Most private credit funds say AI is embedded: far fewer have changed how the work runs.
Important takeaways.
- Apex Group's AI-powered private credit research, published on 12 March 2026 from 105 senior private credit leaders across the Americas, Asia Pacific and the Middle East, most of them C-suite, found 85% saying AI is embedded within their private credit activities and 68% saying it is embedded in a way that drives competitive advantage.
- The same report separates deployment from operating discipline, finding a gap between perceived embedding and achieved transformation: many firms have deployed AI tools, and fewer have redesigned the underlying processes, data flows and governance structures needed to integrate them into day to day decision making.
- PwC's Global Private Credit Survey 2026, conducted between January and March 2026 with more than 120 credit portfolio managers in the US, the UK, Europe, the Middle East, Asia and Australia, found 53% implementing new technologies more frequently within the investment process and 54% most likely to use AI in underwriting. Only 16% view AI-enabled portfolio management, monitoring included, as a current priority.
- What is actually running is narrow and document-shaped. PwC lists 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, and states that AI is not being used for fully autonomous credit approval.
- The benefits credit funds report are process benefits rather than return benefits. In the Apex survey, improved data accuracy (37%) and reduced processing time (30%) were the most significant benefits achieved from middle office programmes, where 63% of respondents are currently implementing AI or automation and 27% report those programmes complete.
- Governance lags adoption in the same sample. Over 60% of Apex respondents report formal policies governing the ethical use of AI in credit decision making, leaving close to four in ten without one, at firms weighted towards institutional-scale platforms.
"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, because 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, drawing 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, and 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, and 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, published on 26 May 2026 and conducted between January and March 2026, captures more than 120 credit portfolio managers in the US, the UK, Europe, the Middle East, Asia and Australia, spread across AUM bands 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, and 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, and it is 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, and PwC is explicit that AI is not being used for fully autonomous credit approval.
Apex reports the same shape in the middle office: 63% of respondents are currently implementing AI or automation there and 27% report those programmes already complete, with data extraction, financial statement processing and credit agreement analysis 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, in which case AI is usually ready now, or whether it depends on complex counterfactuals and legal and economic nuance, in which case it should be treated as a high value assistant. 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, and 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, which 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, and 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.
- Can you say today, without asking around, which workflows run through a model, which data each one can reach, and who owns the output?
- If an output is challenged in six months, can you reconstruct what it was based on?
- Does a new analyst inherit the fund's way of doing this, or invent their own?
A fund answering yes to all three is ahead of both samples. A fund answering no is inside the 85%, which is a comfortable place to be right up until an investor asks the second question. That is the point at which 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, so read the figures as a snapshot of that quarter rather than as a measurement of what any individual fund has in production.
Questions this guide answers.
What are other private credit funds actually doing with AI?
Extracting, summarising, checking and routing documents, with a person still owning the judgement. PwC's Global Private Credit Survey 2026 describes the strongest current use cases as 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, and says AI is not being used for fully autonomous credit approval. Apex Group's March 2026 research reports the same shape in the middle office, where data extraction, financial statement processing and credit agreement analysis are the most common applications. Investment committee memo generation, term sheet drafting, structuring benchmarks and legal clause review are described by PwC as more nascent but fast advancing.
Are credit funds using AI more in underwriting or in portfolio monitoring?
Underwriting, by a wide margin of stated priority. In PwC's Global Private Credit Survey 2026, 54% of the more than 120 credit portfolio managers surveyed said they are most likely to use AI in the underwriting process, while only 16% view AI-enabled portfolio management, including monitoring, as a current priority. The gap is worth noticing because monitoring is the continuous, document-heavy half of a credit fund's work: compliance certificates, borrower reporting packs, covenant calculations and management accounts arrive on a schedule whether or not a deal is live. Intentions run the other way, with risk monitoring and analytics the largest single priority investment area over the next three years in Apex Group's survey, at 27%.
Is our fund behind if we have not deployed AI yet?
Behind on deployment, not necessarily behind on capability. Apex Group's survey of 105 senior private credit leaders found 85% saying AI is embedded in their private credit activities, so a fund with nothing deployed is outside the majority. But 'embedded' is self-reported with no agreed test behind it, and Apex's own finding is a gap between perceived embedding and achieved transformation. A fund where every analyst holds a chat licence and a fund that has rebuilt covenant checking around a model both answer yes to the survey question. The more useful comparison is whether a fund can say which workflows run through a model, what each one can reach, and how an output would be reconstructed if challenged.
What separates the credit funds where AI actually stuck?
Choosing tasks the evidence supports, and building the controls in rather than over the top. PwC's test is whether a task is document grounded and verifiable, in which case AI is usually ready now, or whether it depends on complex counterfactuals and legal and economic nuance, in which case it should be treated as a high value assistant rather than a decision maker. On the control side, Apex Group's Chief AI and Data Science Officer, Helen Wang, states that governance cannot be treated as an overlay once AI becomes part of core operating workflows and has to be designed in from the outset so that controls scale alongside capability. The same report shows that work is unfinished: over 60% of respondents have formal policies governing the ethical use of AI in credit decision making, which leaves close to four in ten without one.
Working with Levercon.
Levercon helps private credit funds and non-bank lenders accelerate AI adoption to unlock their full potential. It does that through one of three routes: Levercon Agents, Levercon Systems or Levercon Strategy.
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This guide is general information, not advice. Factual claims that rely on public sources link to those sources in the text. Practical guidance also draws on patterns Levercon observes across Australian credit funds. No client is named and no figure is attributed to one. Written by Levercon, reviewed before publication and revised in place as the facts change.