AI is accelerating change across private equity and credit, forcing investors to reassess risk, resilience and operational alpha
Artificial intelligence is no longer just another theme to slot into an investor slide deck. It’s becoming a structural force – one that is steadily changing how value is created, assessed and protected across private markets.
The public AI debate tends to swing between two extremes: either it is about to wipe out entire industries, or it’s simply a helpful tool for boosting productivity. In reality, what is being seen across private equity and private credit portfolios is far more nuanced. AI’s impact varies dramatically depending on where a company sits in its value chain, how defendable its model is and how quickly its sector evolves.
For investors, the question is no longer if AI is relevant. The real questions are where it will matter most – and how fast.
Disruption is concentrated
The risk AI poses across portfolios is uneven. Companies whose value rests mainly on workflow coordination, basic interfaces or commoditised analytics – especially where the core output is built on widely available data – are most exposed. In those pockets, AI-native competitors can replicate functionality at near-zero marginal cost, threatening pricing power and eroding competitive moats.
But for businesses sitting at the heart of mission‑critical systems – those with proprietary data, regulatory complexity or deeply embedded workflows – the story is very different. For them, AI often strengthens their hand: improving efficiency, deepening customer lock‑in and boosting margins over time.
So the divide isn’t between ‘AI vulnerable’ and ‘AI safe’. It’s between shallow defensibility and structural defensibility. And for underwriting, that distinction matters a lot.
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Software isn’t homogeneous
A common mistake in market commentary is talking about software as one monolithic category. In practice, software breaks into distinct layers, each with different degrees of resilience.
Leading managers have begun formalising this. Several GPs have widely published articles on how they are approaching AI disruption in the software space. Some of these points include:
- Distinguishing between defensible layers (data, business logic, systems of record) and more exposed layers (UI and surface-level analytics).
- Conducting assessments to find the level of risk in their portfolio, and how much of it is insulated thanks to regulatory complexity, integration depth and proprietary data advantages.
- Reframing the issue entirely: AI isn’t replacing enterprise software; it’s pushing it into a new phase of agentic enterprise solutions, where probabilistic AI models sit inside deterministic, auditable workflows.
The common thread: software risk needs to be analysed by layer, not by category.
For investors, that means sector tilts alone are insufficient. What matters is the architecture underneath the product, not the label on top of it.
Transmission channels and growing dispersion
AI’s influence on private credit is more indirect, but potentially far‑reaching. For the private credit books that are heavily weighted toward technology and services, if AI accelerates obsolescence in parts of those industries, we could see greater earnings volatility and higher default risk.
In a severe AI‑driven disruption scenario, private credit defaults could rise meaningfully, with new issuance falling sharply. But periods of technological transition also create pricing dislocations. High‑quality credits may widen alongside structurally challenged names, especially in illiquid markets.
For disciplined lenders, that can create attractive entry points – so long as underwriting frameworks evaluate AI resilience rather than relying solely on backward‑looking performance. In short, AI brings both risk and opportunity to private credit.
Redefining operational alpha
For private equity, the near‑term impact of AI shows up more in operations than disruption.
AI is quickly becoming part of the standard portfolio value‑creation toolkit – supporting pricing optimisation, sales efficiency, working‑capital management, procurement analytics and more. Managers are already adapting by:
- Using AI to improve decision systems and rethink pricing models.
- Embedding AI assessments directly into underwriting and ongoing monitoring.
- Outlining a shift toward usage‑ and outcome‑based pricing models, enabled by agentic automation that can meaningfully increase annual contract value (ACV) and expand margins.
Operational alpha will increasingly hinge on AI fluency, not just traditional commercial levers. Access to models is not the differentiator – those are commoditising. The real edge will come from governance, integration execution and the ability to deliver quantifiable EBITDA impact.
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Implications for underwriting and portfolio construction
A few structural implications become clear:
1. Underwriting must evolve: Static earnings and backward‑looking KPIs aren’t enough. Investors must underwrite a company’s adaptability – its ability to integrate AI before competition compresses margins.
2. Valuation dispersion will widen: Shallow differentiation may trigger multiple compression as AI replicates features or reduces switching costs. Deeply embedded platforms may instead see support, or even expansion, as AI strengthens their moats.
3. Credit risk needs a second‑order lens: AI-driven disruption doesn’t only affect the borrower; it can ripple through supply chains, customer dependencies and end‑market stability.
4. Active ownership becomes more important: Simply owning ‘tech‑enabled’ assets won’t cut it. The question is whether those assets are AI‑enhanced or AI‑exposed.
The emerging investment discipline
AI will not transform all private markets overnight. Governance, capital intensity and contractual structures build inertia into the system. But AI is speeding up the divergence between structurally advantaged and structurally vulnerable companies. In that sense, AI acts as a sorting mechanism – one that will increasingly differentiate compounding winners from slow‑eroding laggards.
The strongest investors going forward will:
- Analyse risk at the architectural rather than sector level
- Underwrite adaptability alongside cash‑flow durability
- Treat proprietary data and workflow integration as core defensibility
- Deploy AI in portfolio operations with explicit ROI and margin targets
- Identify credit transmission risks before they show up in default data
Ultimately, the winners won’t simply be the firms that identify AI opportunities, but those that institutionalise AI literacy across their investment process. AI isn’t just another theme to monitor. It’s becoming a structural variable in private‑markets investing. And structural variables, eventually, shape returns.