Most private credit funds say AI is embedded: far fewer have changed how the work runs.

"What are other funds doing?" is a benchmarking question, and it is usually asked as a worry about being behind. It has a better answer in 2026 than it had a year ago. Two surveys fielded in the first quarter asked private credit managers directly, rather than asking the asset management industry as a whole and assuming credit looked the same.

This question is not jurisdiction-bound. Both surveys run across the markets a credit fund actually operates in, so the pattern below reads the same in Melbourne, London, New York and Singapore. What changes by market is the regulatory work that sits on top of it, not the shape of the adoption underneath.

The headline adoption numbers are high, and they are self-reported.

Apex Group published AI-powered private credit on 12 March 2026. It draws on 105 senior private credit leaders across the Americas, Asia Pacific and the Middle East, most of them C-suite, at firms with established private credit operations. Of those, 85% said AI is embedded within their private credit activities. A further 68% said it is embedded in a way that drives competitive advantage.

Taken alone, that ends the "are we behind" conversation. Taken with the rest of the same report, it does something more useful. Apex's own finding is a gap between perceived embedding and achieved transformation. Many firms have deployed AI tools. Fewer have redesigned the underlying processes, data flows and governance structures required to integrate those tools into day to day decision making.

That is the sentence to hold on to, because "embedded" is a self-assessment with no agreed test behind it. A fund where every analyst holds a chat licence and a fund that has rebuilt covenant checking around a model both answer yes.

The sector is putting AI on the deal rather than on the portfolio.

PwC's Global Private Credit Survey 2026 was published on 26 May 2026. It surveyed more than 120 credit portfolio managers between January and March 2026, across the US, the UK, Europe, the Middle East, Asia and Australia. The AUM spread runs from under $1 billion to more than $50 billion.

Just over half of respondents (53%) are implementing new technologies more frequently within the investment process. About the same number (54%) are most likely to use AI in the underwriting process. By contrast, only 16% view AI-enabled portfolio management, monitoring included, as a current priority.

That split is the most useful number in either survey. Underwriting is episodic, visible and competitive, so it attracts the budget and the attention. Monitoring is continuous, document-shaped and unglamorous. It is also where a credit fund's recurring reading actually sits: quarterly compliance certificates, borrower reporting packs, covenant calculations, management accounts. The work that repeats is not the work getting the tooling.

What is genuinely common is narrower than "AI".

PwC's account of current use is assistive and control oriented rather than autonomous. The strongest use cases it lists are data room summaries, document extraction, covenant analysis, transaction level cash flow analysis, fraud detection, anti-money laundering and know-your-customer screening, workflow automation and portfolio monitoring. Investment committee memo generation, term sheet drafting, structuring benchmarks and legal clause review sit in the more nascent bucket. PwC is explicit that AI is not being used for fully autonomous credit approval.

Apex reports the same shape in the middle office. There, 63% of respondents are currently implementing AI or automation and 27% report those programmes already complete. Data extraction, financial statement processing and credit agreement analysis are the most common applications. The benefits achieved are process benefits rather than return benefits: improved data accuracy (37%) and reduced processing time (30%).

So the honest benchmark answer is that other credit funds are extracting, summarising, checking and routing documents under human judgement, and counting that as AI adoption. It matches what actually works in a credit fund today. If that is what your fund is doing, your fund is the market.

The funds that got past tools changed something other than the tool.

Both reports arrive at the same distinction from different directions. PwC's test is whether the task is document grounded and verifiable. If it is, AI is usually ready now. If it depends on complex counterfactuals and legal and economic nuance, it should be treated as a high value assistant instead. Apex's Chief AI and Data Science Officer, Helen Wang, makes the governance point. Governance cannot be treated as an overlay once AI becomes part of core operating workflows. It has to be designed in from the outset so that controls scale alongside capability.

The evidence that this is unfinished work sits in the same release. Over 60% of Apex respondents report formal policies in place to govern the ethical use of AI in credit decision making. That leaves close to four in ten without one, in a sample weighted towards institutional-scale platforms.

The spending suggests the sector knows where it is thin. Over 60% of Apex respondents expect technology investment in operations to increase by 20% to 50% over the next three years. Nearly half expect to direct between 50% and 75% of technology budgets towards AI. The largest single priority investment area is risk monitoring and analytics, at 27%. That is the same monitoring work only 16% of PwC's respondents name as a current priority. The intention is ahead of the practice by about three years of budget.

If your fund believes it is different.

On credit judgement it probably is. On this, the distribution is remarkably flat. It holds across AUM bands, across strategies and across regions, which is what makes it a usable benchmark rather than an anecdote. Three questions separate a fund that has deployed tools from one that has changed how the work runs.

A fund answering yes to all three is ahead of both samples. A fund answering no is inside the 85%. That is a comfortable place to be right up until an investor asks the second question. At that point the question stops being about tools and starts being about the layer the tools sit in.

Primary sources: the PwC Global Private Credit Survey 2026 and Apex Group's AI-powered private credit research, announced on 12 March 2026. This guide is general information rather than advice. Both surveys are self-reported and were fielded in the first quarter of 2026. Read the figures as a snapshot of that quarter rather than as a measurement of what any individual fund has in production.