Key Takeaways
- • Nicholas Domnisch, CEO and Managing Partner at EE Solutions, argues in a Forbes Technology Council piece that AI's value for private capital extends beyond productivity gains to "possibility expansion," where lower execution costs change which initiatives are economically worth pursuing.
- • The American Investment Council reports more than 13,000 U.S. companies are backed by private equity, giving sponsors a broad base to test AI capabilities and transfer lessons across portfolios.
- • McKinsey finds nearly 90% of organizations use AI in at least one business function, but only 7% have fully scaled it, and companies seeing the most value are more likely to pursue growth or innovation alongside efficiency.
- • Domnisch recommends revisiting initiatives previously rejected as too expensive or labor-intensive, and re-running both upside and downside cases in investment theses, since falling costs also lower barriers for competitors.
- • The author contends the firms that build the fastest learning loop across their portfolios will be best positioned to capture AI-driven value.
Summary
In an opinion piece published by Forbes, Nicholas Domnisch, CEO and Managing Partner at EE Solutions, a technology operating partner for private capital firms, argues that the most significant AI opportunity for private capital lies beyond productivity. He notes that much of the current AI discussion centers on faster diligence, automated reporting, lower support costs and more productive teams, which he describes as valuable but potentially only a first-order effect.
The scale of private equity provides the context for his argument. Citing KKR partner Alisa Wood, he references roughly 19,000 private equity funds in the United States compared with about 14,000 McDonald's restaurants. The American Investment Council reports more than 13,000 U.S. companies currently backed by private equity, which Domnisch argues gives the industry an unusual ability to test new operating capabilities across a broad base of businesses.
Domnisch draws on several external views. BlackRock's global chief investment strategist Wei Li, speaking in an S&P Global interview, described AI progress in three phases: build-out, adoption and broader productivity gain, with the economy still early in that progression. McKinsey research cited in the piece found nearly 90% of organizations use AI in at least one business function, while only 7% report having fully scaled it; 80% of respondents said efficiency was an objective of their AI initiatives. Andreessen Horowitz general partner Anish Acharya's essays "Notes on AI Apps in 2026" and "Disposable Software" are cited for the argument that cheaper software development can make previously uneconomic products viable.
The core thesis is that falling execution costs can revisit past decisions to reject customer segments, products, personalization or adjacent markets as too expensive. Domnisch recommends that portfolio companies reassess such initiatives, noting that most will remain poor ideas but some will not. He also warns that the same shift lowers barriers for competitors, so investment cases dependent on scarce specialist capacity or costly delivery should be re-examined. Finally, he argues that sponsors can capture advantage by capturing and reusing operating lessons across portfolio companies, turning the portfolio into a learning system. The piece is an opinion contribution from a technology executive and does not report new transaction, fund or performance data.
The scale of private equity provides the context for his argument. Citing KKR partner Alisa Wood, he references roughly 19,000 private equity funds in the United States compared with about 14,000 McDonald's restaurants. The American Investment Council reports more than 13,000 U.S. companies currently backed by private equity, which Domnisch argues gives the industry an unusual ability to test new operating capabilities across a broad base of businesses.
Domnisch draws on several external views. BlackRock's global chief investment strategist Wei Li, speaking in an S&P Global interview, described AI progress in three phases: build-out, adoption and broader productivity gain, with the economy still early in that progression. McKinsey research cited in the piece found nearly 90% of organizations use AI in at least one business function, while only 7% report having fully scaled it; 80% of respondents said efficiency was an objective of their AI initiatives. Andreessen Horowitz general partner Anish Acharya's essays "Notes on AI Apps in 2026" and "Disposable Software" are cited for the argument that cheaper software development can make previously uneconomic products viable.
The core thesis is that falling execution costs can revisit past decisions to reject customer segments, products, personalization or adjacent markets as too expensive. Domnisch recommends that portfolio companies reassess such initiatives, noting that most will remain poor ideas but some will not. He also warns that the same shift lowers barriers for competitors, so investment cases dependent on scarce specialist capacity or costly delivery should be re-examined. Finally, he argues that sponsors can capture advantage by capturing and reusing operating lessons across portfolio companies, turning the portfolio into a learning system. The piece is an opinion contribution from a technology executive and does not report new transaction, fund or performance data.
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