For much of the period following the GFC, cheap and readily available debt and rising valuation multiples provided a supportive backdrop for private equity returns. Today’s higher financing costs and demanding entry valuations have made the return equation less forgiving.
With less scope to rely on leverage and multiple expansion, more of the return must come from what happens to a business during the ownership period. This has renewed the industry’s focus on operational alpha, and artificial intelligence (AI) is emerging as one tool through which to pursue it.
From AI strategy to EBITDA
AI can already be applied across functions ranging from sales and customer service to software development and finance. The relevant question for investors is whether those applications can ultimately produce higher revenue, lower costs, better cash conversion or a more defensible business.
There is a risk that ‘AI-enabled’ becomes the latest label applied indiscriminately across the industry. Having an AI strategy or running pilots is not, by itself, value creation. The investment case depends on whether technology can be translated into measurable improvements in unit economics and, ultimately, exit value.
This is where private equity may have a particular advantage. Unlike a public-market investor, a control-oriented GP can influence management priorities, allocate investment capital, introduce operating expertise and implement initiatives across multiple portfolio companies. AI therefore has the potential to become not simply an investment theme, but an ownership capability.
Private Markets and Portfolio Construction – the decisive trends summit | 5 November 2026 | London
Mid-market opportunity
The opportunity is particularly interesting in the lower and mid-market, where many attractive businesses still operate with fragmented systems, manual processes and limited digital infrastructure. Poor underlying systems or data can significantly limit what AI can achieve, but it can reduce the cost and complexity of automating certain processes.
The practical applications are already fairly prosaic, which may be precisely why they matter. Sales teams can use AI to prioritise leads and analyse customer behaviour, while finance teams can automate elements of reporting and forecasting.
Individually, many of these interventions are unlikely to transform an investment. Collectively, however, relatively modest productivity improvements applied across several functions and sustained over a four or five-year ownership period can have a meaningful effect on EBITDA. For private equity, that compounding effect is more important than the technology itself.
AI as an underwriting question
AI is also beginning to change what investors need to understand before acquiring a business. Traditional diligence remains fundamental, but AI adds another layer of questions. How proprietary is the company’s data? Which workflows are genuinely embedded in customers’ operations? Where are labour-intensive processes susceptible to automation? Could AI strengthen the company’s proposition, or allow a competitor to reproduce it more cheaply?
A company with proprietary data, deeply embedded workflows and mission-critical functionality may use AI to strengthen its competitive position. A business whose differentiation rests primarily on basic analytics or publicly available information may face a different outlook.
The diligence question, therefore, should not simply be ‘How can this company use AI?’ It should also be ‘What does AI do to the durability of this company’s competitive advantage?’
The investment case depends on whether technology can be translated into measurable improvements in unit economics and, ultimately, exit value.
A different approach to buy-and-build
AI could also alter the economics of buy-and-build. Productivity gains may allow more EBITDA growth to be generated organically, while AI could reduce some of the friction involved in integrating acquired businesses. The operating model becomes a source of differentiation.
Individual AI tools are unlikely to provide a durable edge as access becomes increasingly commoditised. The more defensible advantage may therefore sit at the GP level. A sponsor that develops repeatable expertise in identifying use cases, implementing technology and measuring results can apply those lessons across multiple investments. A successful initiative in one portfolio company can become a playbook for another.
This creates the possibility of an institutional learning effect. The competitive advantage is shortening the time between identifying an opportunity and converting it into financial performance. That places greater importance on operating teams and the relationship between investment professionals, management teams and technology specialists. The strongest model is likely to make AI part of the existing value creation toolkit.
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What this means for LPs
For LPs, this introduces another dimension to manager assessment. It is easy for a GP to present an AI strategy; it is considerably harder to demonstrate that the strategy has generated returns. LP diligence should therefore focus on implementation and attribution: where has AI been deployed, what investment was required, and what evidence shows that it has contributed to revenue growth, margin expansion or cash generation?
There is an important distinction here between capability and branding. As adoption becomes widespread, simply using AI will cease to differentiate managers. Dispersion is more likely to arise from the ability to identify the right applications, implement them quickly and measure their economic contribution.
Execution matters more than technology
Many mid-market businesses lack the data architecture required to deploy AI effectively, while implementation costs, governance risks and organisational resistance can constrain adoption. Productivity gains demonstrated in pilots may also fail to translate into sustainable EBITDA.
There is also a more fundamental investment risk. Technology that improves productivity does not automatically create alpha. The benefit must either be captured by the portfolio company or strengthen its competitive position sufficiently to generate additional economic value.
If AI-generated efficiencies are readily available to every competitor in an industry, some of the benefit may ultimately accrue to customers through lower prices rather than to shareholders through higher margins.
The return equation is changing
AI will not replace good underwriting, sensible capital structures or strong management teams, and not every initiative will generate an attractive return. In some businesses it will prove transformational; in others, its impact may amount to incremental efficiency. The opportunity is to distinguish between applications that are interesting and those that are economically valuable – and then repeat the latter systematically across a portfolio.
The firms that do this well may be able to create something more durable than an AI strategy: a repeatable operating capability that translates technological change into revenue growth, margin improvement and cash generation. In an environment in which financial engineering alone is less likely to deliver the returns investors became accustomed to, that capability could become an increasingly important source of alpha.
Written in a personal capacity. The views expressed are solely those of the independent author and not representative of any organisation’s views.