Private equity has spent the last year asking portfolio companies a familiar question in a new language: What is our AI strategy?

The pressure is understandable. Boards want to know whether management teams are moving. CEOs want to demonstrate that they are not standing still. Investors want to understand how AI could affect growth, cost structure, customer experience, and ultimately enterprise value. The conversation is happening everywhere, which is precisely why the more important question is no longer whether portfolio companies are exploring AI.

The question is whether that activity is creating value.

McKinsey’s 2025 global AI survey found that 78% of organizations now use AI in at least one business function, while 71% report regular use of generative AI. Yet more than 80% reported no tangible enterprise-level EBIT impact from those investments. More importantly, McKinsey found that workflow redesign had the greatest influence on whether organizations achieved meaningful financial results from AI.

If AI adoption is becoming widespread but value creation is not, the differentiator is unlikely to be access to technology. It is far more likely to be an organization's ability to integrate technology into the way the business operates.

That distinction is particularly important in private equity, where value creation plans are built around specific operating outcomes. The portfolio companies creating measurable value from AI are not starting with the technology. They are starting with the investment thesis.

They begin by identifying the outcomes that matter most to the investment thesis. In some companies, that may mean accelerating growth. In others, it may mean improving margins, increasing productivity, or creating operating leverage in parts of the business that have become difficult to scale. AI is not treated as a strategy. It is treated as an enabler of strategy.

That sounds obvious. Yet the gap between adoption and impact suggests otherwise.

Many portfolio companies can point to pilots, proofs of concept, and early AI use cases. Far fewer can point to measurable changes in growth, margins, productivity, or valuation. That gap is what matters. Private equity does not create value by adopting technology. It creates value by changing business performance.

Bain's 2025 Global Private Equity Report points to the same divide. While most portfolio companies remain somewhere in the testing and development phase of generative AI adoption, only a minority have operationalized use cases and begun realizing measurable results. Bain concluded that the firms making the greatest progress are helping portfolio companies apply AI to strategic priorities rather than treating AI as a standalone initiative.

The reason has less to do with AI itself than many organizations would like to admit. In many cases, AI is revealing weaknesses that already existed within the business. A customer productivity initiative uncovers fragmented systems. A conversation about AI-enabled decision-making exposes poor data quality. A board discussion about AI strategy becomes a discussion about ERP limitations. Management teams discover that ownership is unclear, priorities compete for attention, and execution responsibilities are spread across multiple functions.

AI did not create those problems. It exposed them.

This is one reason AI is producing such uneven outcomes across portfolio companies. Organizations with scalable processes, accountable ownership, usable data, and strong operating discipline can integrate AI into workflows and decision-making relatively quickly. Organizations lacking those foundations often discover that AI highlights constraints that were already limiting performance long before AI entered the conversation.

Private equity has seen this dynamic repeatedly across carve-outs, ERP modernizations, cybersecurity transformations, and growth-stage scaling initiatives. The same constraints slowing AI adoption today are the ones that have historically slowed transformation efforts across portfolio companies. Technology rarely creates value on its own. Value is created when organizations can translate strategy into execution.

Through Fortium, ZRG's technology leadership and transformation business, we see this dynamic across portfolio companies. Technology initiatives succeed or fail based far less on the technology itself than on an organization's ability to execute.

A private equity-backed carve-out required the separation of technologies, applications, contracts, and business processes across a global operation. Fortium provided the technology leadership needed to coordinate the effort, completing seventeen Day One deliverables and thirty-seven additional separation deliverables without business disruption. The challenge was not identifying what needed to happen. The challenge was orchestrating execution, managing dependencies, and maintaining momentum while protecting business performance.

Similarly, a global manufacturer preparing for significant expansion faced an outdated ERP environment, scalability constraints, cybersecurity concerns, and no clear technology roadmap. Fortium helped establish the architecture, governance, and technology strategy required to support growth. Again, the challenge was not vision. It was execution.

The divide emerging across portfolios is not primarily a technology divide. It is an execution divide.

In many cases, AI is not creating new challenges for portfolio companies. It is exposing existing ones. Weak data foundations, fragmented systems, unclear accountability, operating complexity, and leadership gaps have always constrained performance. AI simply makes those constraints more visible.

The portfolio companies creating value from AI are not necessarily the ones talking about it most aggressively. They are the companies that understand where AI can improve business performance, how it supports the value creation plan, and what leadership, operating discipline, and accountability are required to make adoption stick.

As a result, operating teams should ask a different set of questions. Instead of asking whether a portfolio company has an AI strategy, they should ask where AI can materially improve the economics of the business, what operational barriers could limit impact, and whether the organization has the leadership capacity to translate experimentation into measurable results.

The firms that answer those questions well will create value from AI. The firms that do not may still generate activity. The difference is not the technology. It is whether AI exposes constraints they are prepared to address or constraints they continue to ignore.

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