AI is Already Creating Winners in Private Equity
It’s Time to Choose Sides
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2026年5月11日
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This article was published in Expansión in April 2026: https://www.expansion.com/opinion/2026/04/27/69ef3fdb468aeb24448b4589.html
Every transformative technology reaches a point of no return, when it ceases to be an advantage and becomes the minimum threshold to compete. In private equity, that moment has arrived. Because of AI, for the first time in recent memory the cost of capital is no longer enough to distinguish between funds. What matters now is the ability to harness data and models in order to make better and faster decisions.
A Growing Gap
Our recent report, the 2026 Private Equity AI Radar — which captures the views of 200 senior decision-makers from private equity funds across three continents — depicts an industry split in two. On one side, a minority of funds have systematically deployed AI into production and are already achieving materially superior returns. On the other side is the majority — funds that continue to accumulate pilots, test isolated use cases and manage portfolio companies with traditional tools. The gap between the two is growing, but not linearly.
One key takeaway from our report challenges a common excuse: that the gap between AI leaders and stragglers is primarily a budget issue. In truth, investment levels are broadly comparable between leading and lagging funds. The real differentiator is execution discipline — clear governance, well-defined priorities and the integration of AI across the entire investment life cycle rather than in isolated moments.
Among the surveyed funds, only 36% of portfolio companies report deploying AI in production — with real operational impact, not just experimentation — and only 7% say they have achieved enterprise-scale deployment. At the same time, 43% report they have not implemented AI in any meaningful way. With entry multiples still demanding and traditional value creation levers increasingly exhausted, this should concern any investment committee.
Most strikingly, among those portfolio companies that have moved into production, 95% report meeting or exceeding their business case expectations. These results speak for themselves: AI works when executed well. But, executing well is harder than it appears, and too many funds have yet to clearly identify why their initiatives stall at the pilot stage.
A Growth Bias
Another notable finding is the bias toward growth. The fact that 41% of initiatives focus on revenue generation, compared with 24% aimed solely at cost reduction, is no coincidence: An additional percentage point of growth typically creates more value in absolute terms than the same point of cost savings. Funds that understand this arithmetic prioritize commercial AI — pricing and margin optimization, product innovation, salesforce productivity — over purely defensive automation.
Even so, ambitions remain conservative. Most AI programs target improvements of 5% to 10%, below the 15% that industry evidence suggests is achievable with rigorous implementation. This points to an uncomfortable reality: Part of the potential value is lost before initiatives even begin due to insufficient upfront work to identify and prioritize the highest-return use cases.
The Human Barrier
If one message stands out for management teams, it is this: The main barrier to scaling AI is not technological — it is human. Among respondents, 35% identify talent shortages as the primary constraint and 68% rank hiring as their top priority. Together, these figures reflect a structural tension: Private equity firms are competing for the same talent in a market where supply falls far short of demand.
However, availability of talent is only the most visible obstacle. Beneath it lie crucial tests related to leadership and operating models — questions around speed of deployment, integration with legacy systems, change management, unclear ownership of initiatives and lack of executive alignment. Scaling AI is as much an organizational challenge as a technological one. Funds that treat it purely as an infrastructure problem are destined to remain stuck in “pilot purgatory.”
The Investment Cycle
A deeper shift is also underway as AI becomes embedded in the investment cycle itself. While 67% of respondents consider AI talent and technical teams to be important or critical factors when evaluating an acquisition, 57% assign the same importance to data assets and AI infrastructure. In other words, a portfolio company’s AI maturity is becoming a valuation criterion, not just a post-investment value creation lever.
Looking ahead to exit, the top priority among surveyed firms is modernizing data infrastructure (42%), followed by AI-enabled compliance and risk management (36%) and performance prediction (35%). The message is clear: Buyers will assess AI maturity. Portfolio companies that cannot demonstrate it with tangible evidence — production use cases, metrics, governance — risk suffering valuation discounts that seemed purely hypothetical just a year ago.
Building a Structural Advantage
For decades, private equity has sought advantage through operational efficiency, financial leverage and management capability, yet these levers have been commoditized over time and no longer provide a consistent source of alpha. AI can take over that role as a genuine differentiator, helping new winners emerge on the field. But as leading funds consolidate capabilities and raise industry standards, the window of opportunity is narrowing.
As the findings in the 2026 Private Equity AI Radar suggest, a group of funds is already building a structural advantage — not by investing more, but by investing better: superior selection of use cases, stronger governance, deeper talent integration and, above all, greater discipline in measuring impact and scaling what works. For the funds that have yet to invest in AI, the question is no longer whether to do so but whether they still have time to avoid being left behind.
Permission to use this reprint has been granted by the publisher
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2026年5月11日
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Senior Managing Director, Spain Corporate Finance