Artificial intelligence is opening new avenues for private equity value creation. The real opportunity, however, lies not in adopting the technology for its own sake, but in translating it into measurable operating improvements, new sources of growth and lasting competitive advantage.
Artificial intelligence has moved rapidly from the technology agenda to the investment agenda. For private equity firms facing longer holding periods, challenging exit conditions and mounting pressure to demonstrate operating performance, AI offers a potentially powerful new lever for value creation. Yet a critical distinction is emerging: adopting AI is not the same as capturing its economic value.
For general partners (GPs), the question is no longer simply whether portfolio companies should experiment with AI. It is where the technology can materially improve business performance, how quickly those gains can be realized, and whether they can be sustained and replicated across a portfolio.
Beyond efficiency: Rethinking the value-creation opportunity
The most immediate applications of AI are familiar: automating administrative tasks, accelerating financial reporting, improving forecasting and streamlining customer service. These initiatives can deliver tangible productivity gains. But efficiency alone may not be enough to differentiate a business or fundamentally change its growth trajectory.
Research published by McKinsey & Company in 2026 points to a broader opportunity. Its analysis of private equity-backed companies suggests that more extensive integration of AI across operations, products and new business development is associated with stronger revenue multiples.
As McKinsey’s authors argue, “The greatest opportunities to create value are likely to come not from isolated productivity gains but from embedding AI into products, operating models, and entirely new businesses.”
The distinction is consequential. Automating an existing process may improve margins; developing an AI-enabled service, improving customer retention or creating a new revenue stream can change the economics and growth potential of the business itself. For investors, the ambition should extend beyond doing the same things more efficiently to identifying what the company could do differently—and profitably—with AI.
The implementation gap
Despite the promise, meaningful financial outcomes remain far from universal. PwC’s research on AI in private equity-backed companies highlights a persistent gap between adoption and measurable business impact. Technology investment does not automatically translate into higher revenue, lower costs or stronger returns.
The underlying challenge is less about access to technology than the ability to implement it effectively. AI initiatives can falter when data is fragmented, legacy systems are difficult to integrate, management ownership is unclear or projects are disconnected from strategic priorities.
As Roland Berger observes in its analysis of AI in private equity value creation, “The challenge isn’t the technology – it’s the lack of practical implementation.”
For GPs, this has direct implications for execution. AI initiatives should be tied to clearly defined value-creation objectives, whether improving pricing, reducing procurement costs, accelerating sales conversion or increasing asset utilization. Each initiative needs a credible baseline, an accountable owner and measurable targets. Without these disciplines, experimentation can consume capital and management attention without producing material results.
The boardroom challenge
Realizing AI’s potential also requires investment in capabilities that may be less visible than the technology itself: reliable data, modern digital infrastructure, appropriate governance and employees equipped to work with new tools. Equally important is the ability of leadership teams to make informed decisions about where AI can create economic value and how that value should be measured.
Research from Boston Consulting Group (BCG) highlights the importance of leadership in the digital-to-AI transition. The implication is clear: AI is not simply an IT decision. It is a business transformation challenge that requires engagement from boards, investment teams and portfolio-company management.
This distinction should increasingly shape both due diligence and post-acquisition planning. Investors need to assess not only the potential applications, but also whether a business has the data, systems, talent and management capacity to implement them. Some companies may benefit from targeted automation; others may require more fundamental changes to their operating models or product strategies.
The investment case must also remain grounded in economics. AI projects should be evaluated against implementation costs, adoption risks, expected benefits and the time required to realize returns. Not every promising application will justify the investment, and not every company will benefit from the same approach.
From individual initiatives to repeatable value creation
For private equity firms, the next challenge is scaling what works. A successful application in one portfolio company may offer lessons for others, but replication requires more than deploying the same software. Business models, data quality, customer requirements and organizational capabilities differ.
This creates an opportunity for GPs to develop a more systematic AI value-creation capability: identifying high-potential use cases, sharing expertise across portfolio companies, tracking measurable outcomes and incorporating results into investment reviews and exit preparation.
The strongest approach is likely to combine fund-level coordination with company-specific execution. Central teams can provide frameworks, specialist expertise and lessons learned across the portfolio, while management teams remain accountable for implementation and business outcomes. Over time, this can turn isolated successes into a repeatable source of competitive advantage.
It can also strengthen the investment narrative at exit. Buyers are more likely to place confidence in AI-driven improvements when they are supported by demonstrable financial results, embedded operating capabilities and a credible path to continued growth—not simply a list of technology initiatives.
The real test is value realized
AI will not become a meaningful source of private equity returns simply because adoption accelerates. Its contribution will depend on the quality of the investment thesis, the relevance of the applications selected and the discipline with which they are implemented.
The firms best positioned to benefit will be those that treat AI as a business transformation opportunity rather than a technology initiative. That means pursuing productivity where it matters, investing in growth where the economics justify it, and measuring results against the original investment case.
Ultimately, the question is not how much AI a portfolio company uses. It is whether AI makes the business more competitive, more productive and more valuable. For private equity, that is the difference between technological adoption and genuine value creation.
