AI in private credit is no longer a future-state conversation. It is happening now, at scale, and across functions that sit at the heart of how firms originate, monitor, and report on credit portfolios.
But the story behind the headline numbers tells us something more interesting than adoption alone.
In our latest research, we surveyed 105 senior leaders across the Americas, Asia Pacific, and the Middle East – the majority of whom are C-suite executives at firms managing between $5-20 billion in assets under management. What they told us paints a picture of an industry that has moved well past experimentation but is still working out how to turn technology adoption into genuine operating advantage.
The numbers that matter
The headline figure is striking: 85% of respondents report that AI is fully embedded in their private credit activities. Of those, 68% say it is embedded in a way that drives competitive advantage. Just 2% describe themselves as still in an exploratory phase.
But dig a little deeper and the picture becomes more nuanced.
Investment decision-making is where AI is delivering the clearest returns. 76% of respondents identify it as the area of greatest value, with firms applying AI across deal sourcing, underwriting, borrower forecasting, and pricing calibration. Risk management follows closely at 67%, with a noticeable shift from periodic reviews to continuous portfolio monitoring.
Where things get interesting is in the middle office. 63% of firms are currently implementing AI or automation across middle-office functions, with a further 27% already complete. The top benefit cited? Improved data accuracy (37%), followed by faster processing times (30%). Cost reduction, perhaps surprisingly, ranked lower, suggesting firms are investing in AI not to cut costs but to build the kind of institutional-grade operations their investors expect.
The gap between adoption and integration
Here's the tension the research reveals. AI is present across private credit operations, but its impact remains uneven. Value is concentrated in specific functions, while other areas, particularly valuation and real-time pricing, are developing more slowly.
Only 3.6% of respondents currently use AI-driven valuation models, yet firms consistently rank liquidity insight, reporting accuracy, and risk monitoring among the most valuable potential outcomes. The gap between aspiration and execution suggests that many firms are still building the data foundations and explainability standards needed to support these more advanced applications.
At the same time, nearly half of firms are managing data flows that remain only partially structured. Hybrid extraction methods, combining manual and machine inputs, are still common, which limits consistency and makes it harder to establish clear audit trails as portfolios grow.
Governance is catching up, but not fast enough
Over 60% of respondents have formal policies governing the ethical use of AI in credit decision-making. That is encouraging. But accountability remains concentrated: 49% of firms place AI oversight with the Chief Technology Officer or Chief Data Officer, while only 7% report genuine cross-functional ownership across technology, risk, compliance, and operations.
As AI reaches further into decision-making, investor reporting, and suitability assessment, that narrow accountability structure may not hold.
What comes next
The investment trajectory is clear. Over 60% of respondents expect technology spending to increase by 20-50% over the next three years, with close to half planning to allocate between 50-75% of that budget to AI. Priority areas include risk monitoring and analytics (27%), retail distribution platforms (20%), and valuation and pricing systems (17%).
Perhaps most telling: 94% of respondents believe AI is either very important or critically important to making private credit accessible to retail and non-institutional investors. Over 40% say retail access would not be economically viable without AI-driven automation.
Read the full report
This blog captures only a fraction of the findings. The full report examines where AI is delivering measurable value today, where structural constraints are slowing progress, and which operational capabilities will define the next phase of growth within private credit.
