact. The firms that benefit most will likely be those that connect AI to the business model and value creation thesis, rather than treating it as a collection of disconnected tools. 

As private capital firms bring in AI-experienced leaders to accelerate portfolio value creation, that talent is also beginning to influence how firms evaluate new opportunities. AI leaders can help deal teams test where AI may expand revenue, improve margins, reshape customer experience, or disrupt an existing business model. In that sense, AI capability is becoming part of the investment thesis itself, not only a lens to apply after the deal closes, but for assessing value creation potential during diligence. 

This has practical implications for the diligence process. Funds are increasingly likely to need AI fluency when reviewing new investments: people who can identify whether a target has the data, infrastructure, leadership, and operating model to capture AI-enabled value. The same applies to investing in AI companies themselves, where the ability to distinguish durable advantage from hype will become a more important skill for PE investors to build over time. 

These PE-specific shifts also point to a broader leadership reality. For all the enthusiasm around AI, leaders are clear on its limits. Judgment, accountability, context-setting, and the ability to connect fragmented information into sound decisions remain fundamentally human capabilities. This is especially true when AI is applied to complex business processes: Improving one step in a workflow can create value, but it can also miss the interdependencies that make the whole system work. Leaders will need to understand the business deeply enough to know where AI can unlock leverage, where it may create fragmentation, and how to ensure new tools reinforce rather than undermine organizational coherence. 

The implications for the talent landscape are becoming harder to ignore. There is growing concern about pressure on entry-level roles, the compression of average performance, and the challenge of preserving meaningful development pathways for junior talent. AI literacy is often deeper among younger professionals than among more senior leaders, creating an unusual reversal in the traditional experience curve. This raises important questions for investors and management teams alike: How should leadership evolve, how should organizations build capability, and what will differentiate talent when baseline performance becomes easier to replicate? 

The talent market is also moving quickly. Leading PE firms are recruiting people who have already built and scaled AI, data, or digital functions in large organizations, often from Big Tech, technology-enabled businesses, consulting, or mature digital operators. In Asia, many large PE firms do not yet have a dedicated regional “Head of AI.” Instead, AI strategy is often driven from global headquarters and applied to Asia portfolios through operating partners, external advisors, consulting companies or value-creation teams. That makes local execution capability especially important: Firms will need leaders who can adapt global infrastructure, governance, and playbooks to the realities of Asia portfolios. 

AI opportunity in private capital is real, but it will not be captured by technology adoption alone, but rather by how effectively human judgment, organizational capability, and disciplined execution evolve alongside it. Firms are still early in their adoption journeys, but the questions are becoming sharper—where AI can create real advantage, how it should be embedded into investing and value creation processes, and what kinds of leaders and talent models will be required in an AI-shaped environment.